pure.southwales.ac.uk · Web viewTheory of Acceptance and Sustained Use of Technology: A technology acceptance model adapted in the context of digital mapping for disaster preparedness

PhD thesis, Dave W Farthing, University of South Wales

Theory of Acceptance and Sustained Use of Technology:

A technology acceptance model adapted in the context of digital mapping for disaster

preparedness in East Africa

David W Farthing, BSc, CEng, MBCS, PGCED, CITP

Faculty of Computing, Engineering and Science

University of South Wales

A submission presented in partial fulfilment of the requirements of the University of South Wales/Prifysgol De Cymru for the degree of

Doctor of Philosophy

September 2015

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Table of contentsAbstract................................................................................................................................

Acknowledgements..............................................................................................................

Glossary...............................................................................................................................

1. Introduction..........................................................................................................13

1.1. Overview................................................................................................................13

1.2. Project aim..............................................................................................................15

1.3. Project objectives....................................................................................................17

1.4. Key deliverables.....................................................................................................17

1.5. Project approach and scope....................................................................................19

1.6. East Africa study areas...........................................................................................19

1.7. Thesis structure.......................................................................................................25

2. The Problem Space..............................................................................................27

2.1. Context...................................................................................................................27

2.2. Disaster management..............................................................................................27

2.3. Why maps of developing countries need to be improved......................................51

2.4. Crowdsourcing: A possible solution to mapping developing countries.................58

2.5. Mapping for disaster management.........................................................................66

2.6. Crowdsourced mapping for disaster management.................................................74

2.7. Mapping for disaster preparedness.........................................................................85

2.8. Crowdsourced mapping for disaster preparedness.................................................87

2.9. Chapter summary....................................................................................................92

3. Technologies for Humanitarian Mapping in Developing Countries...............93

3.1. Context...................................................................................................................93

3.2. Storing and retrieving GI........................................................................................95

3.3. Recording GI........................................................................................................105

3.4. Supporting ICTs...................................................................................................113

3.5. Summary of technologies for humanitarian mapping..........................................116

3.6. Chapter summary..................................................................................................122

4. Behavioural Models...........................................................................................123

4.1. Context.................................................................................................................123

4.2. Disaster preparedness models...............................................................................123

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4.3. Innovation models................................................................................................126

4.4. Decision-making and technology acceptance models..........................................130

4.5. Motivation models................................................................................................160

4.6. Sustained use models............................................................................................168

4.7. Selection of models..............................................................................................176

4.8. Chapter summary..................................................................................................177

5. Research Methodology.......................................................................................179

5.1. Philosophical context............................................................................................179

5.2. Research methods.................................................................................................179

5.3. Research tools.......................................................................................................184

5.4. Application of research methodology theory.......................................................187

5.5. Ethical considerations...........................................................................................193

5.6. Chapter summary..................................................................................................194

6. Interviews, survey and empirical fieldwork....................................................197

6.1. Interview themes related to best practice in mapping developing countries for

disaster preparedness............................................................................................199

6.2. Interview themes related to an improved technology acceptance model.............210

6.3. Survey...................................................................................................................221

6.4. Empirical fieldwork case studies..........................................................................223

6.5. Chapter summary..................................................................................................229

7. Proposed TASUT model....................................................................................230

7.1. Analysis of key points..........................................................................................232

7.2. Changes to existing constructs.............................................................................238

7.3. New/revised constructs.........................................................................................242

7.4. Determinants.........................................................................................................245

7.5. Theory of Acceptance and Sustained Use of Technology (TASUT)...................246

7.6. Possible further enhancements.............................................................................248

7.7. Chapter summary..................................................................................................252

8. Applying TASUT to mapping developing countries.......................................253

8.1. Context.................................................................................................................253

8.2. Behavioral intention and initial Use behavior......................................................254

8.3. Sustained use behavior.........................................................................................256

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8.4. Performance expectancy.......................................................................................258

8.5. Costs.....................................................................................................................264

8.6. Social influence....................................................................................................265

8.7. Perceived and actual facilitating conditions.........................................................266

8.8. Motivation............................................................................................................269

8.9. Habit.....................................................................................................................271

8.10. Chapter summary..................................................................................................272

9. Evaluation...........................................................................................................274

9.1. Method..................................................................................................................274

9.2. Responses about TASUT model and supporting text...........................................275

9.3. Responses about the guidelines for applying TASUT.........................................278

9.4. Resultant changes to TASUT and guidelines.......................................................280

9.5. Validation of model by example..........................................................................285

9.6. Chapter summary..................................................................................................290

10. Conclusion...........................................................................................................291

10.1. Reflections on project objectives..........................................................................291

10.2. Key achievements.................................................................................................292

10.3. Critique of research method adopted....................................................................294

10.4. Possible future directions and limitations of the research....................................297

10.5. Summary...............................................................................................................300

11. References...........................................................................................................301

Appendix A.....................................................................................................................325

Appendix B.....................................................................................................................331

Appendix C.....................................................................................................................347

List of figuresFigure 1.1: Map of Mbale region...................................................................................20

Figure 1.2: Map of Mbale region, comprising the districts of Mbale, Manafwa and

Bududa. © OpenStreetMap contributors, used under Creative Commons licence.......20

Figure 2.1: Traditional and evolving views of economic development and disaster

response..........................................................................................................................30

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Figure 2.2: A typical disaster management cycle...........................................................31

Figure 2.3: Baseline for a nation to make collaboration effective for capacity building

(Wagner et al., 2001)......................................................................................................36

Figure 2.4: An example of how a hazard can cascade other hazards (Gill and Malamud,

2014, used with permission)...........................................................................................42

Figure 2.5: GI in the disaster management cycle (adapted from Cordaid, 2007)...........68

Figure 2.6: Example of buildings entered twice into the system (should appear as pale

pink) © OpenStreetMap contributors............................................................................83

Figure 2.7: A road recorded once as a road (grey vertical line) and twice as a track

(brown dots) © OpenStreetMap contributors.................................................................83

Figure 2.8: Multiple errors showing tracks misrecorded, duplicate tracks etc. ©

OpenStreetMap contributors..........................................................................................84

Figure 3.1: Possible multi-layer architecture................................................................119

Figure 3.2: Example data flows....................................................................................120

Figure 4.1 Adopter categories (Rogers, 1963).............................................................127

Figure 4.2: Diffusion of Innovations Theory (Rogers, 2003)......................................128

Figure 4.3: Theory of Reasoned Action (Fishbein and Ajzen, 1975)..........................131

Figure 4.4: Theory of Planned Behavior (Ajzen, 1985)...............................................132

Figure 4.5: Technology Acceptance Model (Davis et al., 1989).................................135

Figure 4.6: Combined TAM and TPB (Taylor and Todd, 1995).................................138

Figure 4.7: Extended Technology Acceptance Model (Moon and Kim, 2001)...........140

Figure 4.8: Technology Acceptance Model 2 (based on Venkatesh and Davis, 2000)142

Figure 4.9: Technology Acceptance Model 3 (based on Venkatesh and Bala, 2008)..143

Figure 4.10: Unified Theory of Acceptance and Use of Technology (Venkatesh et al.,

2003).............................................................................................................................148

Figure 4.11: Research model used by Bandyopadhyay and Fraccastoro (2007) and by

Bandyopadhyay and Bandyopadhyay (2010)...............................................................150

Figure 4.12: Unified Theory of Acceptance and Use of Technology 2 (Venkatesh et al.,

2012).............................................................................................................................153

Figure 4.13: Theory of Reasoned Action 2 (Fishbein and Ajzen (2009).....................157

Figure 4.14: Possibility of determinants of initial and sustained use behavior............160

Figure 4.15: Orientation: Motivation as key to moving from Behavioral intention to

(initial) Use behavior....................................................................................................161

Figure 4.16: Hierarchy of Needs (Maslow, 1943)........................................................1615


Figure 4.17: Motivation Model (based on concepts in Davis et al., 1992)..................163

Figure 4.18: A model for workers' motivation in crowdsourcing (Kaufmann et al.,

2011).............................................................................................................................166

Figure 4.19: Orientation: Moving from (initial) Use behavior to Sustained use

behaviour......................................................................................................................168

Figure 4.20 Proposed Dynamic TAM (based on Davies and Venkatesh, 2004)..........170

Figure 4.21 Significance results of Dynamic TAM (based on Davies and Venkatesh,

2004).............................................................................................................................171

Figure 4.22 Expectation-confirmation Theory (Oliver, 1977).....................................172

Figure 4.23 Baseline model (without habit).................................................................173

Figure 4.24 Competing model (habit as a direct effect)...............................................174

Figure 4.25 Research model (habit as a moderator).....................................................174

Figure 5.1: Overview of the development of thesis.....................................................196

Figure 6.1: Map of Manafwa River and Manafwa River Basin (c) OpenStreetMap

contributors (key features highlighted)........................................................................227

Figure 7.1 Alternative ways to develop a model..........................................................231

Figure 7.2: Unified Theory of Acceptance and Use of Technology 2 (Venkatesh et al.,

2012).............................................................................................................................238

Figure 7.3: Possible relationships between Performance expectancy, Habit and

Sustained use behavior.................................................................................................244

Figure 7.4: Constructs that need to be linked with Behavioral intention, Use behavior

and Sustained use behavior..........................................................................................245

Figure 7.5: Working version of Theory of Acceptance and Sustained Use of

Technology...................................................................................................................247

Figure 7.6: Possible need to include Self-mobilised behavior in some situations........249

Figure 7.7: Possible inclusion of aspects of motivation...............................................249

Figure 7.8: Possible distinction between stated intention and actual intention............251

Figure 8.1: Possible division of storage.......................................................................263

Figure 9.1: Extract showing suggested additional effects of Social influence in bold.276

Figure 9.2: Extract showing Motivation affected by Satisfaction, and Social influence

combined with Motivation............................................................................................277

Figure 9.3: Theory of Acceptance and Sustained Use of Technology: final version...282

Figure 10.1: Possible use of cyclic determinants to represent reinforcement..............299

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AbstractThe purpose of this project is to assist crowdsourced mapping of developing countries

(particularly in East Africa) for the purposes of disaster preparedness. In the event of a

disaster, relief organisations require an up-to-date map of the affected area. In

developing countries, maps of vulnerable areas are often low resolution and out of date.

Crowdsourcing techniques have been used to map such areas after a disaster, but the

geographic information becomes available perhaps days or weeks later. By populating

a digital map in advance of a disaster, various advantages may be gained, such as the

information being available at the outset of disaster response and it having already been

checked. Modern technologies enable local communities to create maps of their own

areas, but a key problem with pre-disaster crowdsourced mapping is how to obtain

sufficient participation from volunteers within the community. This project has

identified factors that encourage communities in developing countries to adopt and use

mapping technologies through a qualitative analysis of crowdsourced mapping case

studies and the views of experts. A model of technology acceptance and use has been

tailored for this purpose. The resulting model is called the Theory of Acceptance and

Sustained Use of Technology. As the name implies, there is an emphasis on the factors

that encourage use of technologies both in the short and long term. It is hoped that the

model and its associated guidelines will help communities to be more prepared for

disasters and so reduce loss and suffering in developing countries.

AcknowledgementsMy thanks and appreciations go to Dr. Mark Ware, Director of Studies, and the rest of

the supervision team, Professor Gary Higgs, Dr. Tony Harris and Dr. Dave Jenkins.

Special thanks to Tim Kirby for proof-reading a draft of this thesis. My deep gratitude

also to all those who spared time for the interviews, correspondence and evaluations

described in chapters 6 and 9. Furthermore the University of South Wales’s Learning

Resources Centre, especially its FindIt system, has been invaluable for finding and

retrieving appropriate literature.

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Travelling expenses for the fieldwork described in Chapter 6 were partially or wholly

funded by:

University of Glamorgan, Research Investment Scheme (July, 2009);

University of Glamorgan, Department of Computing and Mathematical

Sciences (January, 2011); and

Welsh Government, Territorial Approach to Climate Change Project (April

2012).

GlossaryTerm Full Explanationcapacity building

Strengthening the skills, competencies and abilities of people and communities, typically used in the context of developing countries.

COD Common Operational Dataset

A COD is a critical dataset that is used to support the work of humanitarian actors across multiple sectors. CODs are considered to be a de facto standard for the humanitarian community and represent the best-available datasets for each theme. Compare with FOD. There are seven COD themes (IASC, 2010):

administrative boundaries; populated places; transportation network; hydrology (rivers, water

bodies); hypsography (elevation,

contours); population statistics; humanitarian profile

(casualties etc.).contributor Someone who contributes

information to a crowdsource initiative.

crisis mapping

When volunteers update and add detail to maps after a disaster using public mapping systems such as OpenStreetMap and Google Map Maker.

crowdsourcing

The practice of obtaining needed services, ideas or content by soliciting contributions from a large group of people, especially from the 8


Term Full Explanationonline community rather than from traditional employees or suppliers (Meriam-Webster, 2015).

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C-TAM-TPB Combined TAM and TPB

A combination of the Technology Acceptance Model (q.v.) and Theory of Planned Behavior (q.v.) as defined by Taylor and Todd (1995).

developing country

Countries in the lowest two of the World Bank’s four categories, Low Income and Lower Middle Income, which means Per Capita Gross National Income is USD 4,085 or less (World Bank, 2013).

DOI Diffusion of Innovations

A theory about the adoption of new technologies (Rogers, 1963; 2003).

ellipsoid A sphere-like shape where the three radii (X, Y and Z) differ.

EPPM Extended Parallel Process Model

A model of individuals’ reactions to fear appeal messages (Witte, 1992).

FOD Fundamental Operational Dataset

A FOD is a dataset that is relevant to a humanitarian operation, but is more specific to a particular sector or otherwise does not fit into one of the seven COD themes (q.v.). (IASC, 2010)

geodetic Coordinate system based on an ellipsoidal model (rather than a spherical model).

georeference

To tag information with geographic location, especially to ensure aerial imagery aligns with reality on the ground.

geotag To add GI to various media such as photographs, videos, or websites. The GI usually comprises latitude and longitude coordinates, plus perhaps elevation, bearing and accuracy.

GI geographic information

GIS geographic information system

Software for recording, manipulating and analysing GI.

GNSS Global Navigation Satellite System

The generic term for a system that allows autonomous geo-spatial positioning using signals from orbiting satellites. See also GPS.

GPS Global Positioning System

In common usage, GPS is a synonym for GNSS. Strictly speaking GPS is derived from NAVSTAR GPS, which is a specific GNSS operated by the US government.

HOT Humanitarian OSM Team

See hot.openstreetmap.org

Hyogo Framework for Action

The key instrument for implementing disaster risk management, adopted by 168 member states of the United Nations. See section 2.5 “Mapping for

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disaster management”.hypsography The branch of geography concerned

with the determination and mapping of the relative elevation of areas of land. (Oxford Dictionary).

ICRC International Committee of the Red Cross

See www.icrc.org

ICT information communication technology

A term encompassing a wide variety of computing and communication technologies, including PCs, networks etc.

IDP internally-displaced person

Citizens that are temporarily displaced as a result of a disaster or other problem. The concept is similar to that of a refugee except that an IDP hasn’t crossed an international border.

IFRC International Federation of Red Cross and Red Crescent Societies

See www.ifrc.org

informatics The science of processing data for storage and retrieval; information science. (Oxford Dictionary).

IOM International Organization for Migration

NGO working to manage migration, especially post disaster, and to encourage social and economic development through migration. Originally set up by European governments after World War 2.

IPR intellectual property rights

JOSM Java OpenStreetMap

An open-source editor for editing GI on OpenStreetMap.

LiDAR Light Detection and Ranging

A remote sensing technology that measures distance using a laser.

Map KiberaMap MathareMap Mukuru

Initiatives to map slum areas in Nairobi, Kenya.

NGO non-governmental organisation

Any organisation without government input, but usually used to mean a charity, private voluntary organisation, or self-help organisation.

NSDI national spatial data infrastructure

1. An SDI (q.v.) for one nation.2. The aggregate of agencies, technologies, people, and data that together constituted a nation’s mapping enterprise. (NRC, 1993)3. The technology, policies, standards, and human resources necessary to acquire, process, store,

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distribute, and improve utilisation of geospatial data. (Clinton, 1994)

OGC Open Geospatial Consortium, Inc.

See www.opengeospatial.org

OSM OpenStreetMap An open-access digital map that is stored, accessed and updated online.

PEOU Perceived ease of use.

A factor in the Technology Acceptance Model and its derivatives.

PGIS participatory geographic information systems

The merging of community development with geo-spatial technologies for the empowerment of less privileged communities (Rambaldi et al., 2006).

POI point of interest Any location of interest to the user.PU Perceived

usefulnessA factor in the Technology Acceptance Model and its derivatives.

quango quasi-autonomous non-governmental organisation

An organisation to which a government has devolved power.

SDI spatial data infrastructure

SDI has five features: the existence of core datasets; the accessibility of documentation about existing geo-information; the adherence of geo-information to accepted standards; policies and practices promoting the exchange and reuse of geo-information; and sufficient human and technical resources to collect, manipulate and distribute geo-information (EIS-Africa, 2002).

TAM Technology Acceptance Model

The original model of why users do and do not use technologies, as defined by Davis et al. (1989)

TAM2 Technology Acceptance Model 2

A development of TAM (q.v.) as defined by Venkatesh and Davis (2000).

TAM3 Technology Acceptance Model 3

A development of TAM2 (q.v.) as defined by Venkatesh and Bala (2008).

TAMs The super-set of TAM, TAM2 and TAM3.

TASUT Theory of Acceptance and Sustained Use of Technology

A development of UTAUT2 (q.v.) that was produced during this project.

technology acceptance models

When in lower case, this refers to all the various TAMs and UTAUTs (q.v.).

TPB Theory of Planner Behavior

A psychological/behavioural model for decision-making as defined by Ajzen (1985).

TRA Theory of Reasoned Action

A psychological/behavioural model for decision-making as defined by

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Fishbein and Ajzen (1975).TRA2 Theory of Reasoned

Action 2A development of TRA (q.v.) as defined by Fishbein and Ajzen (2009).

UAV unmanned aerial vehicle

Also known as a drone. An aircraft without a human pilot aboard which is controlled either by onboard computers or remotely by a pilot on the ground.

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UNEP United Nations Environment Programme

See www.unep.org

UN OCHA United Nations Office for Coordination of Humanitarian Affairs

See www.unocha.org

UTAUT Unified Theory of Acceptance and Use of Technology

A development of TAMs (q.v.) and other models, as defined by Venkatesh et al., (2003).

UTAUT2 Unified Theory of Acceptance and Use of Technology 2

A development of UTAUT (q.v.), as defined by Venkatesh et al., (2012).

UTAUTs The superset of UTAUT and UTAUT2.VOIP Voice Over Internet

ProtocolInteractive voice communications using Internet technologies.

WASH water, sanitation and hygiene

Includes water supplies, sewage systems and latrines, and can also include hygiene education/practices.

WFP World Food Programme

An initiative by the United Nations Food and Agriculture Organization.

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1. IntroductionPeople need information as much as water, food, medicine or shelter.

Information can save lives, livelihoods and resources. It may be the only form

of disaster preparedness that the most vulnerable can afford. And yet it is very

much neglected. (IFRC, 2005: p12).

1.1. OverviewNo community can be completely safe from disaster*, but those in developing

countries† are particularly vulnerable. A sudden-onset disaster can be particularly

damaging if the communities haven’t made adequate preparations. The World Bank

suggests that more than 95% of all deaths caused by disasters occur in developing

countries. It goes on to say that losses due to natural disasters (as a percentage of GDP)

are 20 times greater in developing countries than in industrialized countries. (World

Bank, 2008; Perdikaris, 2014).

A disaster is an emergency where the resources of relevant authorities are inadequate to

respond effectively (UNISDR, 2009). That means that disaster response organisations

come into an affected country from elsewhere; normally the personnel are unfamiliar

with the locality so need geographic information (GI) about it. During the initial hours

of a response initiative, disaster response organisations have to make many decisions

under immense pressure. Mistakes could have significant – even life-or-death –

consequences for those communities affected. The information systems of response

organisations can assist disaster management in many ways, such as planning and

disseminating plans, communicating among the various response organisations,

estimating casualties from census data, prioritising responses and so on. Relevant

information may be in many forms, such as databases, documents, maps, search-and-

* Unless specified otherwise, the word ‘disaster’ will normally be used to mean ‘sudden-onset disaster’,

as distinct from ‘slow-onset disaster’. See section 2.2.1 for further discussion.† There isn’t a universally agreed definition for ‘developing countries’; it is often a convenient shorthand.

One authoritative definition is provided by the World Bank: developing countries are those in the lowest

two of the Bank’s four categories, Low Income and Lower Middle Income, which means Per Capita

Gross National Income is USD 4,085 or less (World Bank, 2013).15


rescue reports, and reports from the public. Appropriate analysis of such information is

the foundation for effective decision-making. Sir John Holmes, UN Emergency Relief

Coordinator, said (Holmes, 2007 quoted in Limbu, 2012: p1):

Information is very directly about saving lives. If we take the wrong decisions,

make the wrong choices about where we put our money and our effort because

our knowledge is poor, we are condemning some of the most deserving to death

or destitution.

This thesis investigates appropriate ways to gather and record GI to support disaster

management. GI can include a digital map, population densities, river flow volumetrics,

meteorological records, critical infrastructure and building inventories, impact and

casualty data, names, addresses and phone numbers. The World Bank has

acknowledged the importance of GI for disaster management (GIC/ESRI Canada,

2011). Providing GI demands prior preparation and diverse datasets to be on-hand,

including ‘before’ and ‘after’ imagery.

There are a small number of disaster response organisations specifically devoted to

collating GI from various sources and disseminating it among the other disaster

response organisations. Staff working in organisations such as MapAction,

Humanitarian OpenStreetMap Team, The Standby Task Force and the Global Disaster

Alert and Coordination System (GDACS) have expertise in locating relevant GI

quickly. In an ideal world, all potential disaster areas would be mapped in detail.

However, even though almost all developing countries have a national mapping

agency, typically there is little or no GI of remote disaster-prone areas. Even where

such GI exists, it may be fragmented and inconsistent (Musinguzi et al., 2011; Eria,

2012: p161). Traditional data collection and storage techniques have proven to be

impractical for mapping‡ large areas of developing countries due to the high costs and

insufficient funding (IFRC, 2007: p15; Makanga and Smit, 2010; World Bank, 2012).

A candidate solution is crowdsourcing, whereby a large number of volunteers map their

local areas and store GI in a way that can be shared (Kawasaki et al., 2013). In recent

‡ Throughout this thesis the word ‘mapping’ is used to encompass surveying, cartography,

photogrammetry, and GIS as well as basic mapping. In that sense it should be considered to be a

synonym for the more technically correct word ‘geomatics’.16


years there have been several examples of crowdsourced mapping after a disaster (e.g.

Zook et al., 2010). Unfortunately post-disaster crowdsourced maps are not available for

many hours or days, and disaster response teams may not be able to access updated

maps until days or weeks later due to communication bottlenecks. It would be better for

GI to be ready and available before a disaster (ESRI, 2006: p26; Farthing and Ware,

2010).

A key problem with pre-disaster crowdsourced mapping is how to obtain sufficient

participation from volunteers. In order to identify good ways to encourage community

participation, this project has adapted one of the existing technology acceptance models

– the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2 – Venkatesh

et al., 2012) – for pre-disaster crowdsourced mapping initiatives. The adapted model –

the Theory of Acceptance and Sustained Use of Technology (TASUT) – is explained in

Chapter 7. Various technology acceptance models have been developed over the years,

and they have been used with both every-day and specialised technologies in various

settings. In this project is the first adaptation of UTAUT2 for crowdsourced mapping,

for use in East Africa and for use in relation to disaster preparedness.

The case studies and most of the interviews used in this project relate to Uganda and

Kenya. Karatunga’s review (2005) of GIS support for disaster management identified

the need for Uganda to develop its GIS and remote sensing skills, and the need for

stakeholder participation. These needs form the basis of the work in this project. Since

the University of South Wales and its county borough are twinned with the District of

Mbale in Uganda, the project is well placed to study local needs for GI.

1.2. Project aimIn this project a technology acceptance model has been developed in the context of

mapping for disaster preparedness in East Africa. In order to assist such mapping, the

factors that enable and encourage crowdsource contributors to adopt and use mapping

technologies have been modelled, and associated guidance has been produced for

organisations that may plan a mapping initiative. In particular, the UTAUT2 model has

been adapted, based on crowdsourced mapping case studies and the views of experts.

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Walliman (2011) and others suggest these four questions should be considered when

defining a research project.

What are you going to do? Analyse technology acceptance models for their applicability in developing countries,

specifically for mapping East Africa (Chapter 6). Tailor the UTAUT2 model based on

experience from crowdsourced mapping initiatives in East Africa to produce a tailored

model known as TASUT (Chapter 7). Produce guidelines on how to apply TASUT to

future mapping initiatives (Chapter 8).

Why are you doing it? To help mitigate the severity of disasters in East Africa, and to help improve the

efficiency and effectiveness of disaster response organisations. GI can be used to

identify disaster hazards and vulnerabilities, to improve safeguards, and to assist in

disaster response and recovery. The TASUT model and associated guidelines are

designed to make crowdsourced mapping initiative more successful in the short and

long term.

How are you going to do it? Analyse and evaluate a variety of mapping initiatives in East Africa; identify the factors

that contribute to success/failure in the short and long term; map those factors onto

UTAUT2; identify factors that do not fit into that model; propose an improved model

(TASUT). Use TASUT as a framework to produce guidance on how to encourage

communities to contribute to a mapping initiative in the short and long term. The

research method adopted is explained and justified in Chapter 5.

When are you going to do it? The project ran between 2008 and 2015, with a brief suspension for some months

during 2011. Empirical work in Uganda was conducted during visits in 2009, 2011 and

2012, and is described in section 6.4.

1.3. Project objectivesThe project’s objectives are:

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to establish disaster management organisations’ requirements for digital

geographic information both for disaster management and other every-day uses

(see Chapter 2 for the literature review, and Chapter 6 for interviews);

to review techniques and technologies for producing digital maps in developing

countries (see Chapter 3);

to identify contributors’ motivations and barriers to crowdsourced mapping in

the short and long term (see Chapter 4 for the literature review, and Chapter 6

for interviews and empirical field work);

to propose an improved model of technology acceptance that can be used to

strengthen the sustained use of crowdsourced mapping in developing countries

(see Chapter 7);

to produce guidelines on how to apply the model to crowdsourced mapping

initiatives in developing countries (see Chapter 8);

to evaluate the model and guidelines, and make further improvements (see

Chapter 9).

1.4. Key deliverables1.4.1. Literature review of mapping for disaster

management, mapping technologies and behavioural models

In chapters 2, 3, and 4 a large body of literature is brought together that define the

problems space (mapping for disaster management), technological solutions, and

various approaches to dealing with human behavioural issues.

1.4.2. Results of interviews, surveys and empirical work

The author visited Uganda on three occasions in relation this project. These

deployments provided experience of mapping a developing country, experience of

capacity building, and an opportunity to interview relevant people who work in

Uganda. Twenty six experts were interviewed about their experience and opinions of

mapping developing countries for disaster preparedness. Additionally, a small survey

19


was conducted among delegates to a training course. These are documented and

evaluated in Chapter 6.

1.4.3. Theory of Acceptance and Sustained Use of Technology (TASUT) model

Chapter 7 explains TASUT, a technology acceptance model that is tailored for use in

developing countries on mapping initiatives relating to disaster preparedness. Existing

technology acceptance models that are used in other situations are described in Chapter

4. The initial development of the prototype model is described in Chapter 7, and further

refinements resulting from feedback from external evaluators are described in Chapter

9. The final model appears in Error: Reference source not found on page Error:

Reference source not found. The key features that distinguish it from pre-existing

models are:

there is a strong emphasis on encouraging crowdsource contributors to sustain

their use of the technologies;

in order to ensure contributors use the technologies in both the short term and

long term, the importance of motivation is emphasised;

in order to ensure contributors use the technologies in the long term, the

importance of habit is emphasised.

1.4.4. Guidelines for applying the TASUT model to mapping for disaster preparedness

The practical implications of the TASUT model for a crowdsourced mapping initiative

are set out in the guidelines in Chapter 8. Drawing on relevant literature, interview

material and experiences from empirical fieldwork, it emphasises techniques to

encourage local communities to participate in a mapping initiative both in the short and

long term, and highlights potential obstacles to participation. Some further refinements

resulting from feedback from external evaluators are explained in Chapter 9

1.5. Project approach and scopeChapter 5 explains the rationale for the research methodology approaches adopted in

this project. In brief, the constructivist paradigm has been adopted, using abduction,

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action research, grounded theory and qualitative data collection/analysis. TASUT has

been developed from the empirical fieldwork, not created in advance and then validated

afterwards. This approach is in contradistinction to the technology acceptance projects

considered in Chapter 4 that were deductive, that is, each model was proposed based on

literature rather than empirical fieldwork, and then validated using empirical surveys

and studies.

Although it is intended that the outputs of this project (TASUT and guidelines on how

to apply it) will be of worldwide use, they have been based on case studies of non-

expert mappers in East Africa. Therefore the scope of the project is confined to non-

expert mappers in that region. Some cultural issues discussed here may be relevant to

other regions, but some may differ. Even though some references will be made to

psychological models, the author is not a psychologist. The author accepts

responsibility for any misunderstanding of the concepts.

1.6. East Africa study areasThe empirical work documented in Chapter 6 was conducted in the Mbale region of

Uganda, and around Nairobi, Kenya.

1.6.1. Ugandan case studies: Mbale regionFive of the case studies are based in the Mbale region, which is towards the east of

Uganda. Strictly speaking, the name ‘Mbale region’ is an informal title to describe the

neighbouring districts of Mbale, Manafwa and Bududa, as shown in Figure 1.1 and

Figure 1.2. The University of South Wales has links with the Mbale region, so it was

possible to examine several case studies for disaster preparedness.

21


Figure 1.1: Map of Mbale region.

Figure 1.2: Map of Mbale region, comprising the districts of Mbale, Manafwa and Bududa. © OpenStreetMap contributors, used under Creative Commons licence.

22


Table 1.1 shows that Uganda has roughly the same surface area as the UK, and,

although it has a far smaller population, the Ugandan birth rate is very high. Also the

GDP per capita is very small compared with the UK.

Uganda UKArea 241,038 km2 243,610 km2 Population 35,918,915 63,742,977Population growth rate

3.24% (9th highest) 0.54% (152nd highest)

Birth rate 48 births/1,000 population

12 births/1,000 population

Death rate 11 deaths/1,000 population

9 deaths/1,000 population

Life expectancy 54 years 80 yearsGDP per capita USD 1,500 USD 37,300Roads 20,000 km 394,428 km - paved 3,264 km 394,428 km - unpaved 16,736 km Public Byways not

includedTable 1.1: Uganda and UK comparison (CIA, 2014)

According to Karatunga (2005: pp9-10), the most common threat agents across the

Republic of Uganda are:

Banditry: Rebel activities of organisations such as Allied Democratic Forces

and People’s Redemption Army in the West, and the Lord’s Resistance Army in

the North.

Refugees: Immigrant populations who settle near the borders with their mother

countries.

Neighbouring countries: Conflicts over politics or resource usage are common

with Democratic Republic of Congo, Rwanda, Sudan and Tanzania.

Tribal conflict: Historic differences between tribes such as the Bakonzo and

Batooro.

Drought: Especially in the so-called ‘cattle corridor’.

Earthquake: The Western Rift Valley experiences earthquakes from time to

time.

Flooding: Large relatively flat expanses of land around lakes are prone to

flooding.

23


Landslides: Unstable land in the Mount Elgon, Mount Rwenzori, Kisoro and

Kegezi hills can result in landslides that kill hundreds.

Volcanic activity: Active and dormant volcanic areas around Mount Elgon,

Western Rift Valley and the Muhavura volcanoes in Kisoro.

The Mbale region is subject to a number of hazards, the most prominent of which is

landslides. Recent examples include:

1 March 2010: After a prolonged period of rainfall in the whole area, a landslide

hit the Nametsi, Namakansa and Kubewo villages killing an estimated 365

villagers and displacing 2000 (Atuyambe et al., 2011; Bariyo, 2010; GLIDE,

2010; Mafabi, 2010).

21 May 2011: A landslide affected villages in the Bupoto, Bumbo and Mukoto

sub-counties of Manafwa District. There was only one fatality but some 478

people were displaced; water supplies in wells, springs and streams were

contaminated by mud spoil (Ntabadde, 2011).

11 August 2011: Further landslides occurred in Simuyu, Bududa, and the

Uganda Red Cross said some 6,400 were in need of urgent resettlement. Three

lives were lost and five were injured (GLIDE, 2011).

28 August 2011: Further floods and landslides in the nearby Bulambuli District

killed more than 30 and affected some 80,000 people across Uganda, including

more than 47,000 IDPs. Several schools and health centres were destroyed as

well as damage to infrastructure and so on. (USAID, 2011: p113)

25 June 2012: Another landslide in Namaaga and Bunakasala in Bududa killed

18, and injured 9 (GLIDE, 2012). Apparently residents had noticed cracks in the

ground and heavy earth movement hours before disaster struck, but no one

reported this to the authorities for fear of being forced to move.

24


Predicting locations of landslide hazards is difficult, probably more difficult than

predicting the location of most other hazards. In a comparison of landslide and storm

prediction, the IFRC (2005: p53) state:

Flood and landslide hazards are local. They need to be assessed by detailed

hazard mapping months or years before… They can also change dramatically as

trees are cut or as land use and drainage are modified by human actions. As a

result, such hazards prove harder to predict and warn of than high winds and

coastal storm surges.

The government of Uganda identified the paucity of maps and geographic data as

contributory factors that impede disaster risk reduction (OPM, 2009: p35). The

government admitted that risk reduction has been limited by the lack of resources

needed for analysis and reporting in the form of maps etc. It went on to say:

Risk /hazard maps are generally unavailable. Some land use capability maps

exist in the ministry of Agriculture but they do not indicate the risks of

livelihoods from occurrence of natural hazards. There is need to undertake

systematic hazard analysis in the form of mapping and other presentational aids.

and:

Government will… develop a community based risk mapping and preparedness

planning framework and train communities at risk [in] its use.

In the subsequent National Policy for Disaster Preparedness and Management (OPM,

2010: p69) the Ugandan government promised that it,

will come up with national risk, hazard and disaster profiles and maps of the

country depicting each of the known natural and human-induced disasters. The

profiles and maps will be updated at least once every 3 years.

There have been a number of outside-sponsored studies that have recommended that

the Ugandan government should set up its own NSDI. Musinguzi et al. (2011) mention

four reports in particular:

25


The Design and Development of Geographical Information System for Uganda

(GoU 2001).

Review of the Status of Land Information Systems in Uganda Kampala (GoU

2003).

Detailed Plan for the Design and Implementation of a Land Information System

for Uganda (GoU 2005).

LIS Preliminary System Design and Architecture (GoU 2007).

The 2007 recommendation was accepted and built into the Ugandan National

Development Plan 2010/11 – 2014/15 (GoU, 2010). Under the objective “Increase

availability [...] of land information for planning and implementing development

programmes” there are plans to review and implement a digital mapping programme,

and to roll out a decentralised GIS-based National Land Information System (ibid:

p166-167). Under the objective “Improve capacity and efficiency for physical

planning” there are proposals to create a GIS centre and NSDI, and to train planning

staff to use it (ibid: p169). The Ugandan Cabinet approved a National Land Policy for

Uganda in February 2013, and this included approval for an automated Land

Information System cadastre (GoU, 2013).

On 15 October 2007 the Ugandan government launched the Peace, Recovery and

Development Plan to help areas affected by incursions from the Lord’s Resistance

Army (Marino, 2008). Initially this was aimed at areas in northern Uganda, but in June

2009 the Plan was expanded to include eastern Uganda too, including the Mbale region

which is the subject of this case study. The Plan has several programmes, such as local

government enhancement, humanitarian assistance, community development, and

environment management. As with many government-led recovery plans in Africa,

there is much controversy in Uganda about how much money actually benefits those in

need and how much disappears due to corruption (ibid) §.

Partnerships Overseas Networking Trust (PONT)

The University of South Wales has links with Uganda through PONT

(www.pont-mbale.org.uk). This is part of the twinning relationship between the

§ Transparency International (2013) awarded only 26/100 to Uganda for honest dealing, ranking it as

140th out of 175 countries surveyed.26


Rhondda Cynon Taff County Borough and the Mbale region. The University of South

Wales has played a significant part in PONT’s work.

PONT encourages several local NGOs to network with each other and with local

government in order to work towards agreed priorities. The initial projects coordinated

by PONT were training of primary care workers, distribution of mosquito nets,

providing goats for orphans, and improving water supplies. Other work that is ongoing

is improving access roads to schools so children can reach them during the rainy

season, training local police and fire fighters and assisting with local IT needs. It also

acts as the coordinating organisation for initiatives in the Mbale Region that are funded

by larger organisations such as the Welsh Assembly and UN.

1.6.2. Kenyan case study: Kibera, NairobiA US organisation called GroundTruth Initiative has been instrumental in mapping

three slums in Nairobi, Kenya, in an initiative called Map Kibera

(www.mapkibera.org). GroundTruth worked within the community to develop OSM’s

coverage of the Kibera slum so that it covered the whole area. Initially GroundTruth

trained some 20 to 30 people in the use of Global Positioning Systems (GPS) and

mapping software. About 13 of them became committed project team members. They

used consumer-grade GPSs to georeference features and then entered them into OSM

(Map Kibera, n.d.).

The mapping work was subsequently repeated in the Mathare (www.mapmathare.org)

and Mukuru (www.mapmukuru.org) slums of Nairobi. The Map Kibera, Map Mathare

and Map Mukuru projects are widely hailed as exemplars of how to run community

mapping projects successfully. They are examined in more detail in Chapter 6.

1.7. Thesis structure

Chapters 2, 3 and 4 review the literature on crowdsourced mapping for disaster

preparedness, mapping technologies, and behavioural models respectively. In Chapter 5

a range of possible research methodologies are reviewed and the chosen method is

justified. Chapters 6, 7, 8 and 9 contain the primary research of this project. Chapter 6

27


records interviews about mapping developing countries, a short survey about mapping

technologies and the empirical fieldwork. In Chapter 7 concepts drawn from the

primary and secondary research are brought together in the development of TASUT,

and Chapter 8 provides guidelines on how TASUT could be applied to mapping for

disaster preparedness. Chapter 9 explains how the model and guidelines were

evaluated. Final conclusions are set out in Chapter 10.

Throughout the thesis, findings that are relevant to the design of the TASUT model,

such as the importance of a concept and its influence on technology acceptance, are

tagged with <Key Point Model>.

28


2. The Problem Space

2.1. ContextThis chapter explains geo-information needs in disaster management, barriers there

have been to meeting those needs, and identifies how crowdsourcing techniques and

technologies can overcome those barriers.

Since the 2005 World Conference on Disaster Reduction in Kobe, Hyogo, Japan, the

Hyogo Framework for Action has been the key instrument for guiding disaster

planning. It has been adopted by 168 member states of the United Nations and it aims

to build resilience to disasters among nations and communities and thus achieve

substantive reduction of disaster losses in terms of lives, and the social, economic and

environmental assets of communities (UNISDR, 2007). The Hyogo Framework has

five priorities for action:

1. Ensure that disaster risk reduction is a national and a local priority with a strong

institutional basis for implementation.

2. Identify, assess and monitor disaster risks and enhance early warning.

3. Use knowledge, innovation and education to build a culture of safety and

resilience at all levels.

4. Reduce the underlying risk factors.

5. Strengthen disaster preparedness for effective response at all levels.

The next section explains these concepts and the role of GI in disaster management.

2.2. Disaster management2.2.1. Categorising and characterising types of

disasterDisaster / emergencyA disaster is a particular type of emergency. ‘Emergency’ is an umbrella term that

covers dangerous situations whether outside help is required or not. An emergency is,

“a course of events that endangers people, property and the environment, or a deviation

29


from planned or expected behavior” (Karatunga, 2005: p4). Most definitions of

‘disaster’ refer to an emergency that requires outside assistance. For example, the

UNISDR web site (2009) defines it as, “A serious disruption of the functioning of a

community or a society involving widespread human, material, economic or

environmental losses and impacts, which exceeds the ability of the affected community

or society to cope using its own resources.” In the context of this thesis, the need for

outside assistance is a defining characteristic. When disaster response workers arrive in

a disaster area few, if any, will be familiar with the area and so will need to access GI

quickly and efficiently.

Slow-onset / sudden-onset disastersDisasters lie on a scale from ‘slow-onset’ to ‘sudden-onset’. At the extremes, a famine

might develop as a result of endemic poverty and a series of failed crops over a period

of years, whereas a tsunami may give little advance warning because it can travel

across an ocean at up to 800 km/h. Although the slow/sudden-onset concept is helpful,

real-life disasters may be difficult to categorise. For example, a landslide is usually

considered as sudden-onset, but it may result deforestation and climate change that

declined over a longer period of time.

The nature of disaster management differs for slow-onset and sudden-onset disasters. In

slow-onset disasters the key problem is often raising public awareness and support as

the situation gradually worsens. For sudden-onset disasters the problem is usually

related to disaster preparedness, in particular to motivate people to allocate resources in

preparation for an indeterminate disaster that might or might not happen in the future

(Twigg, 2004: p58). For a slow-onset disaster it may not matter if GI is available this

week or next week so there will be opportunities to find GI and/or organise a mapping

initiative. This project is mainly concerned with preparedness for sudden-onset

disasters in which GI is needed very quickly indeed. Unless specified otherwise in this

thesis, the word ‘disaster’ refers to sudden-onset disasters.

Natural / man-made disastersIt used to be common to distinguish between natural and man-made disasters.

However, a disaster is usually characterised by the effect of an event on the local

population, so there is a human element to any disaster (Redmond, 2005). Furthermore,

30


many disasters have some man-made contributory cause such as climate change, soil

destabilisation or residential development in vulnerable areas.

Disaster management / economic developmentTraditionally humanitarian work was often considered to have two categories:

economic development or development aid or development co-operation, which

seeks to address underlying causes of humanitarian problems; and

disaster response, which seeks to ameliorate suffering after a disaster.

In practice the concepts overlap, an example being land mine clearance, but the

distinction had some value. As disaster-related work has developed from narrow

disaster response and to broader disaster management, the links between economic

development and disaster-related work have become more apparent. Disaster

management is no longer an ad hoc sticking plaster for an emergency but a feature of

long-term development, as represented in Error: Reference source not found.

31

Economic development

Disaster response

Evolving view of economic development and disaster management.

Economic development

Disaster management

Mitigation

Prepar

edne

ss

Reco

very

Traditional view of economic development and disaster management.

Figure 2.1: Traditional and evolving views of economic development and disaster response.

Response

used with permission


Misra (2013) proposed some relevant tasks for meaningful disaster management. First,

to evolve a holistic approach that not only focuses on ‘hot spot’ communities but is

based on promoting social capital, and building processes. The intention is to empower

communities to plan, implement and be resilient to disasters. Second, to change the

conventional mind set of vulnerable communities from reactive to proactive, and to

reduce over-dependence for assistance on governments and the public sector. Third, to

evolve workable bottom-up processes whereby community-led plans are integrated

with official higher-level plans. The World Bank has produced a ‘tool kit’ publication

that provides advice for community-based disaster management (World Bank, 2009b).

The tool kit contains nine modules that refer to the effect of community-based disaster

management on a project, prevention/ preparedness/ mitigation, disaster response,

longer-term rehabilitation and construction, monitoring, and some detailed

consideration about gender, disability, age and minority groups.

32

Disaster preparednessContingency planningCapacity-building

Disaster responseCommunity resilienceSearch-and-rescueAid distributionEffect mitigation

Disaster mitigation and preventionVulnerability reductionDevelopment

Disaster recoveryRebuildingRemediatingRehabilitating the community

Strategic planningRisk analysis.Hazard analysis.Vulnerability analysis.Countermeasure selection.

Disaster strikes

Figure 2.2: A typical disaster management cycle.


Disaster management is usually subdivided into various stages and often described as a

cycle of activities. For example, the UK Civil Protection Lexicon (Cabinet Office,

2013) defines the cycle as a sequence of “four civil protection phases”: mitigation,

preparedness, response and recovery. Some other authorities use variations of these

particular terms.

The focus of this thesis is on mapping for disaster preparedness, which is the subject of

section 2.2.2. Nevertheless, it is important to identify what GI will be needed by

response organisations during disaster response; this is considered in section 2.2.3.

33


2.2.2. Disaster preparednessThis project is principally concerned with mapping for disaster preparedness. Disaster

preparedness is that component of disaster management carried out in readiness for a

disaster; it is largely about contingency planning and capacity building. Disaster

preparedness is the extent to which disaster prevention enables the effective and

efficient prevention, reduction, control, mitigation of, and response to disasters. The

United Nations has used this definition of disaster preparedness (UNISDR & UN

OCHA, 2008: p3):

The capacities and knowledge developed by governments, professional

response organisations, communities and individuals to anticipate and respond

effectively to the impact of likely, imminent or current hazard events or

conditions.

The International Federation of Red Cross and Red Crescent Societies uses an

alternative definition (IFRC, 2000: p6):

Disaster preparedness refers to measures taken to prepare for and reduce the

effects of disasters. That is, to predict and – where possible – prevent them,

mitigate their impact on vulnerable populations, and respond to and effectively

cope with their consequences.

The same document (ibid: p6) suggests that disaster preparedness has three main

objectives:

1. Increasing the efficiency, effectiveness and impact of disaster emergency

response mechanisms at the community [and national] level.

2. Strengthening community-based disaster preparedness […] This could include

educating, preparing and supporting local populations and communities in their

everyday efforts to reduce risks and prepare their own local response

mechanisms to address disaster emergency situations.

3. Developing activities that are useful for both addressing everyday risks that

communities face and for responding to disaster situations – for example,

34


health, first aid or social welfare programmes that have components useful for

disaster reduction and response.

In terms of reducing casualties, disaster preparedness has been shown to be worthwhile.

For example, a cyclone that hit Bangladesh in 1991 resulted in more than 140,000

fatalities. The Bangladeshi government, with the help of the United Nations and

partners, put in place early warning and other disaster preparedness measures. When a

cyclone of similar force struck in 2007, the number of deaths was reduced by over

97%, with 3,400 fatalities (UNDP, 2008).

Relatively little has been written about the positive financial value of disaster

preparedness. Cardona et al. (2010) proposed a fiscal model that draws on experience

from the insurance industry. The proposed Disaster Deficit Index compares probable

loss with the country’s financial ability to cope with a critical impact. The Index would

assist government decision-making when deciding priorities for disaster preparedness

and mitigation. Nevertheless, as De Hoop and Ruben (2010) conclude, there are a

number of perverse incentives not to invest in disaster preparedness. For example,

anticipation of external assistance may explain why agencies tend to under-insure.

Indeed many insurance companies may be unable to pay out in the event of a major

disaster anyway.

It would be a mistake to consider disaster preparedness as a one-off exercise that can be

completed; it has to be an ongoing process as circumstances, assets, hazards,

vulnerabilities, resources and so on change. In industrialised countries disaster

preparedness measures may be quite extensive, including perhaps an institutional

framework, tried and tested response plans and policies, compatible information

systems and warning systems, and stockpiles of emergency-related materials such as

shelters, fuel, food, search-and-rescue equipment and so on. In East Africa some of

those measures may be in place, and there may be an appearance of preparedness, but it

is often a façade that hides the lack of any real, tested plans. Often the responsibility for

local disaster preparedness is delegated from central government to districts, counties

and communities (e.g. see OPM, 2009) without the training and resources to do it. The

two main aspects of disaster preparedness are contingency planning and capacity

building.35


Contingency planningGI and GIS can assist in contingency planning in several ways:

population density information has many uses, including helping to estimate

likely casualties and internally displaced persons (IDPs);

relief workers will need to know where landmarks and settlements are for

navigation and context;

information about the transportation network will be needed for emergency

access and evacuation, annotated with bridges and fords in case any become

damaged or impassable;

relief workers will need to know the locations of emergency service and local

authority buildings to seek help or to attend meetings;

similarly, the locations of health centres and hospitals for casualties;

schools, sports fields and stadia may be needed as locations for response

organisations, field hospitals, feeding stations, resource holding areas and safe

zones for IDPs;

water, sanitation and hygiene (WASH) resources that will be needed for disease

avoidance/reduction;

the energy and communications infrastructure will also need to be identified,

safeguarded and perhaps repaired.

Attribute information about features can be important too. For example, in addition to

knowing the location and name of a hospital, response organisations will need to know

what type of hospital it is, its size, the number of emergency beds, the number of

intensive care beds, and even the number of doctors and nurses. Response organisations

can use this to decide where to set up field hospitals, where to send casualties, where to

send medical supplies and so on.

Population density and demographic information are particularly important. Redmond

(2005) argues that, during disaster response, knowing the number of survivors in the

disaster zone is possibly even more important than knowing the number of deaths. The

survivors will need to be evacuated, treated medically, fed and housed. Knowing the

relative economic vulnerability, age profile, and livelihoods can help when prioritising

36


resources. Not all developing countries have up to date census data. Uganda is fortunate

in this regard because a national census was completed in 2014 (Ladu, 2014).

Capacity buildingDuring disaster preparedness phase, local communities need to be trained in the use of

disaster management techniques; this is usually called ‘capacity building’. The UN’s

definition of basic concepts and terminologies in governance and public administration

defines capacity building as follows (UN CEPA, 2006: p7):

Specifically, capacity-building encompasses the country’s human, scientific,

technological, organizational, institutional and resource capabilities. A

fundamental goal of capacity-building is to enhance the ability to evaluate and

address the crucial questions related to policy choices and modes of

implementation among development options, based on an understanding of

environment potentials and limits and of needs perceived by the people of the

country concerned.

One of the first on-site activities in a mapping initiative is to train members of the local

community in the use of mapping technologies. In a study of capacity-building projects

in a variety of developing countries, Wagner et al. (2001) identified that collaborating

with organisations that already have basic skills can help when building capacity. <Key

Point Model> For collaboration to have a beneficial effect, they claimed that certain

‘enabling conditions’ had to exist in the country. “If the country lacks the ability to

absorb… knowledge and put it to good use, its potential positive and lasting effects will

simply drain away.” (ibid: p57) The authors identified a baseline of infrastructure to

make collaboration effective, and nations having a capacity below this baseline cannot

measurably use collaboration to build capacity. The concept is illustrated in Figure 2.3.

37

Scientific Capacity

Lagging Developing Proficient Advanced

Collaboration alone cannot build capacity

Collaboration alone can

build capacity

Baseline “Enabling Environment”


Figure 2.3: Baseline for a nation to make collaboration effective for capacity building (Wagner et al., 2001)

Wagner et al. conceded that the baseline would vary depending on the nature of the

collaboration.

Concerns about disaster preparednessGovernment expenditure on disaster preparedness may not be seen as worthwhile by

the electorate, and thus not a vote winner by politicians. Furthermore, some disaster

prevention activities might be deeply unpopular, such as preventing deforestation in an

effort to stabilise the soil. Conversely, a cynical politician may reason (Jagger, 2007:

pp261-267) that a disaster can bring in substantial foreign aid, and that a government’s

generous response to a disaster can be a vote winner. Thus such a politician may see

disaster avoidance and disaster preparedness as counter-productive in electoral terms.

Consequently, there may be no resources allocated to disaster preparedness, mapping,

asset identification, hazard analysis and so on (ibid).

Where the interests of disaster preparedness and voter popularity may coincide is some

time after a disaster, when disaster recovery begins to evolve into disaster

preparedness. Mitigation and preparedness measures may be popular while the effects

of the previous disaster are still reasonably fresh in people’s minds and they are

concerned to avoid a recurrence. It’s at this point that resources may be available for

geographic technologies. Some may consider this as ‘shutting the stable door after the 38


horse has bolted’, but it may be an effective way to promote disaster preparedness for

the future (for example, see McDaniels et al., 2008: p313; Xiao and Peacock, 2014: p).

2.2.3. Disaster responseAlthough the focus of this project is on mapping for disaster preparedness, the types of

GI needed by response organisations in the event of a disaster are also important. GI

and GIS can assist with initial crisis management by geo-locating the extent of a

disaster, geo-locating field reports, providing realistic estimates of the number of

people affected and identifying assets that may have been damaged. GIS might also

help forecast how a disaster could evolve in the coming hours and days. Furthermore

GI and GIS can assist with aid and rescue in many ways, including:

identifying access and egress routes (Ramchurn et al., 2013), geo-locating

hazards such as fires and toxic spills and thus helping to plan deployments;

recording which areas have been searched for survivors in order to coordinate

effort (Limbu, 2012);

geo-locating disaster response organisations, local administrative offices and the

areas they are serving, often referred to as ‘Who-What-Where’, to create a

common operational picture (Yap, 2011).

Concerns about disaster responseJagger’s survey (2007: p235) among the disaster response community identified a

range of resistance factors to rapid response. The most significant were:

the magnitude of disaster;

the state of readiness of civil defence and local emergency services; and

the degree to which disaster mitigation and preparedness are developed.

Conclusions from Jagger’s survey reinforced the importance of planning for disasters,

capacity building at the local level, and the ongoing need for disaster mitigation and

preparedness.

39


2.2.4. Disaster strategic planningDisaster strategic planning is about coordinating all disaster management work (see the

centre of Error: Reference source not found on page Error: Reference source not

found); it needs a variety of types of information. The Ugandan Department of Disaster

Preparedness and Refugees’ report on disaster preparedness (OPM, 2008: p4) stated:

The social, economic and environmental costs of disasters shall be considered

by the public and private sectors during the planning and development

processes. The socio-economic and environment impact assessments shall be

undertaken to guide planning and budgeting for Disaster Preparedness and

Management. Research on the likelihood of disasters and the assessment of the

likely social, economic and environmental impacts will be conducted regularly

as an integral aspect of disaster preparedness and management.

Risk analysisA risk is measured as the significance of a potential disaster in terms of its assessed

likelihood and impact (Cabinet Office, 2013). The risk of an emergency might be

defined quantitatively using this formula:

re = P(H) • P(E) • i

where re is the risk of an emergency, P(H) is the probability of a hazardous event

occurring, P(E) is the probability of the hazard leading to an emergency, and i is the

impact.

So, in order to establish the level of risk, the analyst needs to know the likelihood of a

hazardous event happening, the likelihood that it will become an emergency, and the

likely impact of the emergency. The following explanation of risk analysis may seem to

imply that the analyst conducts each step in simple sequence, first identifying assets,

then performing hazard, vulnerability and capacity analysis, and so on. In practice it is

a much more iterative process and the boundaries between these steps may be blurred.

For example, impact analysis might take into account the ability of the community to

cope, since a disaster is an emergency that exceeds the ability of the affected

community to cope (UNISDR, 2009). Thus the risk of a disaster might be defined as:

rd = P(H) • P(E) • (i - c)

40


where rd is the risk of a disaster, c is the capacity to cope, and the other terms are as

above. It may not be possible to calculate c formally, though.

Teeuw et al. (2012; p118) identified a variety of uses for GI and GIS in risk analysis,

such as predicting drought and locust infestations, modelling ‘what if’ scenarios, and

identifying neighbourhoods that face evacuation transport difficulties.

Asset identificationAlthough it may seem obvious, the first step is to identify assets that might be at risk,

such as:

residential and other populated areas;

public facilities, such as hospitals and water/sanitation/hygiene (WASH)

resources;

private sector assets, such as a chemicals factory;

human-related assets, such as communities;

natural resources, such as springs and rivers;

infrastructure, such as roads, power lines; and

disaster-relevant resources, such as control centres, search-and-rescue

equipment, stockpiles of relief goods, information systems and disaster response

plans.

Leidig et al. (2013) carried out work on automating the identification of assets using

open source software and a technique called ‘Object-Based Image Analysis’. Initial

results indicate that this technique, in conjunction with open source software, can be

effective at automatically identifying hazardous terrain and vulnerable features.

Features their system could identify included residential and industrial areas, and

critical infrastructure (major roads, railways, bridges, ports, airports etc.) Although this

technique could not provide detail, such as place names, type of facility, capacity and

so on, the GI generated may provide a good basis for detailed ground-based surveys.

Hazard analysis In hazard analysis all the situations that have the potential to bring disaster, but which

can be mitigated, are identified (Cabinet Office, 2013). Twigg (2004: p166) warned

41


against outsiders who see themselves as experts who decide what the hazards and

vulnerabilities are without any dialogue with local inhabitants. Taleb (2010) said that,

by failing to identify community needs, priorities and capacities, such experts could

impose or promote solutions that were inappropriate. Between 2004 and 2006 UNEP

undertook a study of indigenous knowledge in Africa regarding various topics,

including disaster management (UNEP, 2008). It documented a variety of traditional

ways in which local communities could predict emergency situations. For example,

around Lake Victoria, the arrival of large numbers of common swallows was seen as an

indicator of the onset of the rains, and they could predict that it would rain on a given

day by the changing songs of the robin. Conversely, if the swallows delayed their

immigration, it indicated the possibility of poor seasonal rainfall. Similarly, the late

appearance of lake flies moving over Lake Victoria signalled the late onset of the rains

and poor rainfall. When spiders that feed on the lake flies set dense webs across the

winds, that signalled the coming of lake flies and thus imminent rainfall; if the webs

were set late and were low in density, that indicated the late onset of the rains and poor

rainfall (ibid: pp65-66).

GI and GIS can assist by recording locations of past disasters and identifying hazards.

A particular use of GIS in identifying hazards is terrain modelling. GISs have been

used to model landslide susceptibility (e.g. Feizizadeh and Blaschke, 2014), flooding

(e.g. Fura, 2013) and volcano lahar flows (Manville et al., 2013; Landeg, 2013). Other

factors, such as land use, vegetation, topology and hypsography, may help predict the

location and extent of fires or famine. New hazards may develop for various reasons,

such as changes in climate (e.g. heavier rainfall), reduction in resilience (e.g.

deforestation), and population growth and migration (e.g. new building developments).

Often several factors combine to cause new hazards, such as heavy rainfall coupled

with deforestation causing rivers to inundate new dwellings that were built on

floodplains.

The interaction of multiple hazards can exacerbate a situation. Shi et al. (2013)

proposed three categories of multi-hazard scenarios:

Hazard clustering:

o the unfortunate coincidence of unrelated hazards;42


o for example, there may be an earthquake at about the same time as a

flash flood.

Hazard chain:

o where a primary disaster might trigger or influence a secondary hazard,

the so-called domino effect;

o for example, an earthquake triggers a tsunami that inundates a nuclear

reactor which results in the leakage of harmful materials.

Hazard compound:

o where disaster hazards are unrelated, but one might exacerbate the effect

of the other;

o for example, a pandemic causes most search-and-rescue workers to be

unfit for work, then an earthquake strikes and the shortage of rescue

workers results in increased fatalities.

Gill and Malamud (2014) developed a model for hazard chains. Each cell indicates

whether the primary hazard triggers the secondary hazard, or increases the probability

of the secondary hazard, or both. In the example below the authors indicate that a storm

can trigger or increase the probability of floods, a flood can trigger or increase the

probability of a landslide, and indeed that a landslide can cause localised flooding.

43


Figure 2.4: An example of how a hazard can cascade other hazards(Gill and Malamud, 2014, used with permission).

Vulnerability and capacity analysisThe Ugandan Department of Disaster Preparedness and Refugees’ report on disaster

preparedness (OPM, 2008: p3) stated:

Disaster preparedness and management depends on an accurate analysis and

mapping of the vulnerability and susceptibility of communities to risks. It shall

involve geo-referencing mapping and livelihood zoning. Undertaking

vulnerability analysis shall be part of the early warning system.

Vulnerability analysis identifies weaknesses in whatever protective measures already

exist against the hazards previously identified. Compared with hazard analysis, it tends

to be more inward looking, that is, it looks at the people and assets to be protected and

identifies how they might be better protected. ActionAid International (2005) has a

28 page guide on vulnerability analysis. It highlights that a given situation’s

vulnerability is dynamic and complex, which makes it difficult to analyse directly. Any

analysis of vulnerability will contain an element of personal judgement. When

identifying vulnerabilities, analysts should consider a range of different types of

44


vulnerability such as physical, social, organisational and motivational vulnerabilities

(Twigg, 2004).

It is common to compare a community’s vulnerabilities with its resilience, that is, the

capacity of a community to cope. Indeed the two are often analysed together. For

example, the International Federation of Red Cross and Red Crescent Societies

promotes the Vulnerability and Capacity Assessment method (IFRC, 2007). This uses

various techniques to establish, at grass-roots level, communities’ exposure to hazards

and their capacity to resist them. These form the basis of consultation and agreement

between the local community, local and central government, and non-governmental

organisations (NGOs).

GI and GIS can assist by geo-locating the vulnerabilities (such as populated flood

plains and areas of relative poverty), identifying local capacities (such as hospitals and

refuge areas), and presenting the information in a way that is easier to understand. Such

vulnerabilities go beyond basic geographic features. For example, Chen et al. (2013)

describes how a technique called the ‘Social Vulnerability Index’ (SoVI ®) can be used

in conjunction with GIS to highlight areas of high social vulnerability in the Yangtse

River Delta Region in China. However, their study confirmed the need for

comprehensive GI and georeferenced socioeconomic statistics to calculate the Index.

Teeuw et al. (2012; p118) give an examples of identifying vulnerable communities by

combining topography and land cover maps with census and survey data relating to

socioeconomic deprivation.

Impact analysisA landslide in an unpopulated area might not be very significant, but a landslide that

destroys a school, hospital or heavily populated area might be very serious indeed. GI

and GIS can assist by indicating the number of people and assets located in a danger

area. They can also georeference disaster-relevant assets, such as the location of

emergency services (Hacklay et al., 2014: p38). Zerger and Ingle Smith (2003)

evaluated the application of GIS in impact analysis of potential cyclone disasters. They

highlighted the limitations of GIS for real-time disaster planning, including the scale of

spatial data, its suitability for regional-scale decision making, and the risk manager's

requirement for temporal rather than spatial detail.

45


Other non-technical impediments include custodianship and system implementation for

disaster risk management. Their findings showed that the use of GIS in urban disaster

risk management can readily fail due to problems in implementation, user access and

information about the situation.

Countermeasure selectionCountermeasure selection (sometimes known as risk treatment) is the process of,

“determining those risks that should be controlled (by reducing their likelihood and/or

putting impact mitigation measures in place) and those that will be tolerated at their

currently assessed level” (Cabinet Office, 2013). Ideally, local communities should be

responsible for at least some of the countermeasures. In the event of a disaster, the

response of the local community in the first few days can determine how many lives

can be saved (Carter, 2002; IEG, 2011).

GI and GIS can assist by geo-locating disaster-relevant assets (where are the

emergency services located?), gap analysis (where are new services most needed?), and

disaster modelling (what would be the benefit of each countermeasure?) By visualising

potential disasters and how countermeasures might mitigate them, planners can

prioritise resources more effectively.

2.2.5. Information needs in disaster managementA principal use for information is to help make decisions, and the focus of this project

is on providing organisations with relevant GI in order to be prepared for a sudden-

onset disaster. At the commencement of disaster response, relevant information is

scarce and often unverified. The first responders, such as rapid-assessment and search-

and-rescue teams, are faced with critical gaps in information. Even when information is

available, it may come from heterogeneous systems and use incompatible data formats.

Team leaders are faced with uncomfortable decisions about whether to act on

unreliable information or to wait for verification; either way, lives may be lost (Limbu,

2012: pp10, 19). Yap (2011) compiled a table of disaster relevant information needs,

and indicated whether each one was relevant in the disaster preparedness, response,

recovery and reconstruction phases.

46


Information Preparedness

Res-ponse

Rec-overy

Recon-struction

GI?

1. Contacts at local community, regional and national levels

X X X X

2. Local disaster plans, procedures and policies

X X

3. City and housing plans X X Y4. Phone management databases

X X

5. Emergency centres: who-what-where

X X Y

6. Telecom: infrastructure, laws, organisations

X X X

7. Social, demographic and economic data

X X X X Y

8. Safety and environmental standards and codes

X X

9. Cultural asset information: significance, age, construction material and condition

X X Y

10. Land use plans X X X Y11. Critical infrastructure inventories

X X Y

12. Building inventories X X X Y13. Property ownership records

X X X Y

14. Birth and medical records

X X

15. Hazard maps: nature, site and real time changes

X X Y

16. Vulnerability data: who, where, how

X X X X Y

17. Loss/damage data X X X Y18. Weather data: short and mid-term

X X Y

19. Available resources: what, how much, who

X X X X Y

20. Formal key decision-making: who makes what decisions, when, how?

X X X X

21. Informal local authority structures in the family, in the community

X X X X

Table 2.2 Disaster-Relevant Information Needs (Yap, 2011) – augmented to highlight GI

In addition to the information needs of responders, it is acknowledged that people who

are caught up in disaster situations also need information. Survivors may be separated

from their families, or lack shelter and adequate food, or are confused by the events

occurring around them (DFID, 2000). Therefore information is needed to assist disaster 47


responders and to inform those affected. Indeed in developing countries, some of those

affected may be in areas difficult to reach; for days or even weeks, information

broadcast over local radio might be the only outside ‘aid’ they receive (IFRC, 2005:

p12).

2.2.6. Geo-information needs in disaster managementThe previous sections have identified a number of detailed ways in which GI and GIS

can assist disaster management. Furthermore, Table 2.2 indicates that over half of the

classes of information are (at least partly) geographic. Verjee’s study (2007)

highlighted that GIS-based analysis of a disaster area can have a decisive impact upon

the coordination of humanitarian assistance. In particular, the 87 respondents to his

quantitative survey indicated that it was useful for gap analysis, vulnerability

estimation, geo-visualisation, cluster analysis and site selection.

FeaturesThe features needed for disaster management in a given locality will vary dependent on

local needs, hazards and vulnerabilities. Generally, the features fall into two main

categories (Cova, 1999; Hacklay et al., 2014; Teeuw et al., 2012; Jagger, 2007;

Karatunga, 2005; Montoya, 2003; Shekhar et al., 2012; Twigg, 2004; Verjee, 2007;

World Bank, 2009b; author’s primary research):

Baseline information about the locality before a disaster (disaster preparedness):

o administrative boundaries***, such as counties and districts;

o census data* within each boundary to calculate the population of areas

affected by a disaster;

o demographic information such as poverty levels;

o landmarks and settlements* for navigation and context;

o transportation network* for emergency access and evacuation;

o bridges and fords in case any become damaged or impassable;

o features related to local hazards, e.g. land use, vegetation etc. to predict

fires, famine, and potential sources of flooding;

** Asterisked items are among the seven themes of the Common Operational Dataset – see the Glossary

and Table 2.3.48


o vulnerabilities, such as the extent of flood plains, historical seismic

activity, unstable hillsides, chemical factories, disease outbreaks, extent

of previous disasters, and the population/demographics of potentially

vulnerable areas;

o topology or hypsography*:

to identify the physical constraints of the disaster area; and

to assist with modelling potential floods, lahars, landslides;

o emergency services and local authority buildings so relief workers know

where to go for help or to attend meetings;

o health centres and hospitals for casualties;

o other disaster-relevant resources, such as search-and-rescue equipment,

stockpiles of relief goods, information systems and disaster response

plans;

o schools, sports fields and stadia as possible locations for response

organisations, field hospitals, community shelters, feeding stations,

resource storage areas and safe zones for internally displaced persons;

o water, sanitation and hygiene (WASH) resources for disease


o natural resources such as springs and rivers;

o energy and communications infrastructure.

Information immediately after a disaster (disaster response):

o the extent and severity of the disaster’s impact, perhaps aided by

geotagged field reports;

o estimates of casualties and displaced persons (see administrative

boundaries and census data mentioned previously);

o the spread of hazards such as spills, infestation, infection or epidemic;

o damage to infrastructure, such as roads, bridges, water supplies;

o damage to disaster-relevant resources, such as hospitals;

o locations of resources relevant to disaster relief such as disaster response

organisations, local administrative offices and the areas they are serving,

often referred to as Who-What-Where, or sometimes 3W, to create a

common operational picture;

49


o areas that have been searched in order to minimise searching the same

areas twice;

o records of where relief aid has been distributed;

o public relations information:

to gain support from local authorities and politicians;

to raise funds from the public and overseas governments.

Knowing which response organisations are where and what they are doing is important.

In January 2011 the BBC broadcast a radio programme about overseas aid to Haiti in

response to the earthquake 12 months previously (BBC, 2011). It reported critics

complaining that foreign aid organisations failed to coordinate efforts, they were

ineffective, and they swamped some areas with aid but left other areas untouched.

Who-What-Where information can help to avoid these problems.

Common Operational Dataset Baseline and post-disaster information is collected, maintained and controlled by a

range of autonomous organisations. In the event of a disaster, many of these

organisations would be expected to work together, perhaps for the first time. Response

organisations need GI that can be combined and updated to aid decision-making and

control during disaster response. If GI conforms to a common framework, data

collected before, during and after an emergency can be combined more easily (IASC,

2010). To help with this the IASC has issued guidelines for creating ‘Common

Operational Datasets’ or CODs for disaster preparedness and disaster response.

50


Dataset Recommended Governance

Mandatory Data Characteristics

Humanitarian Profile(disaggregated by admin level and populated place)

- Guardian: UN OCHA- Sponsor: UN OCHA- Source: Government, Assessments,UNHCR, IOM

- Internally Displaced- Non-displaced affected- Host family/resident community affected- Refugees- Dead- Injured- Missing

Population Statistics

- Guardian: UN OCHA- Sponsor: UN OCHA,UNFPA (Other potential sponsors could include UNDP, Government agencies or International NGOs)- Source: Government

- Total population by admin level (Individuals)-Total population by admin level (Number of Households)- Age- Sex- Average family size - Unique identifier

Administrative Boundaries(Geographic)admin level 1admin level 2admin level 3admin level 4

- Guardian: UN OCHA- Sponsor: UN OCHA (Other potential sponsors could include UNDP, Government agencies or International NGOs)- Source: Government

- Unique identifier (P-Code)- Name

Populated Places(Geographic)

- Guardian: UN OCHA- Sponsor: UN OCHA, (Other potential sponsors could include UNDP, Government agencies or International NGOs)- Source: Government

- Unique identifier (P-Code)- Names- Size classification- Population statistics- Status if capital of administrative division- Type (Village, spontaneous settlement, collective centre, planned settlement)

Transportation Network(Geographic)

- Guardian: UN OCHA-Sponsor: Logistic Cluster-Source: Government

- Roads (Classified by size)- Railways- Airports/helipads- Seaports

Hydrology(Geographic)

- Guardian: UN OCHA- Sponsor: UN OCHA (Other potential sponsors could include UNDP, Government agencies or International NGOs)- Source: Government

- Rivers (Classified by size)- Water bodies

Hypsography(Geographic)

- Guardian: UN OCHA- Sponsor: UNOSAT- Source: Remote sensing, Government

-Elevation-Resolution

51


Table 2.3. Common Operational Datasets in Disaster Preparedness and Response (IASC, 2010)

The creation of the CODs definitions is a step towards enabling inter-operation of

information systems, disaster response organisations, including those in the UN,

recognising that the COD’s schema does not specify how to characterise individual

objects that are important in a given disaster response scenario (Harvard Humanitarian

Initiative, 2010: p55).

Further uses for GIArmed with this GI, spatial analysts can produce a wide range of important information

for disaster management using GIS. Verjee (2007: pp15-26, 80) identified that GIS can

be useful for:

queries to identify possible relationships, such as the relationship between

malnutrition and land ownership;

measurement of features, such as the slope and extent of watersheds;

transformations, such as adding a polygon representing vulnerability to a hazard

or areas prioritised for de-mining after conflict;

spatial interpolation and density estimation, such as estimating the likely

number of casualties or IDPs in a locality;

optimisation, such as identifying the best locations for humanitarian services for

a given transportation network;

geo-statistical analysis to identify patterns and clusters that may indicate a

causal relationship between aid distribution and disease prevention;

geo-visualisation that helps decision-makers understand GI more easily;

hypothesis testing and simulation, such as identifying the effectiveness of

various candidate countermeasures.

These techniques are often combined to meet the needs of the particular situation. For

example, the types of analysis required during and after armed conflict may be different

from the types required after a major tsunami.

The rest of sections in this chapter consider:

52


the general need to map developing countries (2.3);

what crowdsourcing is and how it can help meet that general need (2.4);

the mapping needs of disaster management (2.5);

how crowdsourcing can be tailored to mapping for disaster management (2.6);

the mapping needs of disaster preparedness (2.7); and

how crowdsourcing can be tailored to mapping for disaster preparedness (2.8).

2.3. Why maps of developing countries need to be improved

2.3.1. ContextThe World Bank (2012) highlighted that most developing countries do not have basic

local data about where schools, hospitals, or water points are located, and the data they

do have are often out of date or incorrectly referenced. All nations are susceptible to

emergencies, but in poor nations those emergencies are more likely to be beyond their

ability to cope. Redmond (2005: p1259) says:

Poverty is the single most important factor in determining vulnerability: poor

countries have weak infrastructure, and poor people cannot afford to move to

safer places. Whatever the disaster, the main threat to health often comes from

the mass movement of people away from the scene and into inadequate

temporary facilities.

Some developing countries have never had a concerted topographical exercise; others

have not been surveyed since colonial days (Steklis et al., 2005††); yet others have only

ever been mapped at 1:1,000,000 scale as part of the major International Steering

Committee for Global Mapping initiative. Detailed maps may be considered militarily

sensitive and not made available to the public (Twigg, 2004). In other countries, non-

†† Steklis et al. (2005) recounted how they created a regional map of Uganda, Rwanda and Zaire (now

Democratic Republic of Congo). For Zaire they had to use maps produced by the Belgian Colonial

Service in 1938 which, they claim, “is still, amazingly enough, the most recent comprehensive map of

the entire area with significant detail.”53


digital mapping techniques still prevail, for example, in Nepal where government

bodies prefer compass and chain surveys (Shrestha, 2006).

In some countries access to topographical data at scales larger than 1:50,000 is still

restricted, and mapping by communities is prohibited; ‘resource grade’ mapping is not

allowed and only high-accuracy, ‘survey grade’ mapping by licensed geodetic

engineers is accepted (Rambaldi et al., 2006). However, professional mapping is slow

and expensive. Informal developments, such as slums, grow quickly. The result of

these factors is that many such countries have badly out-of-date maps showing little

detail. In extreme – but thankfully rare – cases, there may be no maps at all, such as the

fledgling nation of South Sudan when it was not allowed access to Sudan’s official

maps. Goodchild (2007) claimed that world mapping had been declining for decades.

He stated that governments were no longer willing to pay the increasing costs of

national mapping agencies, and often looked to map users as sources of income.

2.3.2. Problems in mapping developing countriesBefore looking at disaster-related mapping in particular, there are some generic

challenges to the mapping of developing countries.

Challenges in capturing GIMany countries converted their maps to digital format by digitising old maps. For

example, for the 1995 launch of the Ugandan Environmental Information Network,

districts had to digitise whatever maps they had, even though they were only at a

resolution of 1:250,000 (Gowa, 2009). Similarly, many years ago in Botswana, the

Department of Surveys and Mapping converted all of its analogue maps to digital

(ESRI, 2002: p10). This approach perpetuates whatever errors and generalisations

existed in the maps. The GSDI Association (2004: p6) says:

However, even in the new era of networked computers, the social habits of the

past continue to prohibit users from finding and using critical geographic

information. This can lead to either the abandoning of a proposed project, or to

unnecessary – and expensive – recapture of existing geographic information. In

many agencies there is still the lost opportunity to reuse incidental digital

geographic information collected for other purposes.54


Governments of developing countries receive relatively little taxation revenue, so

mapping tends to be given low priority. An examination of nations’ official maps

created by traditional national mapping agencies shows that typically only the capital

and perhaps a few major cities are mapped in consistent detail. Their coverage of

disaster-prone rural areas is usually patchy or non-existent. Even if a government has

GI, it may be fragmented, inconsistent and difficult to merge (Musinguzi et al., 2011)

as in the following example from Uganda.

Examples from Uganda of the challenges in capturing GIThe Ugandan government does not have much financial resources to devote to

mapping. For example, Eria (2012: p161) identified how the national mapping agency,

the Department of Surveys and Mapping, is allocated an annual budget of USD 25,000

– scarcely enough to map a single city, let alone an entire country. He explains that the

Department’s GIS is not used extensively because many draughtsmen and

cartographers require retraining, which would be costly. Eria claims that these

employees would rather spend longer periods performing their mapping tasks for the

sake of job security, so long as the government pays their salaries (ibid: p252).

A number of other official bodies hold and maintain digital GI, usually where

comprehensive and accurate maps bring financial benefits, such as improved utility

billing. Musinguzi et al. (2011) reviewed GI at the Ugandan National Forest Authority,

Uganda Bureau of Statistics, Directorate of Water Development, National Agricultural

Research Organisation, Ministry of Works and Transport, and Wetlands Inspection

Division. They found that the various datasets were held in different geodetic systems,

in particular:

Ellipsoid:

o Clarke 1880 RGS;

o Clarke 1866;

o WGS84; and

o Arc 1960.

Projection:

55


o UTM Zone 36:

some without any local adjustment,

three with local adjustment by 200 km on northing, and

one with local adjustment by 10,000 km on northing;

o UTM Zone 36N, and

o GCS Assumed Geographic 1 (strictly speaking this is a spatial reference

system used by software from ESRI, not a formal projection).

Furthermore, the Directorate of Water Development’s dataset was said to use “user-

defined and unique” projection and coordinate systems. Integrating these datasets

would reveal inconsistencies of several hundred metres.

The Ugandan organisations use differing geodetic systems for many reasons. Some

reasons are historical, some technical (related to the software used), and some are

caused by the relatively unusual geography of Uganda (Musinguzi et al., 2011: p12).

Uganda lies across the equator, which poses a problem when using the UTM coordinate

system. UTM has different origins for points that are to the north and to the south of the

equator. This can cause problems when loading GI into various GIS software packages.

For example, ArcGIS requires that data from the two hemispheres be loaded separately,

each with a spatial reference that specifies whether the data is in UTM Zone 36N

(north) or UTM Zone 36S (south). Conversely AutoCad expects the data to be entered

with consistent coordinates for seamless mapping. Thus integrating these sets of data is

technically difficult before we even begin to consider practical and political obstacles.

One Ugandan government department has been trying to bring GI together. In 1989 the

Ugandan government, with financial and technical support from the United Nations

Environment Programme (UNEP) and the World Bank, established the National

Environment Information Center (NEIC) within the Ministry of Environment

Protection. Its mandate was to establish an Environmental Information System to

support development through collaboration with sector institutions in the country

(Gowa, 2009). The NEIC had to abandon its early efforts to collect and store GI in-

house, for three reasons: first, the amount of data was too large for its under-specified

system; second, because gathering GI from other government institutions “sowed the

56


seeds of discord”; and third, because the right to generate maps and statistics were

claimed as the sole mandate of the Department of Statistics and Department of Surveys

and Mapping (ibid: p6). NEIC was eventually incorporated into Uganda National

Environment Management Authority in July 1995; the Environmental Information

System came to be run at the national level by a network of seven government

departments plus Makarere University. There was also a district-level network of local

government authorities. Most districts were mapped at a resolution of only 1:250,000,

with just three mapped at 1:50,000 (ibid: pp10-11). The fact that some of these

organisations employed only one person – not a team – to work on this introduced

difficulties in relation to continuity and sustainability (ibid: p11).

In his comprehensive review of the diffusion of GIS in Uganda, Eria concluded that the

major challenges were (Eria, 2012: p158):

(1) the cost of licensing the software, and related costs of maintaining the

hardware and software, (2) insufficient training facilities within the country, (3)

a lack of awareness about the importance of GIS technology, (4) the lack of a

support network of GIS specialists in the country, (4) [sic] a culture of

corruption and unethical institutional practice, (5) bureaucratic roadblocks in

the acquisition of equipment and in the hiring of new GIS staff, (6) brain drain,

meaning the loss of skilled personnel to developed countries, (7) a project-

driven instead of problem-driven approach to the use of GIS, and a (8)

dependency on donor funding to support GIS activities.

The reason why so many departments have acquired or recorded their own GI

independently is because each department is given a mandate to collect certain types of

data. For example, the Department of Surveys and Mapping has the mandate to record

topographic and political boundaries, whereas The Ugandan National Forest Authority

has the mandate to record forestry. Data sharing laws and policies in Uganda are weak.

Apart from citizen rights under the Access to Information Act 2005, there is no

requirement for government departments to share data. There are no policies about data

sharing among departments either (Uwayezu, 2010: pp14-17; Eria, 2012: pp142, 201-

203). If a Ugandan public body requests GI from another public body, it usually has to

57


pay a fee. To avoid this they gather their own GI despite the duplication of effort (Eria,

2012: pp69, 202).

Challenges caused by the rate of changeKeeping GI up to date is a major task in itself. Tele Atlas (2007) claimed that 10-15%

of its map features changed every year; that figure could rise to 40% in high-growth

areas. One might think that geographic features in developing countries would change

infrequently. However, towns can spring up and grow rapidly. The route of an unsealed

road may become impassable so drivers find an alternative route. Also place names

may change as a result of political upheaval. In South Africa, for example, street names

have been changed to be more politically acceptable. This creates a lot of work for

cartographic companies in updating databases, checking spelling, and maintaining

aliases. A key feature of developing countries particularly relevant to GI and mapping

is that population growth is often much higher than in industrialised countries. Central

and Eastern Africa has the highest growth rate in the world at 2.8% and 2.7% p.a.

respectively; this is greater than worldwide population growth, which is 1.2% p.a.

(PRB, 2012). That population growth results in the growth of residential areas,

especially city slums. That puts pressure on the update cycles and the currency of

geographic data sets.

Specifics from Uganda of the challenges caused by the rate of changeUganda’s annual population growth is 3.24%, which is the ninth highest growth rate in

the world, and has the third highest birth rate at 44.17 births per 1000 population (CIA,

2014). The population of Uganda is expected to grow from 36 million in 2012 to 94

million by 2050 (PRB, 2012). Population growth in Uganda often results in informal

and unplanned slums. Slums are seldom mapped properly and they grow in

uncontrolled ways. This means maps of where the most vulnerable citizens live are

non-existent or quickly become out of date.

Challenges to sustained use of technologiesAny solution to the mapping problem needs to be sustainable in the long-term.

Developing countries have a poor track record in keeping donated equipment up to date

and usable. Replacement components are often not readily available, and repair

technicians may not be based within a reasonable travelling distance. For example,

58


Karikari et al. (2005) reported on how local communities do not maintain or use the

equipment left behind following the end of the Ghana Environmental Resource

Management Project. They argued (ibid: p347):

There is a strong argument that it may be necessary for indigenous scientists

and decision-makers to have a greater degree of knowledge and control of GIS.

Africa still remains heavily dependent on donor support and development

assistance from the west and this presents enormous challenges. However, if

GIS is to be introduced successfully in these countries, it must be developed,

modified and controlled by indigenous people who understand the social,

economic and political context of the situation as well as the technical

capabilities of GIS (Taylor, 1991: p71). For example, after the expatriate

consultants left following the end of the Ghana Environmental Resource

Management Project (GERMP), the key problem was that of nonmaintenance

and minimal use of equipment. […] it is imperative that the diffusion and

application of GIS are tailored to specific institutions in the developing world

and that use is made of indigenous expertise where possible.

Examples from Uganda of the challenges to sustained useGowa (2009: p17) reported on an example in Masindi, Uganda of GIS software and

related hardware that was no longer functional due to a poor repair and maintenance

culture. Empirical fieldwork in Uganda found the same. For example, Mbale District

Government had a licence for ArcView 3.2 dedicated to a single PC, but the PC was no

longer functional.

In addition to the culture of non-maintenance, the way a GIS was introduced to an

organisation could exacerbate the situation. Eria (2012) recounts examples where GISs

were donated for specific projects. When each project ended the GIS was retained, but

because support ended so did its use. The Ugandan government did not appreciate the

strategic importance of GIS and so did not allocate funds for the ongoing use and

maintenance. “Thus, licenses expired, hardware decayed from a lack of maintenance,

and GIS staff members were reassigned to non-GIS roles over time” (ibid: p161). A

related problem is that when government organisations train staff in the use of GIS,

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those staff become attractive to private industrial employers who pay far higher wages

(ibid: p295).

Challenges caused by security, commercial and political concernsSome governments cite national security as a reason why maps and other geo-

information cannot be made publicly available. For example, after a 7.6 magnitude

earthquake in Kashmir in October 2005, the government of Pakistan appealed for high-

resolution satellite images of the affected area to help with the relief efforts. According

to Butler (2005), however, just days later the government forced international response

organisations to remove the images from their websites. Butler claims that the Pakistani

government was sensitive about the area since it is a disputed territory.

Official maps may be misleading, whether deliberately or not. For example, Williams

and Dunn (2003) highlighted that the true extent of indiscriminate use of land mines

may not be revealed in official maps. Conversely, they found that crowdsourced maps

were more revealing of such dangers. A similar problem is that a government may want

to suppress inconvenient evidence of poor healthcare provision, incidences of disease

and so on (Twigg, 2004). Twigg also points out that maps showing government and

industry hazards may be considered too commercially or politically sensitive to share.

Twigg adds that information on commercial hazards such as factories is likely to be

hard to find as commercial organisations prefer to keep a low profile.

2.4. Crowdsourcing: A possible solution to mapping developing countries

Since so many large parts of the world still need to be mapped, traditional data

collection models, where a few experts collect data and specialists enter them, are

impractical because there is little or no funding to do it. The background to this thesis

concerns encouraging local communities to map their areas, and to store that digitally

in a way that can be shared. Even in the nineties it was recognised that private

individuals could contribute to the mapping of nations. Morrison (1997: p26) said that

national mapping agencies, “… no longer need to ‘do’ cartography but rather need to

enable all citizens to do cartography.”60


Modern technologies allow contributions to a crowdsourced mapping initiative from a

wide variety of people. They may be experts or self-trained volunteers, and can include

employees of government authorities and NGOs, volunteers, as well as the public. In

GIS and mapping circles this process is variously termed ‘crowdsourcing’ or

‘volunteered GI’ (Zook et al., 2010; Goodchild, 2007). When the word

‘crowdsourcing’ first became popular, what was remarkable was the achievement of

unpaid amateurs who had varying degrees of expertise. However, its actual usage (and

dictionary definition) goes wider than that by embracing paid and unpaid contributors

(Kawasaki et al., 2013).

Some writers use the terms ‘crowdsourcing’ and ‘Participatory GIS’ (or PGIS)

interchangeably. For other people, though, Participatory GIS is specifically associated

with the use of local spatial knowledge of indigenous peoples and geo-spatial

technologies for the empowerment of less privileged communities (Rambaldi et al.,

2006). The term PGIS tends to be used to describe projects that are issue driven and

that emphasise community involvement in the production and/or use of GI (Dunn,

2007). Dunn considered PGIS development under four headings (1) control and

ownership of geographical information; (2) representing local and indigenous spatial

knowledge (3) the democratization of GIS; and (4) sustainability. Therefore, the term

‘crowdsourcing’ is used because it is more generic and value free than PGIS. In this

context, crowdsourcing can be considered a synonym of ‘volunteered GI’.

This project uses – but is not limited to – local spatial knowledge, and it certainly aims

to benefit local communities through improved disaster preparedness, but is not

restricted to empowering particular classes of community. Also in this project control

and ownership of the GI may be much wider, for example through OpenStreetMap’s

Creative Commons Licence.

2.4.1. Best practice in crowdsourcingBrabham (2013) has brought together best practice guidelines for using crowdsourcing

for public-sector projects from a range of sources. Several of his points are applicable

to disaster mapping projects.

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Planning phase

1. Clearly define the problem and solution parameters.

o The problem under consideration needs to be well framed and contributors

will need clear parameters for their contributions. It might be a waste of

time and effort if contributors were to submit GI that was of no relevance

but failed to submit disaster-relevant features.

2. Determine the level of commitment to the outcomes.

o Brabham’s second point is perhaps of less relevance to mapping initiatives.

In a consultation exercise, the organiser should communicate the impact the

contributions will have on the organisation. In an open data system, all

(reasonably correct) contributions are normally accepted.

3. Know the online community and their motivations.

o Motivation theory, especially in relation to crowdsourcing, is discussed in

detail in section 4.5.

Implementation phase

4. Invest in usable, stimulating and well-designed tools.

o The two most commonly used systems are OpenStreetMap (OSM) and

Google Map Maker, and there are no serious contenders so it is likely to

stay that way for the foreseeable future (see section 3.2). The OSM user

interface can be a little awkward to use, but improved editors are introduced

from time to time. The Map Maker interface is easier to use, but sometimes

the need to wait for moderation of one change delays making a related

change.

o In GIS, the data is usually of equal or greater value than the software. So

contrary to Brabham’s fourth point, the choice of crowdsource mapping

system is likely to be driven by the amount and quality of GI not the

software.

o Bridging the gap between software and data is aerial imagery. Although

imagery is data, the presence of high-resolution imagery makes a mapping

system much easier to use.

5. Craft policies that consider the legal needs of the organisation and the online

community.

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o Brabham’s fifth point mostly relates to issues of preserving free speech and

navigating issues around intellectual property rights (IPR). There is also the

issue of privacy (ICRC, 2013).

o As with point 4, the limited choice of storage for crowdsourced GI allows

little leeway. GI stored in OSM has an open Creative Commons licence,

whereas GI stored in Map Maker is the intellectual property of Google. IPR

is discussed in more detail in section 2.5.

o Privacy might be of concern in where there is armed conflict, social disorder

or repression of minorities. A feature might give away information about

individuals and their allegiances and lead to violent retribution. This is

discussed in more detail below under the heading “Privacy” in section 2.4.2.

6. Launch a promotional plan and a plan to grow and sustain the community.

o Brabham (2013) warns against two extremes in the dynamics of online

communities. First, if a community is slow to take off, it will appear to

newcomers “as if the place is a ghost town”. Conversely, if a community is

extremely large and active, newcomers may feel unwelcome. Brabham

suggests that the implementation of an online community should be

planned. There should be sufficient content for people to be able to orient

themselves, but as it grows there should be ways to welcome in newcomers.

o Ongoing sustainability of the community also needs attention, and should be

planned for by the coordinating organisation.

7. Be honest, transparent and responsive.

o Where an ongoing community needs to be sustained, relationships between

the coordinating organisation and the contributors are usually “strongest

when they are mutually beneficial and characterized by ‘win-win’

outcomes” (Heath and Coombs, 2006: p5). Brabham (2013) suggests that

those in charge of crowdsourcing initiatives should think of themselves

more as curators than managers.

8. Be involved, but share control.

o Brabham highlights the importance of letting citizens to take an active role

in the outsourcing process. For example, it may not be necessary to

moderate all GI submissions centrally if contributors can moderate each

other’s submissions.

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Post-implementation phase

9. Acknowledge users and follow through on obligations.

o Some equivalent to a ‘thank you’ to contributors may be a badge of honour

for them among their peers and colleagues, and this can be as effective a

motivator as a financial reward.

10. Assess the project from many angles.

o It may be possible to analyse people’s attractions to, contributions to, and

views about the initiative purely from online data. Surveys and face-to-face

feedback can reveal interesting lessons for future initiatives too.

Although the common view of crowdsourcing is that volunteers are members of the

general public, in practice most projects are oriented to specific tasks and recruit

volunteers according to criteria such as background and technical skills (Haklay et al.,

2014: p27). Organisations that actually need and will use GI for their work are possible

sources of people to train in the use of mapping technologies. There is a relatively high

turnover of staff in Ugandan organisations. Several of the more successful mapping

initiatives have recruited university students, who usually join for altruistic reasons, but

can be incentivised by competitions with prizes or scholarships (Chapman, 2013;

Hacklay et al., 2014: p27). Other successful initiatives have included farmers and

doctors (Narvaez, 2012: p51).

2.4.2. Ethical issues with crowdsourcingParticipation<Key Point Model> Chambers (2006: pp6-7) lists examples of how participation can be

problematic for local contributors, including:

taking up people’s time at important periods of the year (e.g. weeding left

undone);

exposing contributors to danger, for example:

o urban dwellers in Jamaica analysing violence had to be stopped for their

own safety when local thugs began to take an interest;

o women who take part in participatory activities are abused and beaten by

husbands when the outsider has left;

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raising expectations even though only outsiders benefit; and

the ethical concern of taking knowledge from local contributors and using it to

benefit outsiders, especially profit-making organisations.

PrivacyThe twin concerns here are to protect the privacy of contributors and of data subjects.

Some might say that contributors ought to be careful not to make personal information

publicly available in social media. However, the privacy controls are often set with

little protection as default, and activating the controls can involve navigating many

levels of complex menus. It is also worth considering that in some crowdsourced

mapping systems, the user ID may be linked to each person’s social network profile.

An example is Google Map Maker, which requires a Google user ID; people normally

use the same user ID for Map Maker, Google+, Hangouts, YouTube and so on.

Someone who follows a Map Maker contributor might be able to infer a lot of

information from the related social media.

Most features mapped are impersonal, but some features may give away information

about individuals. When post-disaster ‘crisis mapping’ is related to armed conflict or

social disorder, some GI might indicate to which side a person or family shows

allegiance and may lead to subsequent violent retribution.

<Key Point Model> The ICRC sets out its guidance on professional standards in the

Professional Standards for Protection Work (ICRC, 2013). It sets out a range of

standards and guidelines. Some selected points are quoted here using the original

document’s paragraph numbers (ibid: p18):

3. Protection actors must ensure that their activities do not have a discriminatory

effect.

4. Protection actors must avoid harmful effects that could arise from their work.

5. Protection actors must contribute to the capacity of other actors to ensure that

no harmful effects derive from their actions.

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6. Protection work must be carried out with due respect for the dignity of

individuals.

8. Whenever appropriate and feasible, protection actors should contribute to and

strengthen the possibility for affected populations to access information that can

help them to avoid or mitigate the risks they are exposed to.

In Chapter 6 of that document (ibid: p77) it adds:

36. Protection actors must only collect information on abuses and violations

when necessary for the design or implementation of protection activities. It may

not be used for other purposes without additional consent.

39. Protection actors seeking information bear the responsibility to assess

threats to the persons providing information, and to take necessary measures to

avoid negative consequences for those from whom they are seeking

information.

40. Protection actors setting up systematic information collection through the

Internet or other media must analyse the different potential risks linked to the

collection, sharing or public display of the information and adapt the way they

collect, manage and publicly release the information accordingly.

45. Security safeguards appropriate to the sensitivity of the information must be

in place prior to any collection of information, to ensure protection from loss or

theft, unauthorized access, disclosure, copying, use or modification, in any

format in which it is kept.

2.4.3. Critical assessment of crowdsourced mappingFor the host, the main advantages of crowdsourcing are that the data collection and

recording is free, there are a huge number of contributors spread globally, and

contributors can apply their local knowledge. For local communities, crowdsourcing

can give them some sense of confidence in the outcomes (Shrestha, 2006: discussed

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below). Sometimes crowdsourced information reveals disparities with officially-

sanctioned information. Where official sources are trying to suppress inconvenient

evidence, local people can provide a different perspective (Williams and Dunn, 2003;

Twigg, 2004; Georgiadou et al., 2009). Rambaldi et al. (2006) say that crowdsourced

mapping raises some important questions, including: Who owns the GI? Whose

questions are addressed? And what will happen when experts leave or when donor

funding dries up?

There is a tension between the desire to use professional quality software and high-

accuracy data and the need for a low-cost, independent solution that is free from IPR

restrictions. For example, Verplank (2004) reported on short-term experiments using

ESRI’s ArcPad on different mobile devices for participatory GIS in Tanzania, India and

Mali. He found that communities can use such technology, but notes various problems,

including:

various difficulties experienced with hardware compatibility and data

preparation;

the software required an elaborate number of steps to be taken in order to be

able to add GI;

people who do not have a reasonable understanding of ArcPad cannot operate it

without guidance.

Arguments about accuracy versus price, experts versus amateurs, and controlled versus

accessible will be a matter of judgement. Writing about OSM’s crowdsourcing

response to the Haitian earthquake of 2010, Zook et al. (2010: p30) suggest that:

The extent to which the tensions between expert and amateur knowledge exist

and are reconciled varies across both space and time. Some situations require

data of the highest quality, likely only to be produced by an expert with the

right set of tools and personal skills. In disaster situations, however, geographic

information need only be good enough to assist recovery workers using the

maps, meaning that crowdsourced information is likely to be just as helpful as

that produced by more centralized means. Indeed, it can be even more useful if

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peer production allows for new information to be incorporated and distributed

in near real time.

Data quality issues have to be assessed in the light of intended use. Where GI is used

for disaster management, it may not have to be millimetre-accurate; accuracy of

crowdsourced GI is discussed in more detail in section 2.6.2.

In conclusion, crowdsourced GI may be fit for purpose provided it is relatively

comprehensive and up to date. Depending on intended use, the resolution and

consistency does not have to be to the highest professional standards. The next two

sections examine the mapping needs of disaster management and how crowdsourcing

can be tailored to meet those needs. Subsequent sections focus more specifically on the

mapping needs of disaster preparedness.

2.5. Mapping for disaster management Previous sections discuss mapping developing countries in general, whereas this

section is specifically about mapping for disaster management. The Hyogo Framework

for Action recognises the value of geo-information in disaster management. For

example, among the priorities for action, under the heading “2: Identify, assess, and

monitor disaster risks – and enhance early warning”, it says (UNISDR, 2007):

Understanding risk requires investment in scientific, technical, and institutional

capabilities to observe, record, research, analyse, forecast, model and map

natural hazards. Tools need to be developed and disseminated: statistical

information about disaster events, risk maps, disaster vulnerability and risk

indicators are essential.

The importance of information gathering and management throughout disaster response

is also recognised by the Hyogo Framework. Such information is needed not only to

improve the effectiveness of the response, but also to increase financial and end-user

responsibility (ibid). GI can support disaster management stakeholders by providing

information and helping with decision making, for the following (author’s primary

research; Kaiser, 2003: pp129-136; Verjee, 2007: pp70-73):

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strategic planning for disasters, including analysing risk, identifying assets,

hazards and vulnerabilities, and forecasting potential impacts;

prioritising countermeasures and recording mitigation projects;

contingency planning and evacuation planning during disaster preparedness;

rapid assessment, identifying the extent of a disaster and estimating casualties;

coordinating damage assessment;

identifying and prioritising the most needy areas;

modelling the potential migration of affected people, taking into account their

ethnicities, religions, and tribal affiliations (Verjee, 2007: p70);

producing information to support fund-raising appeals;

identifying suitable locations for disaster response organisations (Who-What-

Where) and refugee/IDP camps;

identifying resources useful for disaster relief;

informing and orienting new relief workers;

prioritising and recording search-and-rescue missions;

monitoring relief activities, identifying under-provision through gap analysis,

and optimising relief work;

recording the distribution of relief aid;

planning disaster recovery as part of disaster response.

Error: Reference source not found suggests ways in which GI and GIS can be useful in

every stage of disaster management.

69

Mitigation

Response

Preparedness

Rec

over

y

- Asset identification- Hazard analysis- Countermeasure, contingency planning- Infrastructure development

- Population- Identification of disaster-relevant resources- Locations suitable for resource holding areas, feeding stations and IDP camps

- Relocation of IDPs- Reconstruction programmes- Gap analysis - Monitoring of recovery grants

- Search-and-rescue- Human health interventions- Emergency water and sanitation- Supplementary feeding of vulnerable groups

Normal

Emergency

Alert alarm

Recovery

Monitoring and

assessment

information

Figure 2.5: GI in the disaster management cycle (adapted from Cordaid, 2007).


Until recent years, prominent crowdsourced mapping initiatives for disaster

management were in relation to disaster response rather than the other phases.

(Mapping for disaster preparedness is considered in more detail in sections 2.7 and

2.8.)

2.5.1. Mapping agencies in disaster managementThere are many organisations involved in mapping for disaster management. Selected

ones are highlighted here as they feature prominently in the rest of the thesis.

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MapActionMapAction (www.mapaction.org) is a registered charity that claims to be the only

NGO with a capacity to deploy a fully trained and equipped humanitarian mapping

team anywhere in the world within just hours (MapAction, 2011a). Historically its

main emphasis has been responding to sudden-onset disasters. It has teams of trained

mapping professionals who can survey a disaster zone and produce maps rapidly. A

team deployed to a disaster zone will survey the affected area, using hand-held GPS

devices if necessary, and combine their findings with existing GI and satellite images to

produce up-to-date maps.

When the team arrives in a disaster zone, its initial job is to produce a small-scale map

showing the extent and severity of the problem, be it earthquake damage, flooding or

storm damage. Over the following days more detailed and specialised maps will be

produced. “At the height of a relief effort, there will be hundreds of agencies

clamouring for different maps. Often, they will give us a very specific brief and they

need a finished map in as little as an hour. We do our best to oblige.” (Nigel Woof,

reported in Savage, 2008.)

The ready availability of cheap GPS devices means that relief workers from other

NGOs can report specific problem areas, new settlements or damaged infrastructure to

MapAction with the exact co-ordinates (Savage, 2008). Typically MapAction will

remain on the scene for a week or two until another organisation, such as the UN

OCHA, can take over.

In recent years MapAction has undertaken capacity-building work in the use of GIS

and GPS in areas that are prone to disasters (MapAction, 2011a). Capacity building is

important, especially where the GI produced needs to be kept up to date in the long

term. Several of the case studies that are documented in Chapter 6 include capacity-

building components, and they demonstrate how difficult it is for capacity building to

be effective in the long term.

Humanitarian OpenStreetMap Team (HOT)HOT (hot.openstreetmap.org) is an intermediary between the OSM community and

humanitarian organisations. HOT has worked remotely and physically in countries to

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assist in recording GI, disseminating GI and training users. Initially HOT’s emphasis

was on post-disaster mapping, but gradually it has expanded its work to include disaster

preparedness mapping (HOT, 2011) and political crises (HOT, 2013). Like MapAction,

they too undertake capacity building work in the use of GIS and GPS in disaster-prone

areas (HOT, 2011).

International Federation of Red Cross and Red Crescent Societies (IFRC)Usually abbreviated as the IFRC (www.ifrc.org), this Federation was founded in 1919,

has grown to 189 national member societies, and can draw upon some 17 million

volunteers (IFRC, 2014). According to its web site (ibid), the IFRC’s vision is, “To

inspire, encourage, facilitate and promote at all times all forms of humanitarian

activities by National Societies, with a view to preventing and alleviating human

suffering, and thereby contributing to the maintenance and promotion of human dignity

and peace in the world.”

The IFRC has a special relevance to this thesis since it arranged for the American Red

Cross to support the Ugandan Red Cross in mapping Manafwa, Gulu and Lira areas of

Uganda (American Red Cross, 2013). The Manafwa project helped create a flood early-

warning system for the Manafwa River Basin, a large marshy area to the West of the

Mbale Region. In order to support the warning system, the American Red Cross

mapped water courses (and access routes to the water courses) in the Mbale region; this

cases study is described in section 6.4.5.

The Standby Task Force (SBTF)The SBTF (blog.standbytaskforce.com) is a global network of some 800 volunteers

with skills in mapping and GIS. Like MapAction, it is often called upon by United

Nations organisations to coordinate crowdsource activities. (SBTF, n.d.)

UN and EU Global Disaster Alert and Coordination System (GDACS) The UN and European Commission coordinate GDACS (www.gdacs.org), which

facilitates information exchange among disaster response agencies to support decision-

making and coordination. The system permits the exchange of ideas, experience and

skills of disaster managers worldwide and brings together information from various

disaster information systems.

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GDACS provides alerts and impact estimations after major disasters through a multi-

hazard disaster impact assessment service managed by the European Commission Joint

Research Centre (JRC). For example, information about flood disasters are provided by

the Dartmouth Flood Observatory. Detailed weather forecast are provided rapidly on

demand by SARWeather. Disaster maps and satellite images are provided by

UNOSAT, though GDACS has a rapid mapping team to coordinate this information.

GDACS also develops standards and guidelines for international information exchange

in disasters.

The United Nations is developing its fast mapping capabilities through UN GDACS,

but because response times can be rather slow, the UN and other agencies tend to rely

on the British NGO, MapAction, in sudden-onset disasters.

2.5.2. Critical assessment of professional disaster management mapping

In a sudden-onset disaster, the key period for saving lives is the first 48 hours. Disaster

response organisations need GI immediately and therefore need to know exactly where

to find it. High-quality maps that are held privately or are difficult to find are less

useful than a map that is openly available and easy to find.

DiscoverabilityDisaster response organisations need to be able to find GI quickly. Sources of global GI

such as ESRI, NAVTEQ, OSM, Google Maps and Google Map Maker are well known.

For example, in a survey among disaster experts about information technologies

(Limbu, 2012) 64% of respondents said they used Google Map Maker, and 50% said

they used OSM. While there may be privately-produced GI of a given disaster area that

is of higher quality, if the response organisations do not know about it and/or cannot

find it, then it will not be used.

Availability and sharingEven where there is an up-to-date, high resolution map, it may not be accessible after a

disaster. This was the case in 2010 when the offices of the Haitian mapping agency,

CNIGS, were destroyed by the earthquake (Richardson, 2010). The report Disaster

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relief 2.0 about information sharing in emergencies (Harvard Humanitarian Initiative,

2010: p16) said that humanitarian field staff arriving in Haiti to begin the relief effort

thought that, after decades of UN involvement in Haiti, they would sufficient GI to

support the disaster response effort. The report said that the staff expected to be able to

access GI about health facilities, demographics, and infrastructure, and the locations

and types of development programs and projects underway. The report continued (ibid:

p16):

Instead, in most cases the responders found both these data and their curators

tragically absent, or simply impossible to reach (notably, a few who remained

put in incredible effort). In the face of one of the largest humanitarian

catastrophes on record, relief workers struggled to access even the most basic

data sets; they would have to start virtually from scratch.

If information is power, then one can expect some people to be reluctant to share

privately owned GI. Facilitators of a survey in Uganda recognised people sometimes

hoarded information for their own advantage, or out of laziness or shyness (Beardon,

2004). Eria (2012) conducted a review of GI in Ugandan public bodies and found that

they were unwilling to share GI for free. Furthermore, to avoid paying a fee, public

bodies gathered their own GI despite the duplication of effort (ibid: pp69, 202). In

Verjee’s (2007) survey of 40 disaster management experts, 15 volunteered that data

sharing is limited due to a lack of a convenient sharing mechanism. 10 respondents said

that data sharing is limited due to concerns about autonomy, power, lack of reciprocity,

and security. Conversely, some more modern maps and GI sources, such as OSM and

Google Map Maker, are made available via the Internet from secure servers.

Intellectual property rights and licensingThe IPRs of professional maps are usually tightly controlled; licences to use digital

maps and other GI are expensive. This is understandable since gathering GI and

creating maps professionally is time consuming and expensive. Furthermore, there may

be national restrictions on dissemination of GI. For example, aerial imagers from the

US Government might be restricted “for official use only, no foreigners” (John

Crowley, quoted in Teeuw et al., 2012). For disaster management, especially in poorer

countries, the expense may be a barrier to effective planning and response. Many IPR

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owners have made their digital maps and GI freely available during disaster response,

though sometimes a delay in formalising the release can mean the information does not

reach the right people in time. It is also important to note that post-disaster availability

does not help during pre-disaster phases such as disaster preparedness.

Some more modern maps and GI sources have Creative Commons or Open Data

Commons licences which freely allow a wide variety of uses (though usually restricted

to non-commercial uses). OSM and Wikimapia are both being developed by members

of the public, and the GI is available under Creative Commons licences (OSM, 2014;

Wikimapia, 2012). Therefore these sources are more useful for disaster preparedness

initiatives, such as the Manafwa River Basin flood early warning system in Uganda

(American Red Cross, 2013).

Economic and financial obstaclesIn an ideal world all countries would have sufficient resources to prepare for disasters,

and there would be intrinsic motivation to produce digital maps and keep them up to

date. In developing nations, where resources are already over-stretched, preparing for

events that might or might not happen sometime in the future receive lower priority

than daily survival problems (Mitchell, 2006; Auf Der Heide, 1989: p11; IFRC, 2007:

p15).

Conclusion from critical assessment of professional disaster management mappingSo far this chapter has set out the GI needs of disaster management, particularly in

developing countries, and introduced the concept of crowdsourcing. Mapping for

disaster management has a range of needs, and meeting them can be too difficult and

costly for governments in developing countries. Professional mapping approaches for

disaster management have a range of problems. Crowdsourced mapping and web-based

servers can provide alternative ways of meeting those needs and overcoming those

problems.

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2.6. Crowdsourced mapping for disaster management

Modern technologies allow new ways to produce and update maps for disaster

management. Crowdsourced mapping systems that use open or Creative Commons

licences provide an economic means for GI to be stored, checked, and shared; because

they are used by the public, crowdsource systems are well known and easily located in

the event of a disaster. Although such systems can support disaster management in

many ways, two are of particular relevance to this project: (1) before a disaster as part

of disaster preparedness; and (2) immediately after a disaster to help with disaster

response. The latter has had quite high visibility in recent years. There have been two

main types of crowdsource systems relevant to disaster response:

report coordination, where those affected report their problems and needs using

systems such as Ushahidi (www.ushahidi.com); and

crowdsourced ‘crisis mapping’, where volunteers from around the world update

and add detail to maps of the affected area using public mapping systems such

as Google Map Maker and OSM (see section 3.2.3 for more detail).

Google Map Maker and OSM are completely open to any volunteers regardless of

qualifications and experience, whereas other systems, such as Global Earth Observation

– Catastrophe Assessment Network (GEO-CAN), restrict updates to teams of trained

volunteers.

2.6.1. Historical perspectiveIndonesia, March 2005An early recorded example of volunteered post-disaster mapping happened a few

months after the Asian Tsunami of December 2004 (GISCorps, 2005). A team of five

volunteers worked with the UN Joint Logistics Centre and NGO BakoSurtanal to

record GI and to draw existing GI together into a single geo-database.

Another trained volunteer, Frank Chang, advised the NGO Global MapAid during its

evaluation of needs in Indonesia (ibid). At the time many disaster response

organisations had their own independent mapping teams, but Global MapAid was 76


among only a few that had specialists in GIS and mapping. Chang identified that there

were many gaps in coverage but at the same time effort was being duplicated

unnecessarily elsewhere.

USA, July 2008A precursor of crisis mapping by the general public was in response to a large wild-fire

in Santa Barbara, Southern California. Named the ‘Gap Fire’, it burned for seven days

during which time local citizens provided updates for homes under threat using online

services such as Flickr (www.flickr.com) (Goodchild and Glennon, 2010).

USA, August 2009Crisis mapping took another small step forward in response to wildfires in the Santa

Cruz Mountains, California. Google users recorded the locations of fire reports and

photographs using Google’s My Maps application (www.google.com/mymaps)

(Martin, 2009).

Philippines, September 2009The first documented example of the general public contributing post crisis to OSM

was in response to Tropical Storm Ketsana, which struck the Philippines on 26

September 2009. OSM encouraged users to improve the geographic coverage of the

Philippines using high resolution QuickBird satellite imagery that was donated by

DigitalGlobe (Sambale, 2009).

Haiti, January 2010The first crisis mapping exercise that received significant press coverage was in

January 2010. An earthquake measuring 7.0 struck Haiti (DEC, 2010). Although Haiti

had been mapped reasonably well by its national mapping agency, CNIGS, the building

where the map data were stored was destroyed by the earthquake (Richardson, 2010).

Volunteers – including expatriates – helped to develop and update OSM and Google

Map Maker maps based on aerial imagery and local knowledge. Search-and-rescue

teams on the ground were able to download the new GI and install it onto their GPS

devices to help them navigate (OSM, 2010).

A report by the Harvard Humanitarian Initiative (2010: p11) said:

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… the 2010 Haiti earthquake response will be remembered as the moment when

the level of access to mobile and online communication enabled a kind of

collective intelligence to emerge – when thousands of citizens around the world

collaborated in volunteer and technical communities [...] to help make sense of

a large-scale calamity and give voice to an affected population.

2.6.2. Critical assessment of crowdsourced mapping in disaster management

Crucial attributes of a digital map are its value, availability, content (features shown)

and accuracy.

ValueThe value of crowdsourced crisis mapping is widely acknowledged. For example, a

member of the Fairfax County Urban Search and Rescue Team from Virginia wrote on

the OSM wiki page (OSM, 2010):

I wish there was a way that I can express to you properly how important your

OSM files were to us. Most of our team members own their own Garmin Rino

and 60CSx units on top of the units we already have in the cache. Having these

detailed maps on our GPS units is a big deal. Shortly after discovering your

work I quickly spread the word and transferred the street level maps onto as

many Garmin units as we could before sending the American rescue teams on

the streets. The team members are thrilled to have this resource you have

created. I wish you could see their faces 'light up' when I take their GPS unit

and tell them that I'm going to give them street level detail maps.

Crowdsourced GI does have to be managed, though. After an aviator Steve Fosset

disappeared over Nevada, USA, on 3 September 2007, the public were asked to help

examine the latest aerial imagery to identify possible crash sites. The search leader,

Civil Air Patrol Major Cynthia Ryan, is quoted by Friess (2007: p3) when explaining

how her e-mail and voicemail inboxes were flooded with false leads:

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The crowdsourcing thing added a level of complexity that we didn’t need,

because 99.9999 percent of the people who were doing it didn’t have the

faintest idea what they’re looking for.

In the early days, it sounded like a good idea. In hindsight, I wish it hadn’t been

there, because it didn’t produce a darn thing that was productive except for

being a giant black hole for energy, time and resources. There may come a day

when this technology is capable of doing what it says it can deliver, but boy,

that’s not now.

In general, the Harvard report and others indicate that the crowdsourced information

was beneficial for the humanitarian response to the 2010 Haitian earthquake. However,

reconciling data in a crisis situation was very difficult. For example, UN OCHA needed

an up to date list of hospitals and clinics, but no agency had a definitive list. Some

available lists showed the same facility three times, one in French, another in Creole

and a third in English. Some facilities appeared several times, such as a doctor’s office

that was inside a hospital (ibid: p29). Conversely, UN OCHA knew of 105 health

facilities that they could no geo-locate. Within 35 hours, HOT had found all but three;

an open source system known as Sahana (www.sahanafoundation.org) was used to

make the resource available in a variety of formats. “This resource became one of the

best resources for health facility data for the next month.” (ibid: p29).

Many reports – the Harvard Humanitarian Initiative (ibid) provides a good summary –

reveal, though, that a major problem during disaster response is the inability of

responders to cope with the quantity of fresh data. If GI can be created in advance of a

disaster, that would help reduce the quantity of fresh GI during disaster response (such

as the locations of health facilities)

Availability (including intellectual property rights and licensing)Disaster response is critical during the first 48 hours of a sudden-onset disaster; this is

sometimes called the ‘golden period’. Since crowdsourced crisis maps are not available

for many hours or days, and not until after the response teams have left their bases, the

teams may not be able to access updated maps until days or weeks later.

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Bandwidth in a disaster area is scarce. During the initial response to the earthquake in

Haiti in 2010, local communication infrastructure was damaged. Many relief workers

carried Broadband Global Area Network (BGAN) satellite phones but the system was

unable to cope with their data demands (Erle, 2010). At the most critical times, the

relief teams had only the GI that they took with them from their home airports. After a

few days Inmarsat re-tasked some satellite antennas to the area, which allowed at least

a slow data connection. Downloading GI over busy satellite phones was very time

consuming – see Box 2.1.

It's tough for those of us blessed with a wealth of Internet access to imagine what this is like. While visiting the MapAction volunteers at Logbase in Port-au-Prince, I volunteered to download them the latest OSM updates over their satellite link.

“See, it's only 14 megabytes,” I said.

“14 megabytes??” I was greeted with incredulous horror. “Dear Lord, don't download that!”(Erle, 2010)

Field staff found the method of exchanging files via web portals to be flawed. They often could not afford to download large files, particularly those which involved datasets, imagery, and new applications. Instead, low bandwidth and unreliable access to the public Internet left staff relying on paper and data formats that could work offline. Portals also tended to provide file dumps instead of a common picture or dashboard, requiring staff to piece together a situational picture by painstakingly reading through lists of documents and blog posts; few had time or adequate network bandwidth for this activity. Many had no means to exchange data with other web sites or services.(Harvard Humanitarian Initiative, 2010: p23)

Box 2.1 Examples of data download problems.

Another aspect of availability is whether there are delays in obtaining permission to use

the GI. If the GI has a proprietary licence, negotiating permission can take time, and

even then there may be restrictions on use and reuse. Creative Commons and Open

Data Commons maps can be used instantly since humanitarian use is almost always

expressly permitted. This flexibility is of benefit to disaster response organisations.

Google Map Maker tries to bridge the crowdsourced and commercial worlds. Like

OSM and Wikimapia, it sources GI from the public. However, the GI becomes the

property of Google. Eventually GI migrates from Google Map Maker to the main

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commercial Google Map. Google’s licences have quite generous clauses and

permissions, but the GI remains under the control of Google (Google, 2012a; Google,

2012b). Specialist disaster response organisations – such as MapAction and the Global

Disaster Alert and Coordination System (GDACS) – have prior agreements to use

Google’s GI for disaster response, but this concession is not given to all.

Twigg (2004: p126) suggests that disaster management work that draws on the

knowledge of local inhabitants has a greater likelihood of success if the outcomes are

open and accessible. This implies sharing information and knowledge. The Map Kibera

Project in Nairobi has also considered this matter. Among the Frequently Asked

Questions, the issue of open data is raised (Hagen, 2012):

Q. Does the community need to own the information collected during the

community mapping? Does it have to result in open data?

A. They don’t need to own it, but [Map Kibera does] believe in free and open

data as a critical part of community mapping. After all, the point is not to help

companies build their commercial base, or to hand over more information to

proprietary silos inside NGOs and governments, never to be seen again. The

idea is to create a commons of information that can lead to greater transparency

from the local level on up, and allow many people to leverage that information.

In January 2012, the World Bank announced an agreement that Google would provide

it and partner organisations access to Google Map Maker, including the underlying

geospatial data (2012). The January announcement encouraged citizens to update

Google Map Maker (World Bank, 2012):

One way to collect this information is to ask citizens directly, and crowdsource

the locations of public infrastructure…

and

Through this tool, citizens are able to directly participate in the creation of maps

by contributing their local knowledge, and those additions are then reflected on

Google Maps and Google Earth.

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After much criticism from those with an interest in disaster response, especially NGOs,

the World Bank back-tracked on this collaboration. In March that year, the Bank’s

Director of Innovation and Change Management clarified (Ryterman, 2012) that the

World Bank wants organisations to use systems that give users free and open access to

the map data they create, but was happy to use existing map information from Google

Map Maker. Ryterman explained that the World Bank’s goal of collaborating with

Google Map Maker was to provide UN agencies and governments faster access to Map

Maker map information for humanitarian, development and disaster preparedness

efforts. She concluded her announcement by saying that the World Bank will work

with a variety of other mapping platforms, in particular, to be consistent with the

Bank’s guiding principles around open data. Subsequent publications from The World

Bank are clear that it (now) prefers to use open data systems such as OSM (e.g. Haklay

et al., 2014).

AccuracyThere seems to be a difference of opinion about the accuracy of crowdsourced GI.

Broadly speaking, GI generated in response to a disaster has proven less trustworthy

than GI generated at other times. It is worth distinguishing between the accuracy of

crowdsourced GI generated normally (not in a disaster situation), and accuracy during

disaster response.

Accuracy of GI generated during disaster response

There is a consensus that crowdsourced data generated during disaster response may be

problematic. In an interview in July 2010, the then Chief Executive of MapAction

pointed out that the GI produced by crowdsourced crisis mappers after the Haiti

earthquake was “often unverified, contradictory and vague” (see section 6.1). He

conceded that using Haitian diaspora to verify the GI helped to reduce the problems,

but he remained of the view that post-disaster crowdsourcing was not the whole

solution to crisis mapping.

Urban search-and-rescue teams found that sometimes less than a third of crowdsourced

reports turned out to be accurate (Harvard Humanitarian Initiative, 2010: p48). “The

tendency of citizens to exaggerate under extreme stress should not be underestimated.”

It is possible that inaccurate information might be provided deliberately out of

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mischief, a personal agenda, or personal gain. There has even been at least one example

of a group providing misinformation with the intention of hijacking a convoy

transporting relief goods (Coleman et al., 2009: p345).

Incidence of accidental errors seems to vary by type of feature and source. For

example, Foody et al. (2013) conducted an experiment among volunteers to establish

their ability to classify land cover from aerial images. Seven (out of the 65) volunteers

completed the whole experiment. All seven volunteers were able to accurately classify

urban areas and open water, but their classification of tree cover, shrub cover,

herbaceous vegetation / grassland, cultivated and managed land and mixed (‘mosaic’)

could be much less accurate, even as low as 14% by one volunteer for cultivated land.

Even the most consistently accurate volunteer had accuracy rates between only 63%

and 74% for those latter types of cover.

Kerle and Hoffman (2013) believe that – for certain types of crisis mapping – the

problems of poor reliability warrant the creation of a team of experts. The Global Earth

Observation – Catastrophe Assessment Network (GEO-CAN) is specifically tasked to

assess from aerial imagery the damage caused by earthquakes. This is a specialised task

and assessments have to conform to the European Macroseismic Scale standard. Rapid

assessments by some 600 to 700 experts are validated and stored on a secure server.

Accuracy of GI generated normally

A key premise of this thesis is that – where possible – it would be better to record GI

and update maps before a disaster rather than afterwards.

Ather (2009), Haklay (2010), Helbich et al. (2012), Wang et al. (2013) and others have

compared the positional accuracy of OSM data with other datasets in the UK, Germany

and China. The consensus is that OSM data is reasonably accurate in well-mapped

areas, especially city centres. Positional discrepancies are more apparent in rural areas,

where roads and junctions are further apart and there is less validation and errors are

corrected less quickly. It seems, then, that reasonable positional accuracy is achievable

from crowdsourced GI under normal conditions, that is, when developed over time

rather than in response to a disaster.

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The above-mentioned surveys focussed on positional accuracy, but accuracy of each

feature’s tags is also important. There are some problems with OSM’s tags: (1) The

choice of valid tags changes from time to time, though the core ones have remained

unchanged for many years. This volatility means that editing and rendering software do

not consistently show symbols for features; indeed the main OSM website fails to

render some valid features at all. Often one has to go into the editor to see all the

features and tags in a given area. (2) Only the most commonly-used tags are supported

by the main editor; a contributor has to look up a list of valid tags for features that are

less common, such as a ‘Coworking space’. The list is quite long and it can be difficult

to find the relevant ones. (3) Those less common tags have to be entered as free text.

The editor accepts them unchallenged, which means features may have misspelled and

non-standard tags (Mooney et al., 2011).

Google Map Maker has a more helpful editor that retains control over tags.

Furthermore, edits go through a moderation process. Edits by new users are subject to

moderation by more experienced users. Once the user has demonstrated ability and

accuracy in a certain type of edit, the system learns to trust the user for that type of edit.

OSM does not have a moderation process, but users can review the map and make

corrections. For example, Figure 2.5, Figure 2.6 and Figure 2.7 show various errors that

have been spotted and corrected.

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Figure 2.5: Example of buildings entered twice into the system (should appear as pale pink) © OpenStreetMap contributors

Figure 2.6: A road recorded once as a road (grey vertical line) and twice as a track (brown dots) © OpenStreetMap contributors

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Figure 2.7: Multiple errors showing tracks misrecorded, duplicate tracks etc. © OpenStreetMap contributors

These errors can be corrected because the GI is being collected without undue time

pressures.

In general, reservations about accuracy of crowdsourced GI may be misplaced. Disaster

mapping agencies, such as Humanitarian OSM Team (Erle, 2010) and MapAction

(2011b), generally consider that any GI is better than no GI, and that minor

inaccuracies are seldom a problem. His experience in Haiti immediately after the 2010

earthquake was that crowdsourced GI did indeed have errors, but urged that agencies

shouldn’t “throw the proverbial baby out with the bath water”. Crowdsourced

information can never be a replacement for traditional humanitarian relief intelligence,

but he thought it could be used responsibly to build an accurate picture of the situation.

Other potential problemsThere are a number of other possible challenges that arise from post-disaster crisis

mapping:

GI is collected under intense time pressures;

GI may be recorded by people who are unfamiliar with the disaster area;

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GI may be recorded wrongly by well-meaning volunteers because they have no

local knowledge;

GI is collected remotely using only satellite images;

GI is unverified by any ground truthing; there isn’t time for it and local workers

have other priorities;

GI might be manipulated falsely for individual gain.

Conclusion from critical assessment of crowdsourced mapping in disaster managementCrowdsourced crisis mapping has produced many benefits for disaster response

initiatives. However we mustn’t become complacent. While crowdsourced crisis maps

may be part of the solution, it is apparent that this is not the whole solution. The

challenge is to devise a method whereby GI is recorded in advance of a disaster, is

accurate and verified, is maintained up to date in the long term, is resilient to disasters

itself, and is available from the moment the relief operation first swings into action.

This thesis proposes that mapping in advance of a disaster – i.e. mapping for disaster

preparedness – is a way to do just that.

The next two sections examine the specific mapping needs of disaster management and

how crowdsourcing can be tailored to mapping for disaster preparedness.

2.7. Mapping for disaster preparedness

2.7.1. ContextIf disaster response organisations are to have access to relevant GI during the 48-hour

‘golden period’ of a disaster, the GI needs to be prepared in advance, be easily

discoverable and be available. In December 2004, many response organisations relied

on GI when responding to a large tsunami in the Indian Ocean that devastated several

countries. ESRI later reviewed the use of GI and concluded that a database of GI

suitable for disaster response should be made available in advance of disasters (ESRI,

2006: p26).

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If vulnerable areas were mapped before a disaster, as part of disaster preparedness for

example, then we gain a number of advantages. First, the GI could be checked free

from post-disaster pressures. Second, the base map would be available to disaster

response organisations immediately after a disaster. Third, in the event of a disaster,

mapping effort could be focussed on recording the extent of damage and Who-What-

Where information, and not be dissipated on recording baseline information.

2.7.2. Critical assessment of professional mapping for disaster preparedness

GI may be stored in various locations, in different data formats, and use different data

models, coordinate reference models and geodetic systems. Locating relevant GI,

loading it into suitable software, merging it with GI from other sources and so on is a

technical process and may require a lot of time and expense.

Pulling together GI from a variety of sources and creating a coherent database can be

costly, even if the base data is freely available and there is no collection of primary

data. Burrough and Masser (1998) recount the experience of two projects in the Baltic

Basin. Even though the contributors were already in possession of some data and other

data were available free of charge, it was still necessary in a number of other cases to

take steps to resolve copyright issues relating to them. There were no rules of conduct

or legal practices for the redistribution of multinational GI datasets originating from

multiple sources. It was also found that the cost of data conversion to a single data

model was often substantial and that this was often compounded by the lack of

metadata to describe the datasets.

Mansourian said (2006: p303):

This is a very important aspect to disaster response as timely, up to date and

accurate spatial data describing the current situation is paramount to

successfully responding to an emergency. This includes information about

available resources, access to roads and damaged areas, required resources and

required disaster response operations that should be available and accessible for

use in a short period of time. Any problem or delay in data collection, access,

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usage and dissemination has negative impacts on the quality of decision-making

and hence the quality of disaster response.

2.8. Crowdsourced mapping for disaster preparedness

2.8.1. PressuresIn an ideal world, all potential disaster areas would be mapped in detail; at the time of

writing, this is not the case. When one considers the coastal plains potentially at risk

from tsunamis, the mountainous areas being deforested and so on, the task is so large

that it cannot be carried out solely by experts and outsiders. A logical solution would be

to engage the local communities to map their own areas. Some work is being done on

identifying areas at greatest risk and building capacity within the communities to

survey and map their areas.

Within Uganda, The National Disaster Preparedness and Management Policy says

(OPM, 2008: p22):

The success of any disaster preparedness plan will depend on the level of

participation, ownership and trust by the community in that plan. Communities

will be involved in vulnerability and capacity assessments. Communities will

also participate in trend monitoring using local knowledge and experience

gained over the years. This will enable them keep records of disaster occurrence

overtime. They will communicate the information to the relevant local authority

for onward transmission. In the event of an impending disaster, communities

will play a pivotal role in executing and responding to evacuation advisories,

hosting and supporting displaced families through community solidarity.

Communities will also play a key role in supporting clean-up operations after a

disaster for public safety and health.

Communities are responsible for taking measures within their own capacities, to

protect their own livelihoods and property. However, it is expected that

measures taken by individual families and communities will be part of an

integrated approach which will include the development of family management 89


capacities and a reduction in their vulnerability over time. The national disaster

management programme is principally aimed at the reinforcement of

community capacity to withstand disaster threats and occurrences.

Based on his experiences in disaster risk reduction projects, Twigg (2004: p114)

suggests that participatory approaches are valuable in disaster preparedness for various

reasons:

They enable people to explain what they see as their vulnerabilities and to

define their priorities.

Participants are often instrumental in disaster mitigation and disaster response.

Twigg claims participatory approaches are well placed for dealing with the

complexity of disasters and the diversity of vulnerability factors.

Working together reinforces local organisation, builds up confidence, skills,

capacity to cooperate, awareness and critical appraisal, and consequently is

empowering.

Participation in planning and implementing projects accords with people’s right

to participate in decisions that affect their lives.

He also suggests the approaches are more cost effective, in the long term, than

externally-driven projects, partly because they are more sustainable.

Working closely with local communities can help professionals to gain a greater

insight into the communities they seek to serve, enabling them to work more

effectively and produce better results.

There are many published examples of public participation in disaster preparedness in

general. Until recently the technology to allow untrained community members to

record the geography of their areas didn’t exist. The first published article specifically

about crowdsourced mapping for disaster preparedness was in 2010.

2.8.2. Historical perspectiveUganda, 2009 - 2013The first documented use of crowdsourced mapping for disaster preparedness was in

the Mbale region, Uganda, mostly between 2009 and 2013 (Farthing and Ware, 2010;

2011; 2013). The author has been involved in various projects in conjunction with the 90


University of Glamorgan, University of South Wales, Welsh Government, United

Nations and the Red Cross that contributed to OSM coverage of the Mbale region. The

focus has been on recording roads, bridges, water courses, WASH resources, health

care facilities, educational institutions and conurbations. A variety of case studies are

discussed in detail in Chapter 6, and they demonstrate the variety of backgrounds of

contributors in the Mbale region:

Professional – but non-expert – employees of government and non-government

organisations who needed to create and use GI as part of their work in the

Mbale region, for example:

o A Landuse Planning Officer and Manafwa District Local Government,

who mapped a crack in the ground that was predicted to cause a

landslide.

o Employees of the American Red Cross who mapped watercourses as

part of a flood early warning project.

Professional employees who created GI tangentially to their work:

o An employee of Kissito Healthcare International in the Mbale region,

who mapped the locations of health centres and roads to them.

o A lecturer at the University of South Wales, who mapped the locations

of schools and other organisations supported by Partnerships Overseas

Networking Trust (explained in section 5.6.3; see page 26) and routes to

them.

Members of the public who lived in or visited the region:

o A student from Germany and a volunteer for the Humanitarian

OpenStreetMap Team, who mapped roads and power lines in the Mbale

region as a hobby.

o An enthusiast who mapped – in his spare time – places and roads that he

knew from a visit to Mbale in 2012.

Indonesia, 2011 - 2013During 2011-2013 the Humanitarian OSM Team (HOT) undertook disaster

preparedness mapping in Jakarta, Indonesia (Chapman, 2013; Narvaez, 2012).

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In Phase 1, the team trained local university students in the use of GPS devices. They

developed a base map on OSM of Padang, Jakarta, Surabaya, Yogyakarta, and

Bandung, with a special emphasis on disaster-related hazards to infrastructure. In Phase

II HOT enabled communities and government bodies to collect risk exposure

information to be used in impact modelling software (Chapman, 2013).

World Bank, 2012 onwardsIn January 2012, the World Bank announced a crowdsourced mapping initiative called

‘The Mapping for Results Initiative’. It had a number of objectives that included

disaster preparedness (World Bank, 2012). By combining the locations of all social

infrastructures, development partners would be better able to track improvements in

local public services and disaster preparedness in developing countries.

Philippines, 2013 - 2014The Philippine Department of Interior and Local Government engaged HOT to support

local government in mapping for disaster risk reduction. 85 staff from three local

government authorities were trained in the use of OSM and how to use GI for hazard

analysis. Unfortunately, only one of those local government authorities continued

mapping after the initial training (Hacklay et al., 2014: p38).

Kathmandu Valley, Nepal, 2013 - 2014The Open Cities project (opencitiesproject.com) aims to empower resilient

communities and is supported by the Global Facility for Disaster Reduction and

Recovery, The World Bank, HOT, Development Seed and US Aid. The team trained

1500 people in mapping. Some of those trainees attended a mapping party in

February 2013 to map locations of educational and health facilities in Kathmandu,

Nepal. Over time some 100,000 features and 2900 kilometres of roads have been

mapped (Open Cities, 2013)

2.8.3. Critical assessment of crowdsourced mapping for disaster preparedness

Karikari et al. (2005) briefly described strategies for improving the appropriateness of

GIS in developing countries. These included local participation, the development of the

public domain, the relaxation of copyrights over existing GIS software and the

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development of software that takes advantage of existing infrastructures in these

countries. They added that the crucial issue was that of capturing GI and cost-effective

ways of keeping GI up to date. Crowdsourced mapping for disaster preparedness

addresses the components of Karikari et al.’s strategies as follows. Crowdsourcing can

be from members of the local communities. By using a Creative Commons or Open

Data Commons crowdsourced online map – such as OSM – the GI is in the public

domain. If GI is stored using open standards, as OSM does, open source GIS software

can be utilised. Finally, Internet-based systems can obviate (part of) the need for an

SDI. In Table 2.4 crowdsourced crisis mapping (post disaster) is compared with

mapping for disaster preparedness.

Characteristic Crowdsourced crisis mapping

Professional mapping for disaster preparedness

Crowdsourced mapping for disaster preparedness

Is GI available at the commencement of a sudden-onset disaster?

No Yes Yes

Can GI be collated at base before deployment?

No Yes Yes

Was GI prepared without undue time pressures?

No Yes Yes

Was GI recorded by people familiar with the area?

Unlikely Varies Yes

Can GI be collected using several sources?

Mainly from satellite images

Yes Yes

Has GI been verified/ground truthed?

No Yes Possibly

Can feature names be translated into multiple languages?

No Yes Yes

Post disaster, can GI efforts focus mainly on damage assessment?

No Yes Yes

Might GI be manipulated falsely for individual gain?

Possible Unlikely Possible

Do we know where the disaster is located and therefore what needs to be mapped?

Yes No No

Table 2.4: Comparison of crowdsourced crisis mapping with disaster preparedness mapping

A key problem with mapping for disaster preparedness is how to motivate local

communities to participate in mapping. In September 2010, the author and his director

of studies presented a paper at AGI Geocommunity '10 conference proposing that local

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communities might record GI to help with their daily responsibilities, and that – if

recorded in an open-access source – it would be available for disaster management

purposes (Farthing and Ware, 2010). In practice, motivating the crowd has proved

difficult.

2.9. Chapter summaryThis chapter has explained the need for GI in different stages of disaster management,

with a special focus on developing countries. The chapter also explained the problems

with mapping developing countries, how crowdsourcing can relieve the problems. The

chapter then gradually focussed in on mapping for disaster management and how

crowdsourcing can be tailored to meet its needs, and finally on disaster preparedness

and how crowdsourcing can be tailored to meet its specific needs. By mapping in

advance of a disaster, GI will be available to disaster response organisations as soon as

they are mobilised. The next chapter evaluates a range of technologies that can be used

to support disaster mapping. Some have been in existence for many decades and others

– especially those relevant to crowdsourcing – are more recent developments.

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3. Technologies for Humanitarian Mapping in Developing Countries

In the pre-disaster context we would like to map vulnerability and develop

baselines for it. The data for these baselines would include scientific hazard

data and the outputs from qualitative assessments at community level.

(Bhupinder Tomar, Senior Officer Disaster Preparedness, International

Federation of Red Cross and Red Crescent Societies, quoted in MapAction,

2011b: p2)

3.1. ContextThis chapter evaluates some of the wide range of technologies that can be used to map

developing countries for disaster preparedness purposes. As concluded in section 2.8.3,

local people might record GI to help with their daily responsibilities, and – if recorded

in an open-access source – the GI would be available for disaster management

purposes. Unlike post-disaster mapping, there is unlikely to be as much media attention

surrounding the creation of such data sets. Furthermore, unlike mapping in

industrialised countries, funding and skills will not be available at the same levels. This

means that local people – non-experts – need to be encouraged to map their own

localities and to share their GI on a publicly-available system. In order for this to be

practical, mapping technologies must have the following attributes:

Fit for purpose. Technologies must perform appropriate tasks and provide GI

relevant to disaster preparedness. This GI includes topographic features and

information for thematic maps. Hardware must be durable in extreme climates,

and when travelling along rough, dusty roads.

Low price. In developing countries government organisations, NGOs and

members of the public do not have much spare funding for mapping

technologies. After a major disaster, potentially large amounts of foreign aid

become available, but disaster preparedness activities generally have less

widespread public support.

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Ease of use. Each contributor is unlikely to use mapping technologies

frequently, there may be little support nearby to help with complex tasks, and

technologies that are difficult to use can demotivate contributors.

Shareability of GI. The GI will be produced by a variety of people and used by

a variety of organisations, perhaps at short notice in the event of a subsequent

disaster.

Although this thesis emphasises the usefulness of GI and GIS, some notes of caution

should be sounded about considering technologies alone to be the solution for disaster

management. For example, Zerger and Ingle Smith (2003) warned that research over

the years has over-emphasised solving technical impediments to the use of GIS for

disaster management and ignored other issues such as custodianship and system

implementation. It has long been recognised (e.g. Salter, 1995) that the problem with

society treating a disaster as only a physical event that has technological solutions, is

that it limits the number of intervention options available.

In 1995, Campbell and Masser (1995) published a study on the use of GIS in Britain.

At the time the take-up of GIS in Britain was, in some ways, similar to the take-up of

GIS in Uganda today. In their in-depth study of early adoption of GIS by British local

government, Campbell and Masser made two notes of caution. <Key Point Model >

First, their study revealed that overcoming technical difficulties were not a major

determinant of overall success. Technical difficulties could be overcome if there was a

drive and a motivation to succeed; conversely projects that had relatively few technical

difficulties could fail if they didn’t have that drive or motivation. Second, when

technical problems were cited as the reason for a failed GIS project, it could be difficult

to determine whether technology was the real reason or just a scapegoat. Where such a

project was imposed from above, staff were unwilling to speak out against it but would

instead over emphasise minor technical difficulties. Therefore there seem to be links

between the ease of use of a technology and social factors. Kizito et al. (2009),

reporting on a variety of decision support systems written for Uganda’s National Water

and Sewerage Corporation, warned about the importance of the local users identifying

the initial concept and possible solutions. Describing a system where this did not

happen, Kizito (2009: p132) said:

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It demonstrated the need for decision support systems to be developed from the

bottom up, starting with users’ needs as perceived by them, rather than being

handed down as a finished product by the system developer.

For many contributors, the key technologies will be those used to record locations and

attributes of features. Decisions about those technologies will be affected by the choice

of the underpinning storage and retrieval technologies.

3.2. Storing and retrieving GI3.2.1. RequirementsBefore considering technologies to record GI, it is necessary to consider how the GI

will be stored and retrieved. Item 132 of the Plan of Implementation of the World

Summit on Sustainable Development (UN, 2002: p54), includes inter alia:

(b) Develop information systems that make the sharing of valuable data

possible, including the active exchange of Earth observation data;

For GI to be useful for disaster management, it needs to be discoverable, licensed or

open source, in a usable (standard) format, and sufficiently accurate. A traditional way

of sharing GI among organisations is to establish a spatial data infrastructure.

3.2.2. Spatial Data InfrastructureThe US Federal Geographic Data Committee defines a Spatial Data Infrastructure

(SDI) as, “the technologies, policies, and people necessary to promote sharing of

geospatial data throughout all levels of government, the private and non-profit sectors,

and the academic community” (FGDC, 2007). The Open Geospatial Consortium

(OGC) says SDI is the, “collection of technologies, policies and institutional

arrangements that facilitate the availability of and access to spatial data” (OGC, 2014).

Typically an SDI is national (an NSDI), but there have also been attempts to create a

global SDI (GSDI, 2004).

Although more than half the world’s nations claim to be developing some form of SDI,

many of these may be in very early stages, perhaps even mere statements of intention 97


(Masser, 2005). Successful implementations are few and far between. A study by the

Spatial Applications Division, Catholic University of Leuven (2006) examined to what

extent all the requirements for an SDI are in place in each country in Europe. The

results reveal that many have reasonable organisational support and have assembled the

data/metadata. Few have made much progress with ensuring that full legal support was

in place, and few had secured long-term funding. It would appear that a nation’s claim

that it is developing an SDI does not indicate that one will come into existence in the

foreseeable future.

National SDI in Uganda Makanga and Smit’s 2010 review of NSDIs in Africa revealed that implementation

there was in its infancy in the 29 countries studied. The authors assessed each country

using a variety of criteria, including organisational responsibility, funding, legal

framework, data creation and access, and metadata creation. 20 out of the 29 countries

had a body that was coordinating attempts to come up with a formal NSDI, although

few of them were considered to have adequate political support. Uganda was one of the

few countries that had support directly from the President’s office, but it didn’t score

well under many other criteria. Uganda’s overall score was in the middle ground: 26

out of a possible 53 points. Ethiopia scored 28, Kenya 33, and Rwanda a creditable 43.

Somalia lagged behind the rest of East Africa with only 23 points. No East Africa

countries had any legal framework for their NSDIs.

Note: Even where a country makes progress, one cannot assume an upward trajectory.

A previous survey in 2003 found that two nations had NSDI clearinghouses but by

2008 both of them had ceased to be operational (Makanga and Smit, 2010).

In 2011, GIC/ESRI Canada published a review of the history of SDI in Uganda. A

report in 2001 had made a variety of SDI recommendations for various government

organisations. In 2004 the Uganda Bureau of Statistics made “significant efforts in SDI

development” (GIC/ESRI, 2011: p16) by adopting policies that defined GIS as a key

strategic tool. In 2006 the Rockefeller Foundation funded activities of an informal

organisation to convene a workshop, though there is no record of any substantive

outcomes. The National Interagency Spatial Data Infrastructure Committee was

launched in 2010 to create a framework for SDI development. There is little evidence

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of substantive progress to date, though, and its web server (www.ugsdi.co.ug) is no

longer in operation. Eria (2012: p125) blames the lack of government mandate for the

lack of progress; several government departments in Uganda claimed the right to host

an NSDI but failed to produce substantive results.

Problems with SDIDeveloping an SDI can take many years, and the benefits take even longer to

materialise. It seems that the government of a developing country is often unable to

sustain funding for the development of an NSDI (Masser, 2005 [citing Giff and

Coleman, 2002]). Where one is created, there may be a problem for disaster situations

in that the NSDI might be damaged or disabled by the disaster (as happened in Haiti in

2010), or a government might withhold access to the NSDI for political reasons. In

some disasters, e.g. Pakistan (Butler, 2005), commercial licensing terms have

obstructed the use of available datasets. Masser et al. (2007) suggested that SDIs are

too focussed on a small elite of spatially aware professionals.

In the aftermath of the 2010 Haiti earthquake, because the NSDI was not available,

Neis et al. (2010) attempted to produce routable maps of Haiti from the UN’s SDI,

known as UN-SDI-T. They found that it was difficult to use, and the data was sparse

and inconsistent. They commented (ibid: p1):

The UN tries its best to set up a SDI but because of the general lack of sufficient

accessible data the data of the UN-SDI-T consists of very heterogeneous

sources and therefore has varying quality.

The team found it much more efficient to create maps from OSM. Response

organisations were able to calculate routes using comprehensive and up-to-date GI

drawn from OSM. In their review of SDI in Africa, Makanga and Smit (2010: p24)

concluded:

The work and resources that have been invested in thematic SDIs and other

informal SDI initiatives through NGOs and private mapping companies will

potentially go to waste if there is a poor handover strategy. Although it would

be easier to establish a NSDI after attaining full political support for the

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initiative, getting political attention has proven to be a task that is beyond the

immediate reach of most NSDI agencies throughout Africa. This means that

organisations that are playing key roles in the data creation and coordination

efforts, and have realised the importance of SDI, should make use of existing

structures [...], to make SDI implementation possible with minimal political

support.

Critical assessmentInitially the author considered whether Uganda needed an NSDI for disaster

preparedness, but Makanga and Smit’s 2010 review reveals that the government of

Uganda (and most African governments) are making little progress in setting up an

NSDI, and they provide no reason to believe that will change in the near future.

Modern crowdsourced systems provide many of the same benefits as an SDI. The EU’s

INSPIRE Directive (INSPIRE, n.d.) sets out five justifications for setting up an SDI,

and Table 3.5 compares them with similar benefits conferred by crowdsourced systems.

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Justification for SDI How crowdsource systems satisfy this

1.

Spatial data should be collected only once and kept where it can be maintained most effectively.

This can be achieved using crowdsource systems plus private GI stored locally at each relevant organisation.

2.

It should be possible to combine seamless spatial information from different sources across Europe and share it with many users and applications.

Modern international standards for GI storage and GIS software make it possible to combine GI from various sources, all without the overhead of creating and running and SDI.

3.

It should be possible for information collected at one level/scale to be shared with all levels/scales; detailed for thorough investigations, general for strategic purposes.

Crowdsource systems allow GI to be shared at appropriate resolutions.

4.

GI needed for good governance at all levels should be readily and transparently available.

Crowdsource systems that have a Creative Commons or Open Data Commons licence allow sharing with little or no restriction.

5.

It should be easy to find what GI is available, how it can be used to meet a particular need, and under which conditions it can be acquired and used.

Crowdsource systems do not enable this formally, but the main systems are easily discovered, and users can annotate entries to indicate how accurate the data are.

Table 3.5: Comparison of how crowdsource systems meet needs of SDI (INSPIRE, n.d. - augmented)

It is advantageous for the crowdsourced systems to store GI using open standard

formats. In situations where GI must be combined from various sources, SDI

technology can access those sources and combine them. However, open standards

allow users to do this as and when needed without the need for an NSDI.

3.2.3. Crowdsource systems Free crowdsource system – Tracks4AfricaAt the commencement of this research project, the only crowdsourced map of Africa

was by Tracks4Africa (www.tracks4africa.co.za). It collates and publishes GI collected

from travellers’ GPS devices. The map has good coverage in South Africa, and

reasonable coverage of neighbouring Namibia, Botswana, Zimbabwe and Mozambique.

The map has less detail of the rest of Africa. The express aim is to collect data about

off-road tracks and the main inter-town roads; less effort is put into mapping towns and

cities. The Tracks4Africa system receives information about where the GPS has been

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(track-logs) and automatically interprets it using heuristics to identify where roads and

tracks are located. Most users are 4x4 and off-road motorcycling enthusiasts, so users

tend not to upload tracks of sealed highways. Because of this the resulting map is not

comprehensive. Originally it was available in Garmin’s proprietary format only, but in

2009 the commercial mapping company, Navteq, bought the rights to incorporate

Tracks4Africa data into its commercial offerings.

Free crowdsource system – Google Map MakerAs mentioned previously, Google Map Maker sources GI from members of the public.

The GI, though, becomes the intellectual property of Google (Google, 2012b). The

web-based user interface is relatively easy to use, and the contributor is encouraged to

complete meta-data for each feature using clearly labelled fields and drop-down boxes

where appropriate (Google, 2013):

By sourcing information from local businesses, governments, partner providers

and of course, our volunteer Map Maker community, our road coverage has

increased from 20% in 2008 to 75% in 2012, and the number of towns and

villages mapped has grown [tenfold].

…

With Map Maker launched in every country on the continent, we’ve also

witnessed the growth of a vibrant mapping community, with more than 100,000

unique editors contributing upwards of 2 million changes to Google Maps of

Africa.

The system has reasonably sophisticated moderation procedures. Initially, all

submissions from a new contributor are moderated before appearing on the Map Maker

system. As the contributor gains experience – and provided the contributions are

accepted as correct by moderators – the system begins to accept contributions without

the need for moderation. Even so, if the contributor posts a type of feature that he/she

hasn’t posted before, or if there is something unusual about a feature (such as a road

with a very sharp turn), that feature will be held for moderation. Sometimes the

moderation process hinders the contributor. For example, if a contributor corrects the

line of a main road and that correction is held for moderation, a new side road can’t be

connected to the main road until after moderation is complete. That may introduce a

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delay of several hours or even days. In some situations that delay can be frustrating,

perhaps leading to the abandonment of related updates.

Separately, Google’s philanthropic arm, Google.org, coordinates a variety of disaster-

related GI on its Crisis Map (www.google.org/crisismap/).

Open crowdsource system – OpenStreetMapOpenStreetMap or OSM (www.openstreetmap.org) – hosted at the University College

London – is a map of the world created mostly from crowdsourced data and made

available to the public under a Creative Commons licence. Originated by Steve Coast

in 2004 (Gyford, 2004), it was developed initially in the UK but within a few years

OSM was being updated and used worldwide. At the time of writing, detailed coverage

is mainly of the US and parts of Europe. Within Africa, coverage of South Africa is

reasonably good but coverage of the rest of Africa is variable. As discussed in section

2.5.2 under the subheading “Intellectual property rights and licensing”, OSM’s use of

Creative Commons licence freely permits a wide variety of (non-commercial) uses.

This makes it particularly attractive to community groups, NGOs and any organisation

without funds for expensive licences.

The main features of these three principal systems are compared in Table 3.6.

Tracks4Africa Google Map Maker OpenStreetMapCoverage Africa Most countries

worldwideWorldwide

Quality of coverage

Southern Africa has good detail. Coverage in other parts of Africa is variable.

Countries that are available are generally quite well mapped.

Mostly US and parts of Europe. Coverage in Africa is variable but improving.

Moderation

Conducted by staff at Tracks4Africa.

All changes by inexperienced users are automatically submitted for moderation. As users gain experience and their changes are accepted, the system accepts changes without moderation.

No formal moderation.

Table 3.6: Comparison of principal crowdsourced mapping systems

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In addition to the main three crowdsourced mapping systems compared in Table 3.6,

two experimental systems deserve mention.

Experimental crowdsource system – CollabMapCollabMap (www.collabmap.org) is the product of an EPSRC-funded project at

University of Southampton. The focus is on planning evacuation routes from locations

and large installations. Part of the design brief was to allow users to record the location

of individual buildings and their evacuation routes more easily than is possible on OSM

and Google Map Maker. Features are traced from aerial imagery. Those features are

then submitted to several other users for them to vote on their accuracy. (Ramchurn et

al., 2013)

Experimental crowdsource system – Geo-WikiGeo-Wiki (www.geo-wiki.org) is designed to record land cover around the world. It

was founded in 2009 by International Institute for Applied Systems Analysis,

University of Applied Sciences Wiener Neustadt and University of Freiburg. Land

cover data can help identify vulnerabilities to disasters, such as deforested

mountainsides and residential areas on flood plains.

3.2.4. Geo-data setsLeidig and Teeuw (2014) list a variety of free sources of geo-data sets, including the

Database Global Administrative Areas (www.gadm.org), Landscan population density

(web.ornl.gov/sci/landscan/), the FAO Soil Map (www.fao.org/geonetwork/), a

directory of ‘geohazard supersites’ by the Group on Earth Observations

(supersites.earthobservations.org), Tropical Rainfall Measuring Mission

(trmm.gsfc.nasa.gov), and many others.

Although much of the GI gathered by organisations can be held in an open system such

as OSM, there will be a need to store some GI privately. Deciding what kind of feature

should go into which layer, and managing that in the longer term, may be difficult. For

example, at the time of writing OSM shows hardly any individual residences in

developing countries. A situation may arise where an NGO needs to record the location

of residences that have been given a carbon-offset grant for a low-energy cook stove.

Clearly the amount of grant given would be recorded on the NGO’s own layer, but 104


should the individual residences be recorded on the base map? Similarly, after a

disaster, should IDP camps be shown on the base map or the post disaster layer?

3.2.5. Data formatsMany organisations, including government organisations in Uganda, use proprietary

GIS and store their GI in native (proprietary) formats. Combining the GI becomes

tedious, and difficulties are compounded if the GI also uses different geodetic systems

(ellipsoids and projections).

There are several international standards for geographic metadata, including ISO 19115

Geographic information – Metadata (ISO, 2003) and ISO 19139 Geographic

information – Metadata – XML schema implementation (ISO, 2007). This standard

defines a schema for describing GI and related services in terms of the identification,

the extent, the quality, the spatial reference, and the distribution of GI.

Fortunately, OSM uses international standards for storing and sharing GI using Internet

protocols (Coetzee, 2014):

the Simple Features Access for vector data (also available under ISO 19125);

the Web Feature Service (WFS) is a standard interface for specifying requests

for retrieving geographic features across the Web;

the Web Coverage Service (WCS) defines a standard interface that enables

access to raster and image data; and

the Web Map Service (WMS) provides an interface for requesting map images

from distributed geospatial databases.

3.2.6. SoftwareIn section 2.2.6, a variety of uses for GIS was set out. Although GIS technology may

have achieved widespread penetration in organisations in Western society, it has not

been so widely adopted in developing countries. Traditionally, professional GIS has

been expensive to buy, difficult to learn, and difficult to maintain. In 1991, Yapa

argued that GIS was not appropriate technology for developing countries because of its

high cost of purchase, maintenance, and expertise. He did, however, recognise the

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importance of GIS. At the time, Yapa (1991) suggested that to make GIS more

accessible in developing countries, four things should happen:

that proprietary rights over GIS software need to be relaxed;

that ‘public domain’ (open source) GIS should be developed;

that GIS software should be built on top of existing software infrastructure in

developing countries; and

that local information systems should be more inclusive of indigenous

knowledge and public participation should be encouraged.

Since then open source GIS has become more widely available, and information

systems can now include indigenous knowledge through crowdsourcing. Leidig and

Teeuw (2014) list a variety of free sources of free GIS software – including QGIS

(www.qgis.org) and SAGA (www.saga-gis.org) – and data sets.

GeoNode (weww.geonode.org) is a widely-used open source system for finding,

creating, sharing, and collaborating with GI. It is built with various open-source

components including Django, GeoServer, OpenLayers and GeoExt. It implements the

OGC standards mentioned previously and can interact with systems that similarly use

open standards.

3.2.7. Intellectual property rightsIn general, GI that has permissive IPR is more flexible for NGOs. Even then, it is

usually not permissible to merge GI from one system into another. For example, GI

obtained under a copyright licence cannot be incorporated into OSM (OSM, 2014).

This demonstrates the need to store GI from different sources separately, and display

them as separate layers in a GIS. Fortunately, provided the GI sources use appropriate

standards, modern GISs can access and overlay them.

3.2.8. Ethical issuesWith the increasing use of GI various ethical questions have emerged, such as who

would be held culpable for losses caused by errors in spatial datasets? This is important

in the event of an accident resulting from the use of GI, for example, if contamination

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of a watercourse is incorrectly modelled. There are also the issues of privacy and

surveillance of society. High resolution aerial imagery has become pervasive on

crowdsourcing and Internet-based geographic systems. What are the implications for

the privacy of citizens if this is used by government agencies for surveillance of

citizens’ activities? (COA, 2014)

3.2.9. EvaluationAt the time of writing, the major crowdsource mapping systems are OSM and Google

Map Maker. Both systems have their strengths and weaknesses. On balance, the OSM’s

Creative Commons licence, and the fact that it can be edited even when there is a break

in Internet service, more than make up for deficiencies in the editors’ user interface.

A GIS can be useful for overlaying GI from different sources, but is not needed solely

for crowdsourced mapping. The major commercial GISs are capable but expensive. A

variety of open source GIS packages is available and they provide all the commonly-

used functions.

Although crowdsource systems provide many of the features of an SDI, if one is

needed – for example to combine private post-disaster GI with a public base map – then

open source systems are available. Geonode (weww.geonode.org) is an example of a

comprehensive open source toolkit that shares and merges GI in OGC-standard

formats.

3.3. Recording GI3.3.1. RequirementsLists of geographic features required for disaster management are given in section

2.2.6. If high-resolution mapping is necessary, almost all features might be mapped

with perimeter polygons. For disaster management, points are usually sufficient for the

location of buildings and some natural resources such as springs, and poly-lines are

usually sufficient representations of roads, streams and narrow rivers. Polygons are

needed to represent only larger features such as flood plains, unstable hillsides, land

use, and administrative boundaries. Mapping teams need to decide which features can

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be mapped using remote sensing (such as satellite images), and which have to be

visited for accurate geo-location using GPS and/or geo-tagged imagery.

3.3.2. Global Positioning SystemIt may be helpful to divide GPS (strictly speaking, GNSS) devices into four categories:

survey-grade, consumer-grade, data loggers, and smartphones.

Professional survey-grade GPSs provide high accuracy, perhaps measured within

millimetres, but are very expensive. Such devices may use a variety of technologies to

achieve such accuracy. GPS satellites use a carrier signal that is modulating relatively

quickly to send pseudo-random codes that are phased relatively slowly; the latter is

known as the ‘code phase’. All GPS devices use the code phase to identify location to

within a few yards, but a survey-grade device then uses the carrier phase to refine that

to a few centimetres Even so, fluctuations within the ionosphere can interfere with the

signal and reduce accuracy. A solution is to use ‘Differential GPS’, which requires two

GPS devices. The first is stationary and identifies its location by averaging over a

period of a few hours; having established its location it then starts to record signal

fluctuations. The second mobile device is used to locate features, but its reported

location is adjusted to take account of any fluctuations identified by the first device.

Although accuracy to within a few millimetres is desirable, it is not necessary for most

disaster preparedness purposes.

Some consumer-grade GPSs can record poly-lines (track-logs) as they move about, and

points of interest (POIs or favourites) when the user presses an appropriate button.

Accuracy is typically within three or four metres horizontally and about five metres

vertically (FAA, 2014: p21), though accuracy is lower for a period after being turned

on, when near tall buildings or large trees, and in mountainous areas.

Most GPS data loggers are cheaper still, as they have no screen. Typically the device

has a power switch to commence a track-log, a button to record a POI, LEDs to

indicate status, and perhaps Bluetooth features. The contributor must record the date

and time a POI was visited, and match that to the date and time the POI button was

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pressed. Accuracy is similar to a consumer-grade GPS, i.e. typically within a few

metres.

A smartphone with built-in GPS and an app to record track-logs and POIs can be

useful. In other respects, operation is similar to a consumer-grade GPS, though

accuracy may not be quite as good.

3.3.3. Geo-tagged imageryGPS and photographic equipment can be combined so images are tagged with their

location. This imagery can be used during mapping exercises to help ensure features

are named correctly. For example, a modern smartphone has the ability to run ‘apps’,

use GPS and take photographs. The author installed Trip Log and OSM Tracker apps

for fieldwork in 2011 and 2012. The geo-tagged images helped ensure feature names

were recorded and classified accurately.

Geo-tagged photographic technology can also be used to turn continuous recordings

into inventories of features. Montoya (2003) explored the use of a low cost and rapid

method of developing an urban building inventory based on the combination of geo-

tagged imagery, remote sensing and GIS. Each building in a target area was surveyed at

a level of detail appropriate to its utility in disaster management. Buildings identified as

being ‘very important’ for rescue and relief would be surveyed in detail. However,

buildings classified as ‘regular facilities’ would receive only a cursory screening from

outside. Video images of regular facilities were taken from a moving vehicle at a rate

of one frame per second (akin to Google’s Street View). These were geotagged using a

GPS.

3.3.4. Remote sensingRemote sensing technology can be categorised in several ways, such as active or

passive, the various wavelengths recorded, and whether originated from satellite,

aircraft or at ground level. In crowdsourced mapping systems, the most commonly

provided form of remote sensing is passive sensing in visible electromagnetic

wavelengths by satellite. In this thesis remote sensing in the visible range has been

referred to as ‘aerial imagery’. Aerial images from satellites, aircraft or Unmanned

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Aerial Vehicles (UAVs or ‘drones’) can provide useful information for identifying

hazards and vulnerabilities during all stages of disaster management (Teeuw et al.,

2012, p1).

Reliance solely on aerial images for mapping is not to be recommended, since it is

difficult to identify specific buildings reliably. Furthermore, in the context of disaster

management, disasters are mainly human in nature and remote sensing tells us little

about the situation on the ground from the perspective of the inhabitants. However,

much can be achieved using aerial imagery. For example, Google’s original base map

of Kenya was originally developed in about ten months by seven interns in Kenya and a

team in India (Schutzberg, 2008). They achieved this by tracing roads and towns from

aerial images onto a digital map and supplemented it with local knowledge; there was

no ground truthing and no GPS devices were used.

A major disadvantage with passive sensing of visible wavelengths is that much of the

Earth’s surface is dark and/or obscured by clouds. Alternatives include passive sensing

in other wavelengths, such as infrared, which might provide data over areas at night,

and active sensing where the sensor emits a signal and monitors what is reflected.

Although it is possible to use Sonar (acoustic signals), most active systems use

electromagnetic signals such as Radar (Ultra-High Frequency and microwaves),

infrared, laser and LiDAR (visible), ultraviolet and gamma ray (Lillesand et al., 2015:

pp 4-9). Modern remote sensing satellites are multispectral; they are capable of

emitting and detecting a wide range of wavelengths. Information that can be inferred

using various wavelengths include elevation (digital elevation model), sea level and

tides, extent of floodwater, water quality (chlorophyll and phosphorous density),

mineral content, industrial activity (heat and atmospheric pollution), meteorology, and

vegetation (ibid: pp 9-23). By comparing readings over time it is possible to infer

phenomena like flooding, famine encroachment, deforestation and desertification. An

example of remote sensing in disaster management is given in Allum et al. (2012). Fine

resolution imagery from the WorldView-2 satellite that included a range of spectral

bands were used to extract detailed information on the morphology of the flow and the

composition of volcanic material along the Northeast Ridge on the island of Tenerife.

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Pre-disaster accessGoogle Map Maker has reasonable aerial imagery of most of the world and this is

freely available. The quality of OSM’s aerial imagery has improved in recent years but

is not as consistent; remote areas may not have good coverage, and images may be

partially obscured by clouds. (Some parts of the world have near-permanent cloud

cover.)

Professionally-produced contemporary images can be rather expensive, especially for

cash-strapped NGOs. Although modest resolution satellite images might cost in the

region of €0.30 per km2 (at 2015 prices), each image may cover thousands of square

kilometres and cost hundreds or thousands of Euros so the investment would be quite

burdensome. Teeuw et al. (2012: pp 3-6) summarised a variety of sources of remotely

sensed imagery including many that are free. The Global Land Cover Facility exists to

encourage the use of remotely sensed imagery among a broad range of science

communities; it focusses on improving the comprehension of land cover change and its

impact (GLCF, 2014). Other free sources include the Advanced Spectral and Thermal

Emission Radiometer (ASTER), ASTER DEM and MODIS systems from NASA and

JAXA, Landsat-5 and Landsat-7 from the US Geological Survey, and TopSat from a

British consortium. ASTER DEM has better spectral resolution than the Landsats, so

potentially can provide more detailed hazard, vulnerability and risk maps (Teeuw et al.,

2012: p 8).

Sometimes governments make imagery available for humanitarian purposes. For

example, the Humanitarian Information Unit at the US Department of State has made

available aerial images of areas where there have been disasters, such as Punia,

Democratic Republic of Congo, and Nimule, South Sudan. This imagery can be

accessed through OSM’s tasking manager (tasks.hotosm.org) (Hacklay et al., 2014:

p 60).

Over the decades various amateurs have experimented with low-altitude aerial

photography. In 1985, Dieter Noli documented a simple but effective system for

photographing a neighbourhood using a camera attached to a tethered meteorological

balloon (Noli, 1985). The entire kit would fit into a medium-sized suitcase and would

cost less than €100. With a low-cost digital video camera, such a solution ought to be

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within the resources of most NGOs and keen amateurs. Between May and July 2010,

amateur geographers attached cameras to tethered balloons and kites (depending on the

weather conditions) to monitor oil spills from the Deepwater Horizon oil rig in the Gulf

of Mexico (Warren and Long, 2010). They claim that over half of the excursions

returned with “excellent” or “usable” data, and over 11,000 images were taken.

More recently, advances in consumer-grade UAVs have made remote-control

multirotor helicopters with built-in high definition cameras obtainable for a few

hundred Euros. Aerial imagery for crowdsourcing using small UAVs is now feasible

(Teeuw et al., 2012: p 8).. These are particularly useful where satellite images are out

of date, or where the images are at low resolution, or where satellite images are

obscured by cloud cover. There are several operational issues, such as operating in

windy conditions. Post-processing photographs derived this way also takes some effort,

as images have to be imported into relevant software, joined together and

georeferenced. MapKnitter (www.mapknitter.org) allows users to merge and

georeference images online for free.

Post-disaster accessIn the event of a crisis arising from “natural or technological disasters” the European

Space Agency makes satellite imagery available to disaster response organisations and

governments under the International Charter Space and Major Disasters (International

Charter, 2000). The licence for those aerial images prevents users from making derived

works or re-distributing the images. In contrast, immediately after the 2010 Haitian

earthquake, GeoEye and DigitalGlobe made aerial images of Haiti freely available with

Creative Commons ‘attribution only’ licences which greatly enhanced the work of the

crisis mapping crowd (Harvard Humanitarian Initiative, 2010: p29).

Post-disaster imagery has a degree of relevance to disaster preparedness mapping

where an area is chronically prone to disasters in that GI derived from such imagery

can help with plans for the future. It is worth noting that the International Charter has

been activated when there are concerns of an impending disaster, such as in 2008 when

satellite imagery of Montserrat in the Caribbean was made available due to concerns of

an impending volcanic eruption (Teeuw et al., 2012: p11).

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Interpreting remotely sensed dataIn general, crowdsource systems display aerial imagery and map data as layers in an

editor, and contributors trace features from the imagery onto the map. Some editors

allow contributors to load other layers including their own GPS track-logs and geo-

tagged photographs.

There are some initiatives that use other methods for contributors to convert remote

sensing imagery into GI, such as Tomnod (Barrington, 2014). Tomnod divides large

images into many small tiles and sends each tile to several contributors. Each

contributor is asked to identify relevant features in the tile, such as disaster-related

damage or key locations in a city. Although each contributor works in isolation, the

system uses a voting algorithm to reach a consensus. Furthermore, each contributor’s

reliability is assessed using an algorithm called CrowdRank. Contributors whose votes

are regularly in agreement with the consensus are given greater credence in future

votes. Tomnod, Inc. (a subsidiary of Digital Globe, Inc.) owns the IPR of the GI

generated from Tomnod (Tomnod, 2014). Marshall (2015) criticised the outputs from

Tomnod as “frustratingly ineffectual” and suggests that the work of the Humanitarian

OpenStreetMap Team (HOT) is better at actually helping disaster response teams to

navigate with accuracy.

A broadly similar concept is used by the United Nations Institute for Training and

Research’s Operational Satellite Applications Programme (UNITAR-UNOSAT). The

GeoTag-X project uses crowdsourcing to analyse photographs taken in disaster-

affected areas (UNITAR, 2014). These photographs are taken on the ground, not from

satellites, and are geotagged. The contributors are asked to indicate the condition of a

structure or some other feature of interest to the United Nations.

There are other initiatives such as AtomicORCHID (ORCHID, 2014). This coordinates

crowdsource information during disaster response. The system sets the tasks and the

contributors provide the feedback.

3.3.5. Checking and moderating GIGround truthing is the process of verifying GI by checking each feature on location. It

ought to be used for all GI, but in particular where the GI was originally gathered using 113


remote sensing. Determining features from aerial images alone can be difficult, for

example, where roads and watercourses are obscured by the tree canopy, or can lead to

incorrect conclusions. For example, after the Asian Tsunami of 2004, a satellite was

tasked to survey the affected area. Based on the images, observers suggested that a

particular railway track in Sri Lanka was undamaged. The team on the ground had to

explain that the tidal wave had flipped the track completely upside-down, making it

useless, despite how it might look from the air. A more common example is the

identification of unsealed roads (tracks). From aerial images alone it may not be

possible to determine if a road is public or private, or whether it is impassable after

poor weather and so on. In places a track can become narrower and narrower and

eventually driveable only by motorbikes. As remote sensing and analysis techniques

improve, it may be possible to distinguish these subtle differences. Nevertheless,

whether features have been identified using remote sensing or ground-based survey,

there may be value in sending other team members to verify the GI and determine if the

map is usable.

3.3.6. EvaluationInaccuracies inherent in consumer-grade GPS mean resultant maps may be less

accurate than those produced using professional technologies. The US Geological

Survey has stated standards for map accuracy (USGS, 2006). Horizontal accuracy is

measured as follows. “For maps on publication scales larger than 1:20,000, not more

than 10% of the points tested shall be in error by more than 1/30 inch, measured on the

publication scale; for maps on publication scales of 1:20,000 or smaller, 1/50 inch.”

(USGS, 1999: p2) They test this by selecting 20 well-defined points – such as a road

junction – and measure their positions using sophisticated surveying techniques.

Provided 90% of these points are within the tolerance, it is certified and published with

a compliance statement.

In practice a modern consumer-grade GPS is likely to have an accuracy of somewhere

around four metres horizontally; it is less accurate at indicating elevation (FAA, 2014:

p21). The horizontal accuracy is generally adequate for the purposes of crowdsourced

mapping, though perhaps less satisfactory when mapping built-up areas, since signal

interference from buildings is worse. Vertical accuracy is not usually an issue since

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crowdsourced systems use its internal digital elevation model. Despite reservations

about accuracy of consumer-grade GPS, locations can be more accurate than those

taken from an aerial image (especially one that hasn’t been orthographically rectified

properly).

Remote sensing is useful for broad coverage and orientation. Furthermore, remote

sensing is particularly useful for identifying the route of watercourses. Unless the

watercourse is navigable, a surveyor can’t take a GPS along the centre line. Most

streams and river banks are dangerous to walk due to undergrowth, mud, slippery rock,

dangerous drops and the risk of water-borne disease. Riverbanks are also usually

private land. Conversely, using remote sensing it is possible to record many kilometres

of rivers and streams in just a few hours.

OSM has some high-resolution aerial imagery of developing countries and coverage is

improving, but the quality is variable and coverage remains incomplete. For example,

high-resolution aerial imagery of the disaster-prone Bududa District in the Mbale

region, Uganda, is partially obscured by clouds and incomplete. Some remote sensing

sources are prohibitively costly; even when cheap or low cost imagery is obtained,

some localities are obscured by near-permanent cloud cover. A UAV (drone) that

captures low-altitude imagery may overcome those two problems. LiDAR might be

useful in specific situations, especially where the precise contours of a feature are

important for some reason.

Geo-tagged photographs of features can be useful but not vital. The photographs serve

as useful reminders of a feature, and a photograph of a name sign outside a building can

help reduce spelling transcription errors. There are initiatives to utilise geo-tagged

photographs to monitor the development of disaster areas (UNITAR, 2014).

3.4. Supporting ICTsIn order to store the GI that has been recorded, crowdsource contributors will require

supporting ICTs – such as PCs, software and Internet communication – and a reliable

power supply. Endemic poverty and poor infrastructure severely hamper development

of ICTs in developing countries. According to the latest available figures from the

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World Bank (2009a) Africa still has the lowest Internet user penetration at about 5%,

compared with global average of 20%.

In order to identify barriers to ICT in developing countries, Touray et al. (2013)

analysed 354 articles in various leading ICT/developing countries journals. Commonly

mentioned barriers to ICT included high costs, low income, corruption, unnecessary

bureaucracy, lack of political will, political instability, lack of proper legal framework,

poor regulation, lack of/inadequate electricity supplies, lack of/inadequate fixed

telephone lines, low Internet bandwidth, unreliable Internet connection, poor network

reception, lack of maintenance culture, lack of ICT skills and high illiteracy (ibid: p10).

These barriers hinder community participation in mapping initiatives.

3.4.1. Internet and power supplyThe choice of hardware and software is likely to be affected by reliability of utilities. It

is quite common for large parts of developing countries to have no electrical power

supply, or at best unreliable supplies. For example, in Uganda only 12% of the

population has access to electrical supplies from the national grid; most of the power on

the Ugandan grid currently comes from two stations on the Nile at Owen Falls, which

makes supplies vulnerable (MBendi, 2014; Energypedia, 2014). During the fieldwork

described in Chapter 6 there were typically several power outages each day, though

official figures claim only 6 outages per month (World Bank, 2014). Larger buildings

often have power generators on standby, but they can be unreliable and create power

spikes that can damage PCs. About 16% of the Ugandan population uses the Internet,

though only 0.1% uses a fixed-line Internet connection (World Bank, 2014). Mobile

broadband is more widely available and more reliable than fixed line.

3.4.2. Hardware and softwareThe above factors affect choice of hardware and software. Laptop PCs can cope with

short power cuts. If a contributor is using a GPS or smartphone to collect GI, an in-car

charger or solar charger can be useful. A mobile broadband dongle will access a more

reliable service than a fixed broadband router. Both types of service suffer

interruptions, so GI editors that can continue without constant Internet access will be

more reliable than purely Web-based editors that require constant Internet access. For

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example, the only editor for Google Map Maker is Web-based; an interruption to

Internet service means recent changes will not be saved to the server. OSM has various

editors, of which the JOSM editor can continue to work despite intermittent breaks in

Internet service.

3.4.3. Social, environmental and economic issuesThere are a range of social issues that hinder the implementation of new technologies in

developing countries. Touray et al. (2013) identified high purchase costs, low income,

high illiteracy rates, and lack of ICT skills as common barriers to uptake of ICTs. If

software is difficult to use, systems are likely to fall into disuse as soon as problems

arise. Eria (2012: p164) warns that an expensive GIS may generate antagonism. Among

general society, Eria claims, there is a culture of communal living, communal rights

and ownership, and a respect for cultural values, norms and practices. The public can

also be cautious toward those in powerful positions of government, for example, local

politicians. A GIS may be associated with authority or a Western way of life and thus

viewed with suspicion.

In relation to government officials, Eria (2012) gave an example of corruption in

cadastre management in Uganda. When applications for land title were processed

manually with printed maps and forms completed in ink on paper, officials could

decide priorities. Eria – himself a Ugandan – claimed an application could take from a

few weeks to a few years “depending on how much of a bribe one is able to provide the

concerned authorities” (ibid: p161). When the inefficient paper-based system was

replaced with an automated cadastre system supported by a GIS, it reduced the

opportunity for officials to take bribes. Consequently there was significant resistance to

the introduction and extension of the Land Information System cadastre.

Among NGOs, relying too much on their own statistics could cause problems. Eria

(2012: p165) gives a fictional-but-realistic example of an NGO whose GIS identifies

that 50% of Ugandan forests are degraded. If official government statistics say only

20% of forests are degraded, the NGO may be taunted as anti-government or perhaps

working for an opposition party. The NGO may lose government cooperation, be

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denied permits, or face other difficulties. Consequently, an NGO may prefer to use

official figures even though it suspects problems are understated.

3.4.4. Financial costs and benefitsWishart and Coote (2007) set out costs and benefits that an organisation might see from

a generic GIS project. Costs might be similar to those on other computer technology

projects, except perhaps the additional cost of acquiring relevant GI. Wishart and Coote

give a long list, including (ibid: p18):

hardware, integration with pre-existing computing infrastructure;

evaluation, selection, acquisition and installation of software; and so on down to

training, human resources planning, skills development and re-skilling.

Benefits will depend heavily on the precise project being considered. Wishart and

Coote (2007: pp18-19) mention several benefits in relation to for-profit organisations:

raised revenues and improved sales from better-targeted marketing;

lower costs, such as lower software migration costs;

increased satisfaction, such as improved customer services and reduced

disruptive activity;

increased efficiency, such as avoiding unnecessary site visits; and

decreased risk, such as identifying areas liable to flooding.

Some of Wishart and Coote’s cost and benefit headings may appear irrelevant to local

government and NGOs in developing countries, but improved disaster preparedness

and mitigation through identifying high risk areas might be considered analogous to

their “Improved customer services” and “Decreased risk” (ibid: p18).

3.5. Summary of technologies for humanitarian mapping

As explained in section 3.1, mapping large parts of the world for the purposes of

disaster management is practical only if local communities can be encouraged to map

their own localities and to share their GI on a publicly-available system. In addition to 118


being fit for purpose, technologies need to be economically priced, easy to use and able

to share GI among contributors and other users.

3.5.1. Storing and retrieving GIThe Ugandan government understands that most or all GI required by government and

non-government organisations could be stored in an open-source, web-based mapping

system such as OSM (OPM, 2008: p69):

Government will come up with national risk, hazard and disaster profiles and

maps of the country depicting each of the known natural and human-induced

disasters… Electronic copies of the profiles and maps shall be made available to

the public through relevant websites and other electronic mechanisms.

OSM can store a range of attributes for every feature:

core attributes, such as key value, type of feature (point, line, polygon);

standard approved attributes, such as name, description (e.g. hospital, tertiary

road, power line, wetlands), condition (e.g. poor road surface, abandoned

building) and so on;

any other attributes, as defined by whoever added them.

o Non-standard attributes are not banned by OSM, but those attributes will

not be rendered as standard by mapping software.

The possibility of storing non-standard attributes raises the question about whether

organisations might store everything they need solely in OSM. For example, an NGO

may want to record details of a financial grant given to specific residences, or a

highway authority might want to note the name of the contractor for road repairs and so

on. That data could be stored as a non-standard attribute, but one has to bear in mind

that if this private data can be seen, altered and deleted by the public: it is neither

confidential nor secure from misuse. So, while it is possible to hold private data within

OSM, organisations ought to store it in a secure GIS. That GIS can read the OSM base

map and overlay the private layers. In a similar vein, to avoid accidentally misleading

the public, proposals for roads and buildings that do not yet exist ought to be stored

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separately from the public OSM base map. Similarly, disaster response organisations

are likely to want to keep GI about post-disaster relief work in their own private GIS.

They may not want the public to see distressing and/or personal details about

casualties. Records of search-and-rescue activities (areas that have been searched) need

to be stored securely so outsiders can’t remove them in order to precipitate a second

search of an area where a loved one lives.

It is likely that three types of layers will be needed. First, a common base map layer

will show features in their pre-disaster state. In section 2.2.6, the first part of the list

suggests pre-disaster features that should appear. (The base map may eventually be

updated some time after the disaster, for example, to represent changed coastline or

villages that have been abandoned.) Second, each organisation may need their own

private layers that show features of interest to them and private to them. For example,

they may need the locations of recipients of aid, demographics, planned future

developments, and any other data not recorded in the public base map. Third, disaster

response organisations will need post-disaster layers of temporary features, such as the

extent of the disaster area, the locations of response organisations, records of search-

and-rescue, and reports of possible survivors, as listed in section 2.2.6. The three types

of layer are demonstrated in Error: Reference source not found.

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etc…

Base map(Public)

Highway data(Private)

Highway authority Health NGO Disaster NGO

Health data(Private)

Post-disaster(Private)

PublicTrained contributors staff

Figure 3.1: Possible multi-layer architecture.

Base map layer read into GISs


The pre-disaster base map can be incorporated into organisations’ information systems

using web services (see section 3.2.5). After the 2010 Haiti earthquake it was found

that web services were more successful for exchanging data than normal web pages

because the data were in structured format, required low bandwidth, and could be

processed in the background (Harvard Humanitarian Initiative, 2010: p41):

The most successful integration of tools and data from V&TCs [Volunteer and

technical communities] happened in the areas where the international

humanitarian system has adopted the most open standards: geospatial data.

Many GIS officers and V&TCs were using applications that supported Open

Geospatial Consortium standards such as WMS (web mapping service) and

WFS (web feature service). As a result, many information systems were able to

exchange critical geospatial information using reliable, consistent formats. For

instance, an OpenStreetMap GIS analyst could point her application to the

output of an IOM analyst’s service, integrating traces of the changing outer

boundaries of IDP camps (polygons). In turn, a third analyst from WFP could

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Base map(public)

Highway data(Private)

Highway authority Health NGO

Health data(Private)

Post-disaster(Private)


Road manag’nt GIS

Health care GIS

Disaster response GIS

Disaster NGO

OSM software

Web service

s

Web service

s

Web service

s

Web browser

Web service

s

http

Figure 3.2: Example data flows.


point his tools to the output of both the IOM and OpenStreetMap and obtain

street level data around those IDP camps.

Error: Reference source not found shows how both information sharing and privacy

could be supported using Web services.

3.5.2. Recording GIFor most disaster preparedness initiatives, consumer-grade GPS devices are likely to be

sufficiently accurate. Most are accurate to within a few metres horizontally (FAA,

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2014), and that is generally sufficient for asset identification, hazard analysis,

vulnerability analysis and impact analysis. During disaster response, the location of

features to within a few metres is similarly sufficient. Most commonly used are hand-

held GPSs; data loggers are far cheaper, but have no facility to record the identification

of features. As smartphones become more widespread, they are likely to become more

prominent; geo-tagged imagery is easy and reliable with smartphones.

When entering GI from a GPS device into OSM and Google Map Maker, the editor

displays the map on screen, and the GPS track-log and POI layers are overlaid. The

contributor must decide where features are from the GPS data, and draw them on the

map. Apart from Tracks4Africa, other crowdsource mapping systems do not

automatically load track-logs and POIs directly into the map because the data have to

be interpreted. For example, when surveying a road, a stop-off alongside the road is not

a kink in the road. Another track-log may record the boundary of a field rather than a

road. Furthermore the contributor is encouraged to enter other data, such as the name

and category of each feature, the type of vegetation, the number of hospital beds, the

quality of the road surface and so on as appropriate. The contributor enters the

horizontal position of each feature; the elevation is assumed from the system’s own I

internal digital elevation model.

Both OSM and Google Map Maker have aerial imagery of most of the world; in some

places OSM’s imagery can be quite low resolution though. Purchasing aerial imagery

can be expensive, but it can be made available from the Humanitarian Information Unit

of the US Department of State (for disaster preparedness) and under the International

Charter Space and Major Disasters (mostly for disaster response). As the

price/performance of UAVs improves, their use in producing low-altitude aerial images

becomes feasible. These are particularly useful where available satellite images are out

of date, too low resolution, or obscured by cloud cover.

Other forms of remote sensing – such as LiDAR – are likely to be needed only in

specific circumstances, such as where higher accuracy is needed to model the path of

lahar flows.

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3.5.3. Supporting ICTsIn general, financial constraints within both government and non-government

organisations mean that low-cost hardware and low-cost or free software is to be

preferred, but with some caveats. Hand-held equipment needs to be durable and

waterproof to be usable during the rainy season. PCs with batteries (e.g. laptop) or

uninterruptable power supply will minimise the effect of power outages. Software that

can work for periods without Internet connection will minimise the effect of service

breaks. In many countries, mobile broadband is more reliable than fixed broadband.

Software needs to be user friendly and allow for lack of ICT skills and low literacy

rates.

3.6. Chapter summaryThis chapter has evaluated a range of possible technologies that can be used for

mapping developing countries in general and East Africa in particular. At the time of

writing, there are two commonly-used crowdsource mapping systems, OSM and

Google Map Maker. Although both can be and have been used, the flexible data licence

of OSM makes it the more preferable system. Technologies for collecting GI, and for

bringing together public and private GI have also been discussed. Finally, basic ICTs

are needed by mapping teams, and the local circumstances are likely to influence

choices made.

It is important not to lose sight of the fact that many aspects of a disaster are human

related, and technology alone can seldom solve human-related problems. Just putting

technologies into place does not mean they will be used. Getting those technologies

accepted and used by contributors is also important. In order to identify how best to

encourage contributors to contribute – especially in the long term – managers of

mapping initiatives need to understand human behaviour; this is the subject of the next

chapter.

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4. Behavioural Models

4.1. ContextThis project’s key aim is to assist the mapping of developing countries for disaster

preparedness by modelling the factors that enable and encourage communities to adopt

and use mapping technologies. It has long been recognised that measuring and

anticipating human behaviour is difficult (for example, see Ajzen, 1991). This chapter

reviews theories that model peoples’ preparedness for disasters, acceptance of

innovations, propensity to adopt technologies, and motivation.

4.2. Disaster preparedness modelsThere are a group of theories that model how people act when anticipating a disaster.

4.2.1. Protective Action Decision Model (Perry and Lindell, 2007)

Perry and Lindell (2007: p302) suggested that people follow five steps when making

decisions about protection measures:

Risk identification. A person’s belief that an extreme event will occur soon.

Risk assessment. If the warnings are from credible sources and the person

believes he/she will experience severe consequences as a result of that event, it

will motivate protective actions.

Protective action search. A person will seek protective measures either from

memory of similar events or other sources of information.

Protective action assessment and selection. Using this information, the person

will decide which protection actions are appropriate.

Protective action implementation. This begins when that person decides it is

time to take action.

Although the Protective Action Decision Model (PADM) model may seem rather basic,

it provides a starting point for breaking down the process whereby a person undertakes

disaster preparedness activities. It highlights the fact that people will tend to repeat

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familiar protective actions unless they are informed of alternatives. Perry and Lindell

found that some people are less likely than others to seek such alternative activities, and

indeed some may have difficulty articulating their need for information about

alternatives. Miller et al. (2012: p5) concluded that, “… while the PADM posits

perceived self-efficacy as influencing the process of protective action assessment and

information-seeking, it does not address the general nature of attitudes or threat beliefs

likely to motivate protective actions.”

Perry and Lindell (2007: p306) reported that warnings are most effective when they

provide specific information about location, time and severity of the impending event.

However, the broad geographic scales and timescales of disaster preparedness hinder

attempts to motivate communities to undertake mapping for disaster preparedness.

Consequently, alternative motivators that are geographically and temporally near must

be identified.

4.2.2. Vested Interest Theory (Crano and Prislin, 1995)Several of the case studies in Chapter 6 reveal a discontinuity between attitudes

towards disaster preparedness and actual preparation activity. Vested Interest Theory is

based on the premise that if an object has a hedonic (positive) relevance, that attitude

will be “highly vested” and therefore be a good predictor of behaviour (Miller et al.,

2012). Crano and Prislin (1995) identified four attitudinal components that are critical

for vestedness: (a) the salience of the attitude; (b) the perceived likelihood of

consequences following from relevant behaviours; (c) the perceived immediacy of the

consequences; and (d) the person’s confidence in his/her ability to conduct relevant

behaviour.

In addition to the attitudinal components, Crano (1983) also proposed five dimensions

for attitudinal behaviour in Vested Interest Theory: (a) where a person has a high

personal stake in the outcomes, they generate more relevant thoughts and use those

thoughts as the basis for making decisions; (b) of equal prominence is the concept of

salience, which pertains to the accessibility of an attitude-object; (c) the degree of

certainty about the possible consequences associated with an attitudinal behaviour has a

moderating effect on the level of vested interest; (d) the temporal immediacy of

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relevant consequences tend to improve consistency between attitudes and actions; and

(e) attitudes that are relevant are more likely to guide behaviour and result in the person

believing he/she can demonstrate self-efficacy. Miller et al. (2012: p7) reported on

several studies that found self-efficacy consistently performs as a reliable predictor of

relevant behaviour.

<Key Point Model> Crano (1983) found that all five of these dimensions need to be

present to predict attitude-related behaviour reliably. It is claimed that if even one is

absent, the overall vestedness will be reduced. This is important to this project. As with

PADM above, disaster preparedness mapping projects with broad geographic scales

and timescales are likely to be demotivating. Crano confirmed Perry and Lindell’s

(2007) proposition that alternative motivators, geographically and temporally near,

must be identified.

4.2.3. Extended Parallel Process Model (Witte, 1992)The Extended Parallel Process Model (EPPM) was an extension of an earlier model. It

was designed to explain and predict individuals’ reactions to fear appeal messages. As

echoed in Crano and Prislin (1995), fear appeals have two main elements, each of

which can be subdivided (Witte, 1992: p331-332):

Threat:

o Perceived severity – the amount of harm or damage that may ensue.

o Perceived susceptibility – an assessment of how susceptible the subject

is to that harm or damage.

Efficacy:

o Perceived response efficacy – the subject’s faith that the proposed

response will effectively mitigate the threat.

o Perceived self-efficacy – the subject’s self-perceived ability to perform

the actions necessary to mitigate the threat.

EPPM suggests that if the subject believes the threat to be insignificant, or that the

subject does not feel susceptible, no further action will be taken. If the threat is

perceived to be significant and the subject feels susceptible, then he/she will appraise

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the proposed response(s). The desired outcome is that the subject performs the response

effectively. However, if the proposed response is considered ineffective, or if the

subject feels unable to perform that response effectively, then he/she is expected to

engage in fear-control processes such as message rejection or source derogation.

<Key Point Model> Fear of, or at least concern about, a disaster is a motivator to take

protective action, but only if people feel they their actions will be effective (Witte,

1992).

According to Miller et al. (2012: p9), Vested Interest (VI) Theory and EPPM can work

in tandem.

VI and the EPPM provide valuable overlapping frameworks for constructing

social action campaigns by highlighting important variables linking attitudes

with behaviours. Both models account for threat severity, or stake, as it is

labelled in VI; and both models account for an individual’s perceived self-

efficacy. However, only the EPPM provides a direct method for measuring

these key variables via an audience analysis survey method.

4.3. Innovation models4.3.1. Diffusion of Innovations (Rogers, 1963)Diffusions of Innovations (DOI) theory was originated in the 1960s by Everett Rogers

(1963; 2003). This theory concerns the adoption of technologies, especially the take up

of consumer technologies by people in Western society. It should be noted, though, that

some researchers have concluded that the theory does not transfer well to other

contexts, e.g. other types of technologies or alternative cultures (for example, Clarke,

1999). DOI theory is perhaps best known for the various adopter categories identified

by Rogers (1963): Innovators (venturesome); Early Adopters (respect); Early Majority

(deliberate); Late Majority (sceptical); Laggards (traditional). Rogers represented them

diagrammatically as in Error: Reference source not found.

128

2.5% 13.5% 34% 34% 16%Innovators Early Early Late Laggards Adopters Majority Majority

Figure 4.1 Adopter categories (Rogers, 1963).


Rogers (2003) suggested that the process through which an individual makes a decision

about an innovation has five stages:

knowledge – an individual is exposed to the existence of an innovation, and

gains an understanding of its functions;

persuasion – when an individual forms a favourable attitude to the innovation;

decision – an individual engages in activities leading to a choice to adopt or

reject it;

implementation – when an individual puts an innovation to use; and

confirmation – an individual seeks reinforcement for a decision, based on

positive outcomes from it, but may reverse that decision if outcomes are

negative (or perhaps neutral).

Rogers defined three types of innovation decision: optional, collective and authority.

‘Optional’ means there is little or no compunction. ‘Collective’ decisions are based on

consensus among members of a social system such that all members must conform.

‘Authority’ decisions are made by relatively few individuals (who have power, status or

technical expertise) on behalf of others, or imposed upon others.

Rogers identified three important roles in the innovation process. ‘Opinion leaders’ had

relatively frequent informal influence over the behaviour of others. ‘Change agents’

positively influenced innovation decisions, by mediating between the agencies and the

participants. ‘Change aides’ complemented the change agents; they had more intensive

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V.Confirmatio

n

IV.Implementatio

nIII.

DecisionI.

KnowledgeII.

Persuasion

Communication channels

Prior conditions:1. Previous practice.2. Felt needs/problems.3. Innovativeness.4. Norms of the social systems

Characteristics of the decision-making unit:1. Socio-economic characteristics.2. Personality variables.3. Communication behaviour.

Perceived characteristics of the innovation:1. Relative advantage.2. Compatibility.3. Complexity.4. Trialability.5. Observability.

1. Adoption

2. Rejection

Continued adoptionLater adoption

DiscontinuanceLater rejection

Figure 4.2: Diffusion of Innovations Theory (Rogers, 2003).


contact with participants, and – though they may have had less competence credibility

– they may have engendered more trust and credibility.

Rogers’s earlier studies were based mostly in agriculture and medicine, but the ideas

were subsequently applied to diffusion of technology. Rogers suggested that adoption

goes through five stages: knowledge, persuasion, decision (adopt or reject),

implementation, and confirmation (continue or change decision). This is represented

diagrammatically in Error: Reference source not found).

<Key Point Model> Diffusion of Innovations Theory has some concepts useful to for

sustained crowdsourcing. Sustained use of a new technology will likely require

contributors to go through all five stages of the decision-making process (knowledge,

persuasion, decision, implementation, confirmation). To assist in that, a project may

benefit from people who can inspire contributors, and who can gain the trust of the

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contributors. <Key Point Model> Secondly, unlike some other models discussed later

in this thesis, Rogers’s model also clearly shows the possibility that a person who

decides to adopt an innovation may subsequently change his/her mind.

4.3.2. Implementation of an Innovation (Campbell and Masser, 1995)

Members of an organisation may be wary of an innovative system or technique when it

is introduced into an organisation. In their publications about the implementation of

GIS in local government, Campbell and Masser (1995) and Campbell (1996) discussed

four perspectives on people’s attitudes to the new technology.

First, ‘technological determinism’ proposes that if the benefits of a given innovation are

so transparent to all concerned, then gaining commitment is inevitable and success is

likely to be unproblematic. Should there be a failure, it would be a temporary setback

that merely needs some technical refinements. In the second perspective highlighted by

(Campbell and Masser, 1995), ‘economic determinism’, the most important aspect of

technical innovation is the economic benefit that should accrue. Campbell says (1996)

that – by failing to take into account social, political and organisational factors – this

perspective fails to measure up to real-world challenges. Those holding the third

perspective, ‘managerial rationalism’, would also be optimistic about realising the

benefits. However, this perspective recognises the importance of using appropriate

managerial techniques too. Those who hold this perspective might emphasise strategy

formulation and planning a sequence of steps that should lead to success. Campbell

claims that innovations introduced using these three objective perspectives seldom have

positive outcomes.

Campbell’s fourth perspective is ‘social interactionism’; it identifies a complex

relationship between the innovation and its context, such as corporate cultures, societal

norms and so on. Those who hold this fourth perspective may place a lower value on

the objective evaluations; much more important would be each individual’s assessment

of the threats and opportunities posed by the innovation.

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When implementing a technological innovation, the leaders need to ensure the

stakeholders’ perspectives are taken into account.

4.4. Decision-making and technology acceptance models

A key deliverable in this theses is a new technology acceptance model that builds on

the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2 – Venkatesh et

al., 2012). This model was derived from a family of models beginning in 1986 with

Davis’s Technology Acceptance Model (Davis et al., 1989). This – in turn – was

derived from Fishbein and Ajzen’s Theory of Reasoned Action (1975). Table 4.7 sets

out the timeline of these models.

Decision-making Technology acceptance1975 Theory of Reasoned Action1985 Theory of Planned

Behavior1986 Technology Acceptance Model1995 Combined TAM and TPB2000 Extended Technology Acceptance

Model2000 Technology Acceptance Model 22003 Unified Theory of Acceptance and Use

of Technology2008 Technology Acceptance Model 32009 Theory of Reasoned Action

22012 Unified Theory of Acceptance and Use

of Technology 2Table 4.7: Timeline for various models

An important feature of all of these models is the distinction between Behavioral

intention and Actual behavior‡‡ (the precise wording may differ between models). That

is, a person may indicate that he/she intends to do something, but in fact never actually

does or vice versa. There may be obstacles between the statement of intent and the

activity, either in the short or long term. This distinction will become important when

analysing the empirical case studies from East Africa in Chapter 6.

‡‡ Where a proper noun phrase from a model is used, such as Actual behavior, then the words are spelled

using the original author’s spelling (e.g. behavior); where those same words are used as normal nouns

then traditional UK spelling is preferred (e.g. behaviour).132

Beliefs and evaluations

Behavioral intention

Figure 4.3: Theory of Reasoned Action (Fishbein and Ajzen, 1975).

Actual behavior

Attitude toward behavior

Normative beliefs and motivation to comply

Subjective norm


4.4.1. Theory of Reasoned Action (Fishbein and Ajzen, 1975)

The Theory of Reasoned Action (TRA) was originally published in 1975 by Fishbein

and Ajzen’s and has since been refined over the decades. Venkatesh et al. (2003: p427)

describe it as “one of the most fundamental and influential theories of human

behaviour.” The original model suggested simply that Behavioral intention was a result

of attitude and subjective norm. Attitude is belief about the consequences of performing

an action multiplied by the evaluation of the outcome. Subjective norm is about

perceived expectations of related participants, such as colleagues and families; “the

person's perception that most people who are important to him/her think he should or

should not perform the behavior in question” (Fishbein and Ajzen, 1975: p302). The

relative weight of these two factors (attitude and subjective norm) will vary from

person to person. Clearly there is much more to this Theory, but at its simplest it can be

represented with this formula:

BI = (AB)W1 + (SN)W2

Where BI is the behavioural norm, AB is attitude towards performing the behaviour,

and SN is the subjective norm. W1 and W2 are the relative weights of these factors.

Because TRA is a general psychological decision-making model, the types of beliefs

and attitudes will differ depending on the circumstances; the user has to determine them

for a given circumstance.

133

Behavioral beliefs

Normative beliefs

Control beliefs

Attitudes towards the behavior

Subjective norm

Perceived behavioural control

Intention

Behavior

Figure 4.4: Theory of Planned Behavior (Ajzen, 1985).


4.4.2. Theory of Planned Behavior (Ajzen, 1985)Ajzen’s Theory of Planned Behavior (TPB) model (1985) is a development of Fishbein

and Ajzen’s Theory of Reasoned Action (1975). In addition to Attitudes and Subjective

norm seen previously, Ajzen added Perceived behavioral control as a determinant of

Intention and Behavior. Perceived behavioral control is the subject’s assessment of

whether there are sufficient resources (time, money, skills etc.), “their confidence in

their ability to perform” (Ajzen, 1991: p184). Ajzen notes that it is the subject’s

perception of control that influences his/her intention rather than any actual controls.

Ajzen further developed the idea of aggregating multiple factors; he noted that those

factors should ideally be observed in different situations at different times so that other

sources of influence will tend to cancel each other out. His resulting model is claimed

to present a more valid measure of the underlying behavioural attitudes.

TPB originally hypothesised that Attitudes, Subjective norm and Perceived behavioral

control are each determined by a set of beliefs. Behavioural beliefs are things that a

person believes to be true about the consequences of an action or activity; it is often

about the perceived utility of an action. Normative beliefs are things people believe are

normal in their situation, such as national, societal and corporate culture. Control

beliefs relate to the basic enablers of an action, such as whether there is adequate time,

finance and skill.

<Key Point Model> Ajzen (1991) accepted that the ability of this model to predict

actual behaviour was dependent on stable conditions. That is, Intention and Perceived

134


behavioral control must remain stable in the interval between assessing them and

measuring Behavior. <Key Point Model> Similarly, the Perceived behavioral control

must not differ much from actual behavioural control if the model is to work

satisfactorily. That is, although perception about adequate time and finances are

important, and don’t have to be perfect, if resources in reality are insufficient then

actual behaviour may nevertheless become impossible. It had been thought that the

three beliefs could be explained using expectancy-value formulas:

Attitude towards behavior: A∝∑ b i ei

[...] the strength of each salient belief (b) is combined in a multiplicative fashion

with the subjective evaluation (e) of the belief’s attribute, and the resulting

products are summed over the n salient beliefs. A person’s attitude (A) is

directly proportional (%) to this summative belief index. (Ajzen, 1991: p191)

Subjective norm: SN∝∑ ni mi

The strength of each normative belief (n) is multiplied by the person’s

motivation to comply (m) with the referent in question, and the subjective norm

(SN) is directly proportional to the sum of the resulting products. (ibid: p195)

Perceived behavioral control: PBC∝∑

i=1

n

c i p i

Specifically, [...] each control belief (c) is multiplied by the perceived power (p)

of the particular control factor to facilitate or inhibit performance of the

behavior, and the resulting products are summed across the n salient control

beliefs to produce the perception of behavioral control (PBC). (ibid: p197)

In the same article Ajzen (1991) reviewed the results of a range of studies that used the

TPB. He found that the expectancy-value formulas for the three ‘beliefs’ did not

135


establish a clear link with measures of their associated attitude, subjective norm or

perceived control. His conclusion was that the expectancy-value formulas were not

good measures of a person’s beliefs.

4.4.3. Technology Acceptance Model (Davis et al., 1989)

The journal article Davis et al. (1989) explains how Davis had derived the Technology

Acceptance Model (TAM) from TRA in his unpublished PhD dissertation of 1986.

Davis adapted the model specifically to explain acceptance of technology. He

populated the Beliefs and evaluations component to show how a person’s attitude

toward using technology is affected by Perceived usefulness (PU) and Perceived ease

of use (PEOU), that is, assessments of the benefits that motivate use and the (absence

of) obstacles to use.

On the other hand he decided to remove Subjective norm, which is the product of a

person’s perceived expectations of his/her referent groups, and his/her motivation to

comply. The concept of Behavioral intention is important to this study; Behavioral

intention is, “the degree to which a person has formulated conscious plans to perform

or not perform some specified future behavior” (ibid: p214). Based on the results of

various studies, Davis found it difficult to disentangle the direct effects of Subjective

norm as a determinant of Behavioral intention from indirect effects via Attitude. The

situation is complicated by the way Attitude may be the result of the ‘false consensus’

effect in which people project their own attitudes on others (Davis et al., 1989: p986).

Fishbein and Ajzen (1975: p304) confirm that the role of Subjective norm in TRA was

not well understood. Davis’s resulting model is shown in Error: Reference source not

found.

136

External variables

Perceived usefulness (PU)

Perceived ease of use (PEOU)

Attitude toward using

Behavioral intention to use

Figure 4.5: Technology Acceptance Model (Davis et al., 1989).

Actual system use


Davis (1989) conducted two field studies to assess the reliability and validity of the

model and its scales. The first study questioned 184 respondents within IBM’s Toronto

Development Laboratory. Respondents had an average of six months’ experience using

the computer systems under consideration. The second study involved 40 paid

participants who were part-time MBA students at Boston University. They were

questioned about their PU, PEOU, and intention to use specific software relating to

their course; subsequently their actual use was recorded and correlated.

A potential limitation of the research conducted by Davis was that all respondents were

drawn from a relatively narrow social base, i.e. Western, well educated, technically

literate and so on.

In study 1 (184 IBM users, ibid: p326), Davis found that PU and Actual system use

correlated at .63. PEOU and Actual system use correlated at only .45. In study 2 (40

MBA students, ibid: p330) Davis found that PU was significantly correlated with

Behavioral intention... The result for correlating PEOU was less clear; for one system it

was not significant, for a second system it was significant. Across both systems PU and

Actual system use correlated at .85. PEOU and Actual system use correlated at .59. All

of these correlations were significant at the p<.001 level.

137


In a separate paper, Davis et al. (1989) conducted a survey among MBA students at the

University of Michigan. In this study, Actual system use was measured at two points:

once soon after the study and again at the end of the semester, 14 weeks later.

The results from study 3 (40 MBA students, ibid: pp993-994) confirmed that the key to

Actual system use was to establish PU. This had a significant effect on Attitude, (β=.61

and β=.5 at the first and second assessments, p<.001). Once Attitude was set,

Behavioral intention and Actual system use tended to follow (the three items lying in a

chain in Error: Reference source not found); the authors reported a high correlation

between Attitude, Behavioral intention and Actual system use in the study. PEOU was a

secondary determinant in the short term (β=0.2, p<.01 at time 1), but in due course

people could overcome difficulties with using the technologies (β=-.11, non-significant

at time 2).

<Key Point Model> Various studies confirmed that there was a high correlation

between Behavioral intention and Actual system use; in these studies, what people said

and what they did tended to be the same. However, all these studies were based in

industrialised countries and situations where there was a degree of compulsion (e.g.

Davis, 1989; Moon and Kim, 2001; Venkatesh and Davis, 2000, Lay et al., 2013).

Conversely, in the case studies documented in Chapter 6 there was a significant

disparity between stated Behavioral intention and Actual system use. Interviewees who

have worked in developing countries confirm that stated intentions and actual

behaviour in East Africa often differ significantly (same chapter). This suggests that

cultural differences can be significant on how the TAM works.

Published applications of TAM to GISLoukis et al. (2010) used TAM to help evaluate the usefulness of GIS, digital maps and

geo-referenced multimedia in e-participation, that is, online participation in

government. This study focussed on spatial planning, environmental and energy issues.

The researchers developed an e-participation system which allowed citizens and

government organisations to share multimedia content that they generated themselves

(such as a photograph or video) that showed something of concern or supports an

opinion about a geographic location. The system allowed users to search for relevant

138


content uploaded by others. The system also supported discussion forums and online

petitions.

Loukis et al. conducted five pilot studies in Greece, Czech Republic, Slovakia, UK and

the Netherlands. From the study in Greece they found that the main stakeholder groups

were reluctant to adopt the system; many did not believe that changing the means of

debating an issue would lead to higher citizen empowerment. Usage tended to be by

younger and more computer-literate citizens, but not representative of the local

population. In the Czech study, among other things they found that it was important to

gain a ‘critical mass’ of both participants and relevant content. The team had many

promotional campaigns to locate a sufficient number of participants, though in the end

this proved successful. The UK citizens found the discussion forums useful, but the

geo-referenced multimedia less so. Both the UK and Czech study concluded that if the

materials appended to the map are limited in quantity and quality, it dissuades citizens

from contributing more. Among the overall conclusions, they found, “The use of maps

to pinpoint relevant data seems to be a well-accepted practice by citizen users in order

to broaden their understanding of complex local problems and spatial suggestions.”

(ibid: p12)

Lay et al. (2013) used TAM to explore GIS usage among 719 geography teachers in

senior high schools in Taiwan (nearly half of all such teachers). Somewhat surprisingly,

they found that PEOU did not have a direct effect on Actual system use (-.086, non-

significant), though there was an indirect effect in that PEOU was a factor in PU and so

had a beneficial effect on Actual system use (.178, p<.001). The strongest link was

found in the simpler path between PU and Actual system use (.301, p<.001).

Lay et al.’s 2013 findings may not be applicable in wider contexts. For example,

PEOU was not directly relevant to Actual system use, perhaps because a teacher would

seek to overcome difficulties if there was a good enough reason. Conversely, a

teacher’s assessment of how useful a GIS is (PU) may be partly dependent on PEOU,

that is, if it is difficult to use then pupils will be reluctant to use it and this will be

problematic for the teacher. This means that PEOU was indirectly relevant to Actual

system use. In other contexts, PU may be assessed more in terms of what can be

achieved with the functionality. 139


Subjective norm


Behavior intention

Actual behavior

Figure 4.6: Combined TAM and TPB (Taylor and Todd, 1995).

Perceived usefulness

Perceived ease of use

Peer influence

Superior’s influence

Self-efficacy

Resource facilitation

Technology facilitation

(Constructs shown in bold are new features.)


TAM has been extended in other ways, unrelated to GIS. For example, Liu et al. (2010)

extended TAM for online learning.

4.4.4. Combined TAM and TPB (Taylor and Todd, 1995)

Taylor and Todd (1995) drew together concepts from Theory of Planned Behavior and

the Technology Acceptance Model. Error: Reference source not found shows how the

Combined TAM and TPB (C-TAM-TPB) model enumerates the three ‘beliefs’

constructs on the left-hand side of Ajzen’s TPB model using concepts from Davis’s

TAM and elsewhere.

Taylor and Todd highlight that TAM was tested with measures of self-reported usage

rather than actual usage. They also note that the whole model was not tested

simultaneously; rather that the various parts were examined separately using statistical

140


regression techniques. The C-TAM-TPB model was tested against TAM and TPB

using a survey of 786 undergraduate and MBA students of the Business School at

Queen’s University, Kingston, Ontario, Canada. The results showed:

TAM accounted for 52% of variance in intention, and 34% in behaviour;

TPB accounted for 57% of variance in intention, and 34% in behaviour,

o Consequently the addition of Perceived behavioral control did not

improve the understanding of behaviour compared with TAM.

C-TAM-TPB accounted for 60% of variance in intention, and 36% in

behaviour, a small improvement over the separate models.

Perhaps because the C-TAM-TPB study found that Perceived behavioral control was

not a major determinant of Actual behavior, this construct does not feature in the next

few models, and re-appears only in the much later Unified Theory of Acceptance and

Use of Technology (Venkatesh et al., 2003: see 4.4.8).

4.4.5. Extended TAM (Moon and Kim, 2001)Moon and Kim (2001) recognised that most research into TAM had been conducted

from an extrinsic motivation perspective, but apart from Davis et al. (1992), there had

been little or no emphasis on intrinsic motivation§§. One deficiency identified by Moon

and Kim is that TAM did not work well outside the workplace. They claimed that this

was because TAM’s fundamental constructs did not fully reflect the variety of user task

environments (ibid: p218). Moon and Kim produced Extended TAM to model the

public’s use of the Web. Building on the work of Davis et al. (1992), Moon and Kim

proposed that Perceived playfulness more comprehensively addressed aspects of

intrinsic motivation such as activity absorption, curiosity and arousal. This additional

factor is shown in Error: Reference source not found.

§§ Intrinsic motivation is where fulfilment is generated by the activity itself, and extrinsic motivation is

where an activity is undertaken to achieve a desired outcome; see Section below.141


Perceived playfulness

Attitude toward using


Figure 4.7: Extended Technology Acceptance Model (Moon and Kim, 2001).

Actual system use



They defined three dimensions of Perceived playfulness (Moon and Kim, 2001: pp219-

220):

concentration: the individual perceives that his or her attention is focused on the

interaction with the Web;

curiosity: the individual is curious during the interaction; and

enjoyment: the individual finds the interaction intrinsically enjoyable or

interesting.

Moon and Kim conducted regression analysis to compare Extended TAM’s overall

predictive fit (adjusted R2) to the results of a study of 152 graduate students in the

School of Management. Behavioral intention to use was found to be significantly

related to all three determinants (Perceived usefulness, ease of use and playfulness) at

R2=.394, and to each individual determinant at β=.285, β=.269 and β=.245 respectively

(p<.001). Actual use was also related to Behavioral intention at R2=.378 and β=.615

(p<.001).

Using the same data, Moon and Kim compared Extended TAM with the original TAM.

They found that Extended TAM explained 39% of variance in Behavioral intention to

use, but original TAM accounted for 35% of the variance (though was still significant).

Thus, Extended TAM explained individuals’ Web acceptance behaviours better than

original TAM.142


The researchers then analysed the study group depending on whether the task was work

related or entertainment related (ibid). For the 55 conducting work-related tasks, PU

had a more significant effect (β=0.409) than Perceived playfulness (β=0.239). For the

87 conducting entertainment-related tasks, Perceived playfulness had a more significant

effect (β=0.491) on Behavioral intention than PU (β=0.202). Perceived playfulness

was found to be significant for both groups, which may be considered surprising.

The concept of playfulness was subsequently adopted into TAM3 (Venkatesh and Bala,

2008: see section 4.4.7), but as an external variable rather than a key determinant in

itself.

Published applications of Extended TAM to GISChang and Caneday (2011) outlined how TAM, and in particular Extended TAM that

includes Perceived playfulness, can enhance understanding of how tourists are

motivated to use online maps such as Google Maps when planning vacations. 155

responses (out of 1265 approaches) indicated that PU determines the frequency of

usage, and Perceived playfulness may decide the depth and duration of utilisation in

tourism information searches. Perceived playfulness increased intrinsic motivation,

especially during the initial exploratory stages.

4.4.6. Technology Acceptance Model 2 (Venkatesh and Davis, 2000)

In the original TAM (Davis et al., 1989) PU and PEOU were determined by

unspecified External variables. In Technology Acceptance Model 2 (TAM2),

Venkatesh and Davis (2000) specified the external variables that determined PU in

more detail as shown in Error: Reference source not found.

Subjective norm acknowledges the influence of peers on whether the subject would use

the technology. Subjective norm can be affected by external variables: Voluntariness

(the extent to which the user may choose to use the technology) and by Experience.

Subjective norm contributes to the Image or status of the individual. Job relevance is a

measure of the tasks that can be performed with the system. Related to this, Output

quality is an assessment of how well the system performs the tasks. Finally, Result 143

Subjective norm



Intention to use

Figure 4.8: Technology Acceptance Model 2 (based on Venkatesh and Davis, 2000)

Usage behavior

Image

Job relevance

Output quality

Result demonstrability

Experience

Voluntariness


demonstrability relates to how tangible the results are as a result of using a technology

(ibid). These variables are related solely to the PU construct of TAM2. However, the

TAM2 extensions do not address the PEOU construct; Venkatesh and Bala addressed

this in TAM3 (see section 4.4.7).

Attitude no longer appeared in this model. The Subjective norm construct originally

appeared in TRA but not in the original TAM. It reappeared in TAM2 as one of the

external variables.

4.4.7. Technology Acceptance Model 3 (Venkatesh and Bala, 2008)

In 1996 (before TAM2) Venkatesh and Davis began work on identifying the external

variables affecting PEOU. Their initial experiments (n=108) hypothesised two factors:

computer self-efficacy and objective usability (definitions given below). The results of

these initial experiments confirmed that an individual's perception of a particular

144

Subjective norm



Intention to use

Figure 4.9: Technology Acceptance Model 3 (based on Venkatesh and Bala, 2008).

Usage behavior

Image

Job relevance

Output quality

Result demonstrability

Experience Voluntariness

Computer self-efficacy

Perceptions of external control

Computer anxiety

Computer playfulness

Perceived enjoymentObjective usability


system's ease of use was greatly affected by computer self-efficacy; objective usability

had an impact only after direct experience with the system.

Building on the 1996 experiments, Venkatesh and Bala (2008) produced a wider set of

external variables that determined PEOU. They combined them with TAM2’s variables

to give the diagram in Error: Reference source not found.

Venkatesh and Bala (2008: p280) define the new external variables as follows:

145


‘Anchor’ variables:

o Computer self-efficacy: The degree to which an individual believes that

he or she has the ability to perform specific task/job using [the

technology].

o Perception of external control: The degree to which an individual

believes that an organizational and technical infrastructure exists to

support use of the system.

o Computer anxiety: The degree of an individual’s apprehension, or even

fear, when she/he is faced with the possibility of using [the technology].

o Computer playfulness: The degree of cognitive spontaneity in...

interactions [with the technology].

‘Adjustment’ variables:

o Perceived enjoyment: The extent to which the activity of using a specific

system is perceived to be enjoyable in its own right, aside from any

performance consequences resulting from system use.

o Objective usability: A comparison of systems based on the actual level

(rather than perceptions) of effort required to complete specific tasks.

Published adaptation of TAM3Lindsay et al. (2011) adapted TAM3 to model the acceptance of mobile technologies

within the UK police force. In their pilot study, they shadowed some 30 participants as

they used mobile technologies for recording and reporting incidents. In the main study,

48 trained police officers gave qualitative feedback in focus groups about factors that

encouraged and discouraged them from using mobile devices. The factors were

recorded down the left-hand side of a table, the TAM3 constructs were set across the

top of the table, and where a factor was considered to be covered by a TAM3 construct,

a ‘Y’ was entered, as indicated in Table 4.8. Factors found not to be supported by

TAM3 were added to their new m-TAM model, grouped as security/reliability,

performance, management style or cognitive acceptance factors (ibid: p399). The

original version of m-TAM had 28 constructs (ibid: p404); a later revised version had

33 constructs (Lindsay et al., 2014).

146


Overarching theme

MDT officer acceptance factors

PU PEOU Subjective norm

Etc.

Operational performance factors

Officer performance

Y

Functionality YData inputting qualityEtc.

Security and reliability factors

Reliability Y

Security YInterface design Y

Etc.Table 4.8 Sample extract from Technology acceptance categories and factor mapping (Lindsay et al., 2011)

Lindsay’s adaptation has a special relevance to this project as the qualitative approach

of mapping observed factors onto a technology acceptance model is the same approach

used in Chapter 7 to derive the new Theory of Acceptance and Sustained Use of

Technology (TASUT) model.

4.4.8. Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003)

The Unified Theory of Acceptance and Use of Technology (UTAUT) was proposed by

Venkatesh et al. (2003) following a review of the Diffusion of Innovations theory, the

Theory of Reasoned Action, the Theory of Planned Behavior, the various TAMs, C-

TAM-TPB (described previously), the Motivation Model (see section 4.5), plus the

Model of PC Utilization (Thompson et al., 1991) and the Social Cognitive Theory

(Bandura 1986). Venkatesh et al. conducted four studies, and in each they measured the

subjects’ intentions and actual use one week after being introduced to the technology,

one month later, 3 months later, and finally 6 months later solely to assess actual use.

Using the constructs from each of the above-mentioned models, the authors found the

following had a very significant or highly significant effect on Behavioral intention as

shown in Table 4.9.

147


n=645 pooled from the four studies. Key: *** p<.001, ** p<.01. (p<.05 have been omitted for brevity.)

Model Behavioral intention

Significance

Notes

TRA Attitude (A) ***TAM2 Perceived

usefulness (PU)***

PEOU x EXP ** Ease of use becomes less important as experience is gained.

SN x EXP x VOL **TAM2 with gender

PU x GDR *** Usefulness more important among men

PEOU x GDR ** Greater for womenSN x GDR x EXP

** Greater for women but diminishes with increasing experience

Motivation Model Extrinsic ***Intrinsic ***

TPB with voluntariness

A ***

SN x EXP ** Diminishes with experience

SN x VOL ** Only in mandatory settings

TPB with gender A ***A x GDR *** Greater for menSN x GDR x EXP

** Greater for women but diminishes with increasing experience

TPB with age A ***A x AGE *** Greater for younger

workersPBC x AGE ** Greater for older

workersSN x AGE x EXP ** Greater for older

workers, but diminishes with increasing experience

C-TAM-TPB PU ***PBC x EXP ** Diminishes with

increasing experienceMPCU Job-fit ***

Complexity x EXP

** Diminishes with increasing experience

Diffusion of Innovations

Relative advantage

***

Social Cognitive Theory

Outcome expectations

***

Table 4.9: Effect of predictors on Behavioral intention. (Based on Venkatesh et al., 2003)

148


What may be considered notable is the lack of significant effect many of the

determinants had on Behavioral intention. For example, Subjective norm appears in

many models, but no study found it to be significant; Ease of use appears in some

models but similarly was not found to be significant except in conjunction with other

variables.

Some models did not perform at all well. In Diffusion of Innovations, no significant

effect was found from Ease of use, Result demonstrability, Trialability, Visibility,

Image, Compatibility, Voluntariness of use, or Experience. Only the relatively simple

Motivation Model performed well, where its only two factors – Extrinsic motivation

and Intrinsic motivation – were both found to be significant.

Most of the models considered had Behavioral intention as the only predictor of actual

Use behavior; some models also had Perceived behavioral control as a predictor. The

results were similar whether the technology was voluntary or mandatory: Behavioral

intention was found to be highly significant; Perceived behavioral control was only

slightly significant at 6 months, as shown in Table 4.10.

n=645. Key: *** p<.001, * p<.05

1 month 3 months

6 months

Studies of Behavioral intention to use

.61*** .60*** .58***

voluntary use

Perceived behavioral control

.04 .06 .17*

Studies of Behavioral intention to use

.58*** .61*** .56***

mandatory use


.07 .07 .20*

Table 4.10: Effect of predictors on Use behavior. (Based on Venkatesh et al., 2003)

Venkatesh et al.’s proposed model (2003) aimed to address larger organisational

systems, rather than the personal types of software addressed by previous trials.

149

Performance expectancy


Figure 4.10: Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003).

Use behavior

Effort expectancy

Social influence

Facilitating conditions

Voluntariness of use

Experience

Age

Gender

Moderating variables

R2=.76 R2=.53


Error: Reference source not found shows how extensively this model differs from the

various TAMs. It shows four main determinants of Behavioral intention and Use

behavior, rather than just two. They are defined as (Venkatesh et al., 2003: pp447-453):

Performance expectancy: The degree to which users believe that using the

system will help them improve their performance (similar to PU and some

somewhat similar to Extrinsic motivation).

Effort expectancy: The degree of ease associated with using the technology

(similar to PEOU).

Social influence: The degree to which the users perceive that their important

peers believe they should use the technology (similar to Subjective norm and

Image).

Facilitating conditions: the degree to which an individual believes that the

organisational and technical infrastructure exist to support use of the technology

(in some respects similar to Perceived behavioral control and Compatibility).

Four key moderating variables were added: Gender and Age had not appeared in

previous models; Experience and Voluntariness (of use) were external variables first

seen in TAM2.

150


TRA and TRA2 TPB TAM, TAM2 and TAM3

UTAUT and UTAUT2

Attitudes towards behavior

Attitudes towards behavior




Effort expectancy

Subjective norm Subjective norm Subjective normImage

Social influence

Perceived behavioral controlControl beliefs

Perceptions of external control


Computer playfulness/Perceived playfulness

Hedonic motivation

Experience ExperienceVoluntariness Voluntariness of

useBeliefs and evaluations

Behavioral beliefs


Normative beliefs


Intention (Behavioral) intention to use


Actual behavior Behavior Actual system use(Usage behavior)

Use behavior

Table 4.11: Broadly equivalent constructs among the various models.

After validating the model using the previously gathered data, Venkatesh et al.

conducted two further studies (n=80 and n=53). They found strong empirical support

for UTAUT, in particular:

three direct determinants of Behavioral intention were Performance

expectancy, Effort expectancy, and Social influence;

two direct determinants of Use behaviour were Behavioral intention and

Facilitating conditions,

o few other acceptance models reviewed here considered any determinants

of use other than intention, so this is a key improvement;

the other four variables – Gender, Age, Experience and Voluntariness of use –

were found to be significant moderating influences.

151



Figure 4.11: Research model used by Bandyopadhyay and Fraccastoro (2007) and by Bandyopadhyay and Bandyopadhyay (2010).

Effort expectancy

Social influence

Voluntariness of use

Experience

AgeGender

Income


Venkatesh et al. (2003) note that Performance expectancy is the strongest factor in

intention, especially for men and younger workers. Effort expectancy becomes more

significant for women and older workers, but decrease with experience. The inclusion

of Social influence depended very much on the moderating variables; its effect was

insignificant without the influence of those moderators. Finally, Facilitating conditions

was found to be significant only for older workers who had a great deal of experience.

Published applications of UTAUT in other cultural settingsBandyopadhyay and Fraccastoro’s 2007 article claims to be the first paper to test

UTAUT outside the USA. Their 2007 study examined the acceptance of smart card

prepayment electricity metering systems in India, and the Bandyopadhyay and

Bandyopadhyay study of 2010 compared the India results with the USA. Although the

papers claimed to use UTAUT, in fact their research model omitted – without

explanation – the Facilitating conditions and Use behavior constructs, and added

Income as a moderator, as shown in Error: Reference source not found

The 2010 comparison found that their model provided a good fit for predicting

Behavioral intention in both India (R2=72%) and the USA (R2=86%). Interestingly, the

152


effect of Effort expectancy on intention was not found to be significant in the USA,

unlike in previous studies by Venkatesh etc.

Gupta et al. (2008) applied UTAUT in a study of 102 employees of a government

organisation in India. They found that most of UTAUT was supported by the responses,

perhaps at a lower level of statistical significance, but a noteworthy finding was that

there was no significant relationship between Behavioral intention and Use behavior (a

finding repeated in the empirical studies in Chapter 6.)

Published applications of UTAUT to GIS in other cultural settingsYan Sun’s 2007 dissertation proposed modifying UTAUT to model the implementation

of GIS into a Chinese swine farming business. She found that Gender and Age and

Voluntariness of use were of less importance, Experience remained important, and she

found a new factor – Education level – was a significant moderator of effect. She also

introduced Telecommunication infrastructure as a new determinant of Use behavior,

which she calls an ‘environmental’ determinant.

4.4.9. Unified Theory of Acceptance and Use of Technology 2 (Venkatesh et al., 2012)

A variety of studies extended and enhanced UTAUT for use in other contexts, or to

expand the theoretical mechanisms, or to include other predictors (see Venkatesh et al.,

2012: p158 for a summary). Development of UTAUT2 paid particular attention to the

consumer use context. So, where Price value was not considered in studies of students’

acceptance of technology in a university lab, it was found to be of great significance to

consumers purchasing their own technology.

Two other new constructs were added to the old version of UTAUT (see Error:

Reference source not found). Hedonic motivation is defined by Venkatesh at al. (ibid:

p164) as the fun or pleasure derived from using a technology. Hedonic motivation was

found to be key to Vested Interest Theory (Crano and Prislin, 1995). It is similar to

constructs already seen, such as Perceived playfulness in Extended TAM (Moon and

Kim, 2001) and Computer playfulness in TAM3 (Venkatesh and Bala, 2008).

153


The final new construct was Habit. This was based on work by Davis and Venkatesh

(2004), Kim et al. (2005), Limayem et al. (2007) and others: studies had found that

frequently performed past behaviour was a significant determinant of future behaviour.

An important characteristic of Habit in UTAUT2 is that it is a determinant of

behaviour, not just of intention as is the case for most other determinants. This is

crucial since – as we shall see in the interviews and fieldwork documented in Chapter 6

– there was a weak relationship between intention and behaviour, and consequently a

need to identify other determinants.

The effect of Habit can be either positive or negative. Habit has some apparent

similarities with Experience, but Habit is prior behaviour and is measured as “the

extent to which an individual believes the behaviour to be automatic” (Venkatesh et al.,

2012: p165). Experience is a necessary but not sufficient condition for the formation of

Habit; over a given period of time (experience) different people will form a different

level of habit.

Three of the four UTAUT moderators were retained, but Voluntariness was dropped

because consumers have no organisational mandate (Venkatesh et al., 2012).

154

Performance expectancy 1


Figure 4.12: Unified Theory of Acceptance and Use of Technology 2 (Venkatesh et al., 2012).

Use behavior

Effort expectancy 2

Social influence 3

Facilitating conditions 4

Experience

AgeGender Moderating variables

Hedonic motivation

Price value

Habit

Notes:1. Moderated by age and gender.2. Moderated by age, gender and experience. 3. Moderated by age, gender and experience.4. Effect on use behaviour is moderated by age and experience.

R2=.74

R2=.52


An unusual feature of the original UTAUT was that the Facilitating conditions

construct was shown as a direct determinant of Use behavior only, but not of

Behavioral intention. In UTAUT2, Facilitating conditions remains a determinant of

Use behaviour but is now also found to be a determinant of Behavioral intention.

Venkatesh et al.’s study was conducted (in Chinese) among users of mobile Internet

technology in Hong Kong. Table 4.12 sets out the results on Behavioral intention.

155


n=1512. Key: *** p<.001, ** p<.01, * p<.05 (empty cell means no significant effect)neg = inversely proportional.D = Direct effects only. D+I = Direct effects and interaction terms.


UTAUT UTAUT2 Notes

D D+I D D+IR2 .35 .56 .44 .74Adjusted R2 .35 .55 .44 .73 Significant paths only.


*** ***

Effort expectancy *** ** ***Social influence *** *Facilitating conditions ** ***Hedonic motivation ***Price value *Habit ***Performance expectancy moderated by gender and age

*** *** Most important to older women.

Facilitating conditions moderated by gender and age

*** Most important to older women.

Price value moderated by gender and age

* neg

Most important to older women.

Social influence moderated by age and experience

** *

Effort expectancy moderated by gender, age and experience

** neg

* neg

Stronger among younger men in early stages of experience.

Social influence moderated by gender, age and experience

*** neg

*** neg


Hedonic motivation moderated by gender, age and experience

*** neg


Habit moderated by gender, age and experience

*** neg

Active among older men in later stages of experience.

Table 4.12: UTAUT2 survey results on Behavioral intention. (Based on Venkatesh et al., 2012)

156


This suggests that UTAUT2 accounts for more variance in intention than the original

UTAUT. Table 4.13 indicates that UTAUT2 is also better at accounting for variance in

actual Use behavior.

n=1512. Key: *** p<.001, ** p<.01, * p<.05 (empty cell means no significant effect) neg = inversely proportional.D = Direct effects only. D+I = Direct effects and interaction terms.

Use behavior UTAUT UTAUT2 NotesD D+I D D+I

R2 and Adjusted R2 .26 .40 .35 .52

Behavioral intention *** *** ***Habit *** **Facilitating conditions ** *Behavioral intention moderated by Experience

** neg

As experience grows, original intention becomes less important.

Facilitating conditions moderate by age and experience

*** ** Conditions are more important among older, more experienced users.

Habit moderated by gender, age and experience

*** neg

Active among older men in later stages of experience.

Table 4.13: UTAUT2 survey results on Use behavior. (Based on Venkatesh et al., 2012)

Regarding Behavioral intention:

Overall:

o ignoring interaction terms, the direct effects of UTAUT2 explained 44%

of the variance;

o including interaction terms boosted that to 74% of the variance in

Behavioral intention;

o these represent significant improvements in variance explained

compared with the original UTAUT.

Hedonic motivation was stronger among younger men in early stages of

experience.

Price value was most important to older women.

Habit was most active among older men in later stages of experience.

157


Regarding Use behavior:

Overall:

o again if we ignore interaction, UTAUT2 explained 35%;

o including interaction terms boosted that to 52% of the variance in Use

behavior;

o these represent significant jumps in variance explained compared with

the original UTAUT.

Habit was – again – most active among older men in later stages of experience.

As Experience grows, the effect of original Behavioral intention becomes less

important.

Important: Several of the previous models showed Behavioral intention as the sole

determinant of Use behavior. Under UTAUT2 it was found that this relationship

applies only among inexperienced users. This in turn demonstrates the dangers of

relying on surveys conducted solely among inexperienced users such as university

students when using an unfamiliar technology.

4.4.10. Theory of Reasoned Action 2 (Fishbein and Ajzen (2009)

This decision-making model was published after the various TAMs and UTAUT, so it

is worth considering whether it reveals anything that the technology acceptance models

may have missed. Ajzen (1985) added Control Beliefs and Perceived Behavioral

Control to original TRA to produce his TPB. Fishbein and Ajzen’s current Theory of

Reasoned Action 2 (TRA2) model (2009) rephrases the original TRA model to adopt

from Ajzen’s Planned Behavior the concepts of behavioural, normative and control

beliefs in predicting human actions.

All of the behavioral and technology acceptance models considered so far have been

directed graphs, that is, there are no iterations. TRA2 (Error: Reference source not

found) is the only one that shows iteration: the outcomes from actual Behavior can

affect (reinforce or contradict) previously held Behavioral beliefs and Control beliefs.

Such reinforcement is not shown overtly in any of the technology acceptance models.

158


Subjective norm


Intention

Behavior

Figure 4.13: Theory of Reasoned Action 2 (Fishbein and Ajzen (2009).

Behavioral beliefs

Normative beliefs

Control beliefs

Background factors

Actual control


Like other models, TRA2 distinguishes between intention and actual behaviour. Unlike

the other models, Fishbein and Ajzen develop some useful insights into why intentions

and actual behaviour may differ. These can include (Fishbein and Ajzen, 2009: pp56-

60):

Actual control moderates the effect of Intention on Behavior, and feeds back

into Perceived behavioral control;

temporal instability, whereby a subject whose intention varies over time is less

likely to do what they originally indicated they would;

sequential hurdles, whereby the greater the number of steps to be completed

before being able to perform the task, the lower the correlation with intention;

volitional control, if they lack the skills or resources to undertake the action;

hypothetical versus real situations and pseudo-inconsistency, which are

variations of how real life can alter intention and so change the outcome

(sometimes positively, sometimes negatively).

159


<Key Point Model> Actual control is relevant as well as perceptions about control.

Although TRA2 was developed by psychologists and models generic decision making,

these insights might be applied to crowdsourced mapping, for example:

Asking about intentions on just one occasion will not help you determine who is

more susceptible to temporal instability, so it may not be a good indicator of

actual behaviour.

If someone has to borrow a GPS, travel some distance to the survey area, go

through several steps to record features onto the system, then they may be put

off.

o For example, before a user can enter features recorded on a GPS into

Google Map Maker, the user must first upload data from the GPS,

convert the track-log data format, load the converted track-log onto a

web server, and then enter codes into Google Map Maker for it to

superimpose the track-log onto the map. Sometimes the user must wait

for certain features to be moderated before being allowed to add others.

Even crowdsourced systems that are supposed to be easy to use can pose

obstacles to qualified, experienced users. Without anyone to turn to for help it

can be very discouraging. Also GPS devices and aerial images are relatively

expensive for members of the public.

It is easy to postpone disaster management activities and procrastinate. In a

society where simply making ends meet is difficult, investing time and effort to

reduce problems that may happen in the future – if at all – receive lower

priority.

4.4.11. Evaluation of the modelsThe various technology acceptance models were developed from positivist stand-

points, using deductive strategies and quantitative analysis of questionnaires that asked

the subjective opinions of respondents. A limitation of this approach is that the

researcher must propose possible factors for inclusion in the questionnaire. If a factor is

overlooked it will not appear in the questionnaire and therefore will not appear as a

significant factor or moderator. By way of example, the developers of TAM, TAM2,

160


TAM3 and UTAUT (1986 – 2003) didn’t consider Habit, so did not look for any effect

it had on Use behavior; habit was first considered in Davis and Venkatesh (2004) and

was found to be a key driver of behaviour. In the following chapter there is an

explanation for this project’s adoption of constructivist and advocacy stand-points,

using an abductive strategy based on qualitative data. This is proposed in order to

reveal new factors and dependencies.

A weakness of the technology acceptance models reviewed above is that they have had

relatively little impact outside of academia. It seems very few practitioners have heard

of them, and there is little guidance for practitioners on what the practical implications

would be if they did decide to use them. In order to help facilitate the application of the

proposed new model, a set of guidelines will be produced for managers of a

crowdsourced mapping initiative.

A second weakness of these models is that they fail to identify determinants of

sustained usage, which is of vital importance to a disaster preparedness initiative. Most

of the studies reviewed in this chapter recorded respondents’ intentions and actual use

at a single point in time so did not consider sustained use. Only Davis et al. (1989;

1992; 2004), Venkatesh et al. (2002; 2003) and Limayem et al. (2007) conducted

longitudinal studies but they lasted only a few months and focussed mostly on initial

adoption rather than sustained use.

A third weakness of many of the studies that have been reviewed*** is that few

considered any determinants of Actual behaviour/ Actual system use/ Use behaviour

other than intention. Their authors, it seems, did not consider which factors might cause

a person to change his/her mind or how those might be mitigated. Only Ajzen (1991),

Ajzen et al. (2006), Taylor and Todd (1995) and Venkatesh et al., (2003; 2012)

identified any other determinants of Use behavior. For example, Ajzen et al. (2006)

found that if subjects created a plan of action (‘implementation intention’) they were far

more likely to carry out an activity.

*** Fishbein and Ajzen, 1975; Ajzen, 1985; Davis et al.,1989; Loukis et al., 2010; Lay et al., 2013; Moon

and Kim, 2000; Chang and Caneday, 2011; Liu et al., 2010; Lindsay et al., 2011; Venkatesh and Davis,

2000; Venkatesh and Bala, 2008.161


Figure 4.14: Possibility of determinants of initial and sustained use behavior.

Use behavior

New determinants?

Existing determinants



New dependencies?

Sustained use behavior

New determinants?


It is also notable that many studies involved university students in industrialised

countries, and concerned the use of technology that was part of their respective courses

(summarised in Venkatesh et al., 2003: p436). Many studies were conducted after the

subjects had already used (or rejected) the technology for some time (ibid: p437), so the

high correlation between the self-reported Intention to use and actual Use behavior may

have been affected by subsequent actual experience. It is not clear the extent to which

the results are generalisable or applicable in other contexts, such as crowdsourcing or

East African society.

Could it be that some dependencies are missing between existing determinants and Use

behavior, or perhaps some other determinants have yet to be identified? Should initial

(short-term) use be shown separately from sustained (long-term) use, as seen in

Rogers’s Diffusion of Innovations? Perhaps more attention needs to be given to the

right-hand side of the diagram than has been the case hitherto, as suggested by the

emboldened constructs in Error: Reference source not found.

A candidate factor in moving from intention to use is motivation, which is considered

in the next section.

4.5. Motivation modelsA key to moving from intention to use is that the users need a motivation to use. Auf

Der Heide (1989: p15) set out a variety of reasons why organisations give low priority

to disaster planning. He adds, “The mention of these factors should not be taken to

162


Use behavior

Determinants

Determinants

Determinants

Determinants


Determinants

Figure 4.15: Orientation: Motivation as key to moving from Behavioral intention to (initial) Use behavior.

Self-actualisation

Esteem

Love/belonging

Safety

Physiological

Figure 4.16: Hierarchy of Needs (Maslow, 1943).


imply that, because disasters are improbable, effective countermeasures are not

practical. Rather, it is to point out that motivational issues need to be considered during

disaster planning.” The following sections identify what motivates people, that is, to

move from mere intention to actual use.

4.5.1. Hierarchy of Needs (Maslow, 1943, 1969)One of the early models of motivation (Maslow, 1943) identified five types of

motivation, arranged as layers, as in Error: Reference source not found.

He later added a sixth motivator, Self-transcendence, at the top above Self

actualisation. Self-transcendence is defined as motivation that goes outside oneself and

163


seeks “to further a cause beyond the self and to experience a communion beyond the

boundaries of the self through peak experience” (Maslow, 1969: p35).

<Key Point Model> Maslow (1943) proposed that there was little point appealing to the

higher-level needs if the lower-level needs were unmet. In the context of crowdsourced

mapping, while contributors from wealthier communities might produce GI for esteem

and self-actualisation, contributors from poorer communities may be concerned about

physiological (food, clothing and housing) and safety needs.

4.5.2. Intrinsic and extrinsic motivation (Ryan and Deci, 2000)

For an activity, motivators can be split in two main types: ‘intrinsic motivation’ and

‘extrinsic motivation’ (Ryan and Deci, 2000). Intrinsic motivation is where fulfilment

is generated by the activity itself (for example, it is a creative outlet or fascinating or

addictive). Extrinsic motivation is where an activity is undertaken to achieve a desired

outcome (for example, to earn money, to avoid sanctions, to build a reputation).

<Key Point Model> Kaufmann et al. (2011: p2) helpfully enumerated intrinsic and

extrinsic motivations:

Intrinsic motivation:

o enjoyment-based motivation;

o community-based motivation.

Extrinsic motivation:

o immediate payoffs;

o delayed payoffs;

o social motivation.

The concept of intrinsic and extrinsic motivation has been adopted in the domain of

technology acceptance, as seen next.

164

Figure 4.17: Motivation Model (based on concepts in Davis et al., 1992).

Extrinsic motivation

Intrinsic motivation


Usage behavior


Enjoyment

Perceived output quality

Perceived ease of use

Task importance

Moderating variable


4.5.3. Motivation Model (Davis et al., 1992)Davis et al. (1992) identify extrinsic and intrinsic motivation as important to

technology acceptance. Although the title of their paper, “Extrinsic and intrinsic

motivation to use computers in the workplace”, suggests a broad application of

motivation, the authors focus on only one extrinsic motivator and one intrinsic

motivator. They gave Perceived usefulness as an example of extrinsic motivation, and

Enjoyment as an example of intrinsic motivation, but then focussed solely on these.

This was perhaps a missed opportunity because they ignored a range of other possible

motivators. Davis et al. hypothesised that output quality would enhance both usefulness

and enjoyment, and similarly ease of use would make a system more productive

(therefore useful) and enjoyable. These effects on Perceived usefulness would be

moderated by the importance of the task, though. The authors did not represent the

model as a diagram, but their concepts are summarised in Error: Reference source not

found.

<Key Point Model> Davis et al. (1992) highlight that extrinsic motivation is important

when intrinsic motivation is low or absent. However, they reported on a variety of

published studies that show that introducing an extrinsic reward can diminish intrinsic

motivation where this was previously strong. This negative influence has been

explained in the context of Deci’s cognitive evaluation theory. The introduction of an

165


extrinsic reward shifts ‘locus of causality’ away from the internal to the external; that

locus of causality – along with feelings of competence and self-determination – are key

determinants of intrinsic motivation (Deci, 1975: pp139-142).

Venkatesh et al. (2003) conducted a study assessing a range of models, including this

Motivation Model. The details were discussed in section 4.4. It was notable that the

2003 results did not support large parts of the other models, but the Motivation Model

was the only one where all of its constructs proved highly significant on people’s

intentions.

4.5.4. Motivation to participate in crowdsourcing activities (Coleman et al., 2009)

Coleman et al. (2009: pp342-343) summarised the observations of research into what

motivates people to contribute to crowdsourcing initiatives such as Wikipedia and

Open Source Software development. They suggested this list of motivators:

Generous or constructive:

(1) Altruism.

(2) Professional or Personal Interest.

(3) Intellectual Stimulation.

(4) Protection or enhancement of a personal investment.

(5) Social Reward.

(6) Enhanced Personal Reputation.

(7) Provides an Outlet for creative & independent self-expression.

(8) Pride of Place.

Selfish or destructive:

(9) Mischief.

(10) Social, economic or political agenda.

(11) Malice and/or Criminal Intent.

Clearly this thesis is focussed on the former eight motivators, but we need to guard

against the last three.

166


A EuroSDR Workshop in 2009 (reported in Heipke, 2010: p552) classified

crowdsource mappers as follows:

i. Map lovers – a small group who produce trustable and very valuable data.

ii. Casual mappers – hikers, bikers, and mountaineers for example – who may

spend only a little effort in mapping routes they have taken.

iii. Experts – users in organisations that require accurate maps such as mountain

rescue, fire brigades etc. They are motivated by the feeling that they may

improve maps for their own use and use by others.

iv. Media mappers – a potentially large groups of people motivated sporadically

through media campaigns. Significant effort is needed for the campaign to take

off, and contributions are made over a short period and in a restricted locality.

v. Passive mappers – users with mobile phones (with or without GPS activated)

that may be unaware they are providing location, direction and time data to a

system.

vi. Open mappers – users that spend a significant amount of time and effort to

create open datasets (such as OSM). They are motivated by contributing to and

using good public data.

vii. ‘Mechanical Turks’ – who perform ‘micro-tasks’ for monetary payment.

Ramchurn et al. (2013) produced a crowdsource mapping system for identifying

evacuation routes from 5000 buildings that adjoin the Fawley Oil Refinery near

Southampton. Local citizens were encouraged to augment GI drawn from Google Map

Maker with the exit route from each building (a ‘micro-task’); the results would then be

used for disaster simulation. Despite a reasonably extensive publicity campaign,

participation by local citizens was initially poor. The team experimented with different

incentives to motivate ‘the crowd’ to contribute exit route micro-tasks. So in addition to

whatever moral or intrinsic motivations there are, the team also provided a lottery-

based reward (one ticket for every ten micro-tasks, prizes of £200 and £100), and then a

lottery-plus-competition-based reward (increased lottery prizes, plus a leader-board of

contributors with £100 going to the top contributor). They found the lottery-plus-

competition incentive was by far the most effective. The number of contributors

increased three-fold and the number of contributions leapt from a few hundred per day

to some 4500 in a single day (and often well over a thousand after that). 167

Worker’s motivation in

crowdsourcing

Figure 4.18: A model for workers' motivation in crowdsourcing (Kaufmann et al., 2011).



Enjoyment-based

motivation

Community-based

motivation

Immediate payoffs

Delayed payoffs

Social motivation

Community identificationSocial contact

Payment SignallingHuman capital advancement

Action significance by external valuesAction significance by external obligations and normsIndirect feedback from the job

Skills varietyTask identityTask autonomyDirect feedbackPastime


As a benchmark, the team also recruited a separate group of contributors through

Amazon’s Mechanical Turk – a system for hiring home-workers to conduct micro-tasks

for a small unit fee, paying perhaps just $0.02 per micro-task. They found that Turkers

produced results quickly (in one case, 7000 micro-tasks were completed in three

hours), but this “generated data of significantly lesser quality compared to those from

the local deployment” (ibid: p9). 35% of features contributed by Turkers were rejected

as inaccurate or incomplete, compared with just 8% of those from citizens; Turkers

spent on average just 9.4 seconds per micro-task, compared with 23.5 seconds by local

citizens.

4.5.5. Workers' Motivation in Crowdsourcing Model (Kaufmann et al., 2011)

Looking at general use of the Amazon Mechanical Turk system, Kaufmann et al. were

curious about Turkers’ motivations, because the median pay – reported as

USD 1.38/hour – was low by Western standards. <Key Point Model> Based on a

review of various works on technology-related motivation, Kaufmann et al. (2011: p4)

developed the model shown in Error: Reference source not found.

168


In a survey of 431 Turkers, Kaufmann et al. (2011) tested what motivators there were

other than money. They found that extrinsic motivators (payment, skill development,

social motivation) had a strong effect on the time spent on the Amazon system.

However, for many workers, intrinsic motivators were more important, especially the

different facets of enjoyment-based motivation such as task autonomy and skill variety.

For this subject group, ‘social contact’ scored rather low as a motivator. However,

these results may indicate something about the people who work via Amazon

Mechanical Turk and it is not known if they are applicable to other crowdsourcing

activities such as mapping for disaster preparedness.

Brabham has published various papers about motivation for crowdsourcing (2008,

2010, 2012a, 2012b, 2012c). His 2008 survey of 651 contributors to a photography

system found a mix of intrinsic motivators (e.g. “it helps me improve my photography

etc. skills” 79%, “it is a creative outlet for me” 77%, “it is fun” 72%) and extrinsic

motivators (e.g. the opportunity to make money 90%, the opportunity to earn a

reputation as a good photographer/artist 50%). Curiously, whether money was an

influential motivator remains unclear because, when subsequently asked outright, most

claimed they didn’t care about the money. It should be noted that contributors were

predominantly middle- and upper-class, higher-educated white people (Brabham,

2008). Even so, in Brabham’s 2010 survey of 17 competitors in ongoing t-shirt design

competitions, all mentioned that they were motivated by cash prizes. Other motivators

volunteered by respondents were the opportunity to improve one’s skills, the

opportunity for eventual freelance work, the love of the online community, and an

addiction to the online community. To summarise, actions that can improve and reward

a person’s motivation to contribute can include the following:

Providing evidence or reassurance that their contributions will be used for the

greater good.

o Contributors will want to see their contributions used, and quickly

(Coleman, 2009: p345).

Recognising the value of their work, e.g. by the community or other

contributors.

169


Figure 4.19: Orientation: Moving from (initial) Use behavior to Sustained use behaviour.

Use behavior

Determinants

Determinants

Determinants

Determinants


Determinants


o Google Map Maker does this by showing names of contributors in the

corner of the screen. OSM now has a ‘WhoDidIt’ screen, but it is not

integrated into the main system.

Providing a stipend or remuneration.

Managers of a crowdsourced mapping initiative will need to consider these ways to

motivate members of the community to use mapping technologies. Once motivated to

use a technology, it may be important for use to be sustained. This is the topic of the

next section.

4.6. Sustained use modelsIt has been identified in section 2.3.2 that one of the problems in mapping developing

countries is that it is difficult to sustain a mapping initiative. There have been some

studies into the factors that encourage people to sustain their use of a technology in the

longer term.

The longer-term sustainability of any technological system is bound to be of concern.

As an example of the potential problem, Schotanus (2005) reported his experiences of

installing simple water hand pumps in Ethiopia. Even though they had allocated

caretakers and a budget for maintenance, many pumps fell into disuse. This has

implications for the sustained use of more complex mapping technologies.

170


A UNESCO-funded project to train local communities in the use of ICTs had many

successful outcomes (Dyson et al., 2006). There remained, however, doubts over the

sustainability of four of their five case studies. The doubts were raised by a variety of

problems, such as the lack of skills to continue without outside assistance, the lack of

ongoing funding, internal divisions among the communities, and the small number of

trainees.

The concept of sustained use appears in the literature under various synonymous terms,

such as IS continuance, post-adoption behaviour, routinisation, infusion and

assimilation (see Limayem et al., 2007: p707). Unlike initial use, sustained use is not a

one-time decision but perhaps a series of minor decisions to continue. Clearly there

comes a point where any technology will eventually be discarded. So the timescale of

‘sustained’ will vary from one situation to another. Perhaps in some situations,

sustaining use for a few months would be sufficient. In disaster mapping, sustained use

is likely to be required for many years rather than months.

4.6.1. Dynamic TAM (Davis and Venkatesh, 2004)Before considering the main sustained use models, it is worth considering a relatively

less successful model. Dynamic TAM was an adaptation of the original TAM in which

the constructs were repeated three times. Perceived usefulness (PU) and Perceived ease

of use (PEOU) were measured for their effect on intention and short-term behaviour,

then for sustained behaviour, and finally for long-term behaviour, as indicated in Error:

Reference source not found. <Key Point Model> An important feature of this model is

that usage behaviour was divided into ‘short-term’, ‘sustained’ and ‘long-term’. A

possible weakness of the model is that Davis and Venkatesh didn’t consider any

determinants other that PU, PEOU, intention and previous usage.

171

PU

PEOU


Short-term usage behavior


Sustained usage behavior


Long-term usage behavior

Figure 4.20 Proposed Dynamic TAM (based on Davies and Venkatesh, 2004).

Early user reactions

User reactions after significant hands-on experience

User reactions after significant hands-on experience

PU

PEOU

PU

PEOU


Mirroring results from a previous study (Venkatesh et al., 2002) PU, PEOU and

Behavioral intention were found not to be significant determinants of sustained and

long-term use. The only determinant of sustained usage they found was short-term

usage, though this was perhaps because the study did not consider any other (new)

determinants.

172

Figure 4.21 Significance results of Dynamic TAM (based on Davies and Venkatesh, 2004).

All constructs measured at T2 were non-significant determinants of sustained usage behaviour.

All constructs measured at T3 were non-significant determinants of long-term usage behaviour.

PU

PEOU


Short-term usage behavior

Sustained usage behavior

Long-term usage behavior

*****

***

*

***

***

Key:*** p<.001** p<.01* p<.05


Considering lessons learned from UTAUT2 (Venkatesh et al., 2012) and from the

models discussed below, where other determinants were found relevant to sustained

use, this demonstrates a potential weakness in some hypothesis-testing methodologies:

factors that are not proposed in the original hypotheses (e.g. motivation) will not be

measured.

4.6.2. IS Continuance Model (Bhattacherjee, 2001)In marketing literature, satisfaction is widely considered key to building and retaining a

loyal base of long-term consumers. Bhattacherjee (2001) recognised a common

deficiency in Innovation Diffusion Theory (Rogers, 1962), TPB (Ajzen, 1985) and

TAM (Davis et al., 1989): while initial acceptance of technology is an important first

step, long-term viability depends on its continued use, rather than merely initial use.

Although some previous models had recognised the importance of continued use, they

had employed the same set of pre-acceptance factors to explain both initial acceptance

and continued use. Those models could not then explain the “acceptance-

discontinuance anomaly” (Bhattacherjee, 2001: p352).

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IS continuance intention

Satisfaction

Confirmation

Note: t1 = pre-consumption variable; t2 = post-consumption variable

Figure 4.22 Expectation-confirmation Theory (Oliver, 1977).

***

***

*****

***

R2=.41

Key:*** p<.001** p<.01


Bhattacherjee based the IS Continuance Model on concepts from Expectation-

Confirmation Theory (Oliver, 1977). The adaptations for information technologies

were based on empirical findings from a survey of 122 online banking customers.

The study found that satisfaction was the strongest predictor of continued use

(Bhattacherjee, 2001: p364). <Key Point Model> The results suggested that habitual

use is determined by the users’ satisfaction with the technology and continued use

determined by perceived usefulness. User satisfaction is influenced, in turn, by whether

technology use to date confirms their original expectations.

4.6.3. Predictive Power Model (Limayem et al., 2007)In order to identify the role of habit, Limayem et al. analysed some thirty-five prior

studies that had included Habit as a determinant of intention or behaviour.

Interestingly, some studies highlighted that Habit can be assessed only by measuring

performance itself; it may not be reflected in people’s conscious thoughts or stated

intentions. <Key Point Model> Limayem et al. (2007: pp709-716) determined that

Habit has three primary antecedents:

frequent repetition of the behaviour, because if it is performed frequently it

becomes automatic;

the extent of satisfaction with the outcomes of the behaviour (see the IS

Continuance Model, Bhattacherjee, 2001); and174


Confirmation

Satisfaction

IS Continuance Intention

IS Continuance Usage***

Figure 4.23 Baseline model (without habit).

***

***

***

*** ***

R2=.187

Key:*** p<.001* p<.05


relatively stable contexts, because habit can be predicted only if circumstances

do not change.

Limayem et al. introduced a new, fourth antecedent: Comprehensiveness of usage. This

is the extent to which the individual uses various features of the technology in question.

Users who take full advantage of a technology’s overall functionality, “will not confine

their IS usage to specific situations only.” (Limayem et al., 2007: p716). In the

Predictive Power Model only three antecedents appear. The authors decided not to

show Stable context because their data were collected in only one context; stability was

‘controlled’. They conducted longitudinal surveys of 227 university undergraduates’

use of the Web (ibid: p721). Given that this study was about continued usage, a

potential weakness of their approach was that it lasted only 13 weeks. In modelling the

results, they experimented with three variations of the IS Continuance Model: one

without habit, a second with Habit as a determinant of IS continuance usage, and a

third with Habit as a moderator of continuance usage; see Error: Reference source not

found to Error: Reference source not found. They found that the coefficient of

determination was highest in the third model (R2=.187, R2=.211 and R2=.261

respectively).

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Confirmation

Satisfaction


IS continuance usage***

Figure 4.24 Competing model (habit as a direct effect).

***

***

***

*** ***

R2=.211

Habit

***


Confirmation

Satisfaction


IS continuance usage***

Figure 4.25 Research model (habit as a moderator).

***

***

***

*** ***

R2=.261

Habit

***(negative)

***

Comprehensiveness of usage

*

Frequency of past behavior

*


In the third model, Habit is shown as lessening the need for the individual to ‘access’

intention. It has a moderating (negative) effect on the determinant link between

intention and continuance usage; an individual doesn’t have to access his/her previous

intention if usage is automatic. The authors admit that – even after extending

Bhattacherjee’s model (2001) – they were able to explain only 26% of the variance in

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IS continuance usage behaviour; they believed further refinement of the model was

warranted. It may be noted that the incorporation of Limayem’s Habit construct was a

characteristic feature of UTAUT2 (Venkatesh et al., 2012). However, Venkatesh

included it as a determinant (on the left) rather than a moderator (at the bottom); see

Error: Reference source not found on page Error: Reference source not found.

<Key Point Model> Limayem et al. made several recommendations for improving

sustained usage (2007: p732), which appear to be relevant to managers of a

crowdsourced mapping initiative, including:

getting users quickly into the habit of using a new technology;

fostering satisfactory experiences, especially by avoiding discouragement as a

result of problems;

encouraging users to use a technology in as many situations as possible and

useful, that is, avoiding niche usage.

4.6.4. Participation Ladder Model (McCall and Dunn, 2012)

McCall and Dunn (2012) suggest participation of local communities in an initiative can

be to varying ‘intensities’. Their participation ladder shown in Table 4.14 should be

read from the bottom upwards.

Intensity

Rung of the ladder

Actors – insiders and outsiders

Geospatial information

Highest Initiating actions Self-mobilisation

Initiate community mapping for a variety of reasons

Involvement in decision-making

All actors decide at all stages Joint mapping legend

ConsultationLocals refine or prioritise external ideas

Mapping local needs and priorities

Lowest

Information sharing

Two-way communication between insiders and outsiders

Eliciting local spatial knowledge

Sharing in benefits Insiders Monitoring

distributionTable 4.14: Participation ladder (starts at the bottom) (McCall & Dunn, 2012)

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The authors do not imply that every project should strive for maximum intensity. The

intensity a given project adopts should be appropriate to the tasks, competencies, and

relationships between participants. However, clearly the higher rungs of the ladder

represent more sustainable and empowering circumstances. A key objective of this

project is to identify barriers to the adoption of mapping technologies so that they are

enabled to take part at the higher intensities, and that this can be sustained in the longer

term.

Perhaps the pinnacle of sustainability would be where local contributors initiate actions

themselves, recruit other local people and pass on their training and enthusiasm to

them. None of the case studies in Chapter 6 demonstrated this, and no interviewees

were aware of a mapping initiative where this happened. This is confirmed by Haklay

et al. (2014: p45), who tell of a project to map the fledgling nation of South Sudan (a

neighbour of Uganda) by Sudanese diaspora that demonstrated that diaspora recruited

other expatriates to join them, but they found that contributions from local people in

South Sudan were scarce.

4.7. Selection of modelsAll of the models described in this chapter have their strengths. Some have been refined

over the decades, such as the various technology acceptance models; some have not

received such attention. An objective of this project is to model contributors’

motivations and barriers to mapping and to propose an improved model. One model

will be selected as a starting point and adapted to incorporate key points identified from

the literature review and empirical work.

The various technology acceptance models have proven flexible because they allow the

designers to add new constructs as necessary. Examples already mentioned include

TAM and Extended TAM adapted for GIS (Loukis et al., 2010; Lay et al., 2013; Chang

and Caneday, 2011) and online learning (Liu et al., 2010), TAM3 adapted for mobile

policing technologies (Lindsay et al., 2011), and UTAUT adapted for use in India

(Bandyopadhyay and Fraccastoro, 2007; Bandyopadhyay, 2010; Gupta et al., 2008)

and China (Yan Sun, 2007). There have been many other adaptations.

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One of the models – TAM, TAM2, TAM3, Extended TAM, UTAUT or UTATU2 –

needs to be selected as the basis for adaptation. In their study of the adoption of mobile

technologies by a police force, Lindsay et al. (2011) considered applying UTAUT, but

decided to select TAM3 instead because they considered it more robust and more

widely applied than UTAUT at the time. Lindsay et al. applauded UTAUT as a

significant contribution towards information technology research because of the way

several theories are synthesised into a concise model. They identified the new

Facilitating conditions construct as a key strength of UTAUT because previous models

did not consider them. On the other hand, they identified a weakness in that the model

gives little attention to the context and the organisational culture of the organisation in

which it is used, and they were particularly important to their study (ibid: p392);

organisational culture is less important in this project.

As of January 2015, UTAUT2 represents the state of the art in technology acceptance

modelling. Furthermore, constructs such as (hedonic) motivation and habit are useful –

as will be seen in Chapter 7. So UTAUT2 will be used as the starting point for the

proposed new TASUT model. Concepts from several other models will also be

incorporated into the model, especially to take into account factors such as motivation

and sustained use.

4.8. Chapter summaryThis thesis concerns the modelling of factors that enable and encourage crowdsource

contributors to adopt and use mapping technologies for disaster preparedness.

Literature concerning mapping for disaster preparedness was reviewed in Chapter 2.

Although the technologies – as reviewed in Chapter 3 – may be important,

understanding how to engage contributors is at least as important for a successful

mapping initiative. In order to understand the context of technology acceptance models,

this chapter has reviewed the literature on a wide range of models. Some models are

specific to disaster preparedness, some explain how innovations are implemented, some

model decision making in general or technology in particular, some model longer term

motivation, and finally some model influences on sustained use. The models have been

critically evaluated and possible areas for improvement have been identified. Some of

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these areas for improvement relate to the ways in which the culture and facilities in

developing countries differ from those in industrialised nations. For example,

conditions to facilitate the use of modern technologies cannot be taken from granted,

and there is often a culture of ‘saving face’ which means that a stated intention to use a

technology is a poor indicator of actual use. Other areas for improvement relate to the

need for sustained use. For example, many of the models discussed in this chapter were

validated over a single University semester, whereas disaster preparedness initiatives

need to be sustained indefinitely.

Areas for improvement to these models can also be learned from empirical fieldwork

and interviews, but before discussing these, options for research methodology are

considered in the next chapter.

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5. Research MethodologyThe questionnaire is cheap, easy, and mechanical. The study of human

behavior is time consuming, intellectually fatiguing, and depends for its success

upon the ability of the investigator. The former method gives quantitative

results, the latter mainly qualitative. Quantitative measurements are

quantitatively accurate; qualitative evaluations are always subject to the errors

of human judgment. Yet it would seem far more worthwhile to make a shrewd

guess regarding that which is essential than to accurately measure that which

is likely to prove quite irrelevant. (LaPiere, 1934: p237)

Many of the behavioural model studies discussed in Chapter 4 were derived from

quantitative analysis of surveys. This thesis adopts a rather different methodology that

is perhaps less objective, but draws on the experience of experts and on empirical work.

To help understand why, this chapter begins by reviewing the principles.

5.1. Philosophical contextIt would be easy to assume that there is only one valid approach – or perhaps just a few

valid approaches – to a research project. That preferred approach will be based on each

person’s assumptions about reality, how to go about assessing reality, and whether that

assessment is objective or subjective. These are known as ontological assumptions and

epistemological assumptions (Blaikie, 2007). Ontological assumptions relate to the

ways we answer the question, “What is the nature of reality?” Epistemological

assumptions relate to the ways people answer the question, “How can reality be

known?” These assumptions are explored in detail in Appendix A.

5.2. Research methods5.2.1. Inductive / deductive / retroductive / abductive

strategiesBlaikie (2007) suggests that research strategies can be categorised under four headings:

inductive, deductive, retroductive and abductive.

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The inductive strategy involves generalising from specific statements to general

statements. It has been characterised as a ‘bottom up’ approach. By accumulating and

analysing a large quantity of observations or data, the researcher produces

generalisations or even laws. The generalisations are then used to explain further

observations. (Walliman, 2011).

The deductive strategy condenses general statements down to specific statements. It has

been characterised as ‘top down’. The researcher constructs general theories and makes

inferences in order to produce hypotheses. The aim is to test each of those hypotheses,

eliminate false ones and to corroborate the one that is not eliminated (Blaikie, 2007).

The retroductive strategy uses reason and imagination to create a model or picture of

the mechanisms that are responsible for producing the phenomena we observe. Blaikie

claims retroduction can be traced back to Aristotle since it requires us to move from

observations to the creation of a possible explanation. The researcher documents and

models a phenomenon and then constructs a hypothetical model of the underlying

mechanism.

The abductive strategy may be used in the social sciences and informatics in order to

create concepts and theories. These are derived from the everyday concepts and

understandings of the social actors. A social science researcher will examine every-day

lay concepts and motives, and then produce a technical account from the lay accounts.

Blaikie says that the researcher then develops a theory and tests it iteratively.

5.2.2. Grounded theoryGlaser and Strauss (2009: p1) described grounded theory as, “the discovery of theory

from data”. It seeks to counter a perceived under emphasis on generating theories and

an over emphasis on verification of theories. Having identified a study area, the

researcher collects data and simultaneously ‘open codes’ them as they are collected;

‘open coding’ means the data are categorised naturally as they occur, not into headings

dictated by the eventual theoretical solution. The researcher also records memos of

potential relationships among the data. The data are then ‘selective coded’ into only the

core and related categories. The researcher’s theory is formed by examining the memos

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and selectively coded data; the data are then organised into ‘theoretical codes’ or

categories to substantiate the researcher’s theory.

5.2.3. Quantitative / qualitative / mixed dataMuch has been written about the relative merits of quantitative and qualitative data.

Positivist and realist epistemologies (see Appendix A) are closely associated with

quantitative data collection and analysis. If truth can be proved independently of the

researcher, then the researcher needs to gather and analyse facts. If those facts are

numeric, they can be compared, manipulated, analysed for trends, and presented for

scrutiny. Numeric results can often be reproduced and verified by others. Typically

quantitative researchers use either controlled experiments to measure phenomena

directly, or surveys of a sample from a population.

Quantitative data can sometimes give a false impression of objectivity. For example, if

users are surveyed for their opinions about mapping technologies, the responses can be

collated and analysed numerically. Statistics and comparisons can be drawn leading to

conclusions. However, the statistics are about subjective opinions not objective facts:

Did they like the user interface? How did they feel about using the hardware? That

approach is fine if the researcher is aware of the subjectivity. However, the researcher

can draw no firm, objective conclusions about the quality of the hardware or software.

Perhaps the technologies were imposed on respondents, and so they may over

emphasise difficulties out of proportion. Conversely respondents may see the

technologies as toys that are a pleasure to use, and so understate the problems they

experience.

There are also questions about the use of statistics to determine causality, statistical

significance and variance. If there is a correlation between two variables, does the one

cause the other, or vice versa, or is it coincidence? In Chapter 4, many of the

behavioural models were justified on the basis of statistical significance. However, it

has been found that re-running the same experiment several times can produce very

different significance levels – what has been called “the dance of the p values”

(Cumming, 2013). A relationship that is of high significance in one experiment may be

found to be of moderate or no significance in a repeat experiment. Finally, Venkatesh

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et al. (2012) cite several works – Malhotra et al. 2006; Sharma et al. 2009; Straub and

Burton-Jones 2007 – that suggested common-method variance (CMV) is a major

methodological concern with research into technology acceptance models. CMV is a

spurious variance that is cause by the measurement method itself, rather than the

constructs being measured.

Answers to open-ended questions are difficult to analyse numerically, so in a purely

quantitative project the questionnaire would ask only closed-ended questions. This

approach may not give a complete picture of the respondents’ views. What if the

questionnaire fails to ask pertinent questions and there is nowhere for the respondents

to record how they really felt?

Constructivist and relativist epistemologies are closely associated with qualitative data

collection and analysis. In some projects it may be more informative to gain in-depth

opinions from a few experts than a ‘tick sheet’ from a thousand people who do not

really understand the topic. There are some disadvantages regarding the use of

qualitative data. Qualitative data are often subjective and open to interpretation; the

same circumstances might be interpreted differently by another expert. Qualitative data

collection may not be repeatable, so other researchers may not be able to confirm the

outcomes subsequently. In some projects the collection of qualitative data can be

intrusive and may even affect the results.

Often projects use some combination of quantitative and qualitative data collection and

analysis. For example, a survey questionnaire will ask several closed-ended questions,

but also have space for comments, opinions and explanations. The researcher may

supplement the questionnaire with structured interviews and case studies. It is

important not to be blinded by the need for numbers. As Cameron said (1963: p13),

“not everything that can be counted counts, and not everything that counts can be

counted.”

5.2.4. FieldworkEven the most closely controlled scientific experiment with conclusive laboratory

results leaves an element of doubt in the mind unless it can be repeated in real life. In

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some projects laboratory experiments are impractical, due perhaps to scarce resources

or time or ethical concerns or some such. In social sciences and informatics, a

laboratory experiment can bring a sense of artificiality; subjects’ responses may be

conditioned by the false environment, perhaps to give what they perceive is the ‘right’

response rather than the one they would give in real life. In field experiments, subjects

are more likely to respond in the way they normally would (Walliman, 2011). Subjects

may also be more willing to take part in fieldwork research because they do not need to

take time away from the workplace.

5.2.5. Action researchThe term ‘action research’ can be used to cover a variety of non-classical research

methods, though it was originally coined by Kurt Lewin (1946). Styre and Sundgren

(2005: p54) describe action research as, “a loosely coupled framework of research

methodologies aiming both at producing theoretical insights and practical effects.”

Rather than remaining at a distance, the researcher contributes to and makes a

difference to the subject, and so contributes both to solving a practical problem and to

further social science or informatics through collaboration. It has been said that action

research, “starts with the idea that if you want to understand something well you should

try changing it” (Easterby-Smith et al., 2012: p9).

An acknowledged weakness of action research is that it is difficult to generalise the

results. Because the observations and interventions are so bound up in a particular

situation there may be reduced confidence that the action will be successful in another

context.

5.2.6. Case studiesIt is often useful to examine in depth a set of events, activities, processes or individuals.

The advantage of using case studies is that they demonstrate phenomena in their natural

environment with all the attendant real-life complications. A limitation of case studies

is that they are bounded by time and activity (Creswell, 2009); if only a small number

of case studies are examined, any conclusions drawn may not be applicable more

generally. There is also the danger that the chosen case studies aren’t comprehensive.

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For example, if an initiative fails it may not be possible to find the participants to

complete a survey or interview; successful projects may be more prominent and the

participants contactable. If the researcher finds only successful initiatives he/she may

erroneously conclude that all such initiatives are successful.

5.2.7. Narrative researchNarrative research is an approach in which the researcher studies the lives of

individuals and asks them to provide stories about their lives. These stories are often

retold by the researcher in a narrative chronology (Creswell, 2009). In the end, the

narrative combines views from the participants’ lives with those of the researcher’s life.

5.2.8. EthnographyIn ethnographic research, the researcher is interested in how the subjects interpret their

own behaviour, rather than in imposing an interpretation from the outside (Walliman,

2011). The approach regards the context to be as important as the actions under study.

The key principle of ethnography is that the researcher should understand the subject

group in terms of their perspectives, needs, meanings and significances, especially in

relation to their behaviour.

5.2.9. TriangulationIf only one method is used to gather data, or to analyse the data, there is a danger that

biases will go undetected. A researcher can use a variety of methods and look for

convergence of outcomes.

5.3. Research tools5.3.1. Experiments and quasi-experimentsA researcher alters an independent (or manipulated) variable and examines the resulting

effects on dependent variable(s). In order to do this the experiment needs to be in a

controlled environment. The controlled environment may be one where all other

possible variable inputs are eliminated, or it may be where there is a control group; the

experiment group and the control group are compared to see if there are significant

differences in the results (Walliman, 2011).

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It may not be possible to force a change in the independent variable, or the researcher

may choose not to interfere with the system under study. In that case the researcher

may simply wait for the independent variable to change and then observe the effects.

Such a quasi-experiment has the advantage that external interference is minimised. A

disadvantage is that the researcher may have to wait a long time to observe all the

various values of the independent variable.

5.3.2. Questionnaires and surveysOne way to ascertain people’s opinions and experiences is through questionnaires.

Quantitative approaches typically require a large number of responses; analysing a

large number is eased if the questionnaire asks closed-ended questions. Qualitative

projects can also make use of questionnaires, especially if they ask open-ended

questions.

Many advantages of questionnaires relate to how people can respond without the

researcher being present. For example, respondents may be geographically dispersed,

there may be slightly less influence exerted by the researcher over the respondent, the

respondent can take time to answer at his/her own convenience, and anonymity is high.

It is true that a survey can alternatively be administered personally; doubts can be

clarified and there may be a near-100% response rate.

When completing a questionnaire, the respondent may want to mention something

relevant but there is nowhere to write it; perhaps a pertinent question wasn’t asked, or

the range of allowable responses is too narrow or too simple, or there is no ‘Any other

comments’ section.

Some problems with questionnaires can be quite subtle. For example, many

questionnaires used to validate the various technology acceptance models (described in

section 4.4) were administered among university students enquiring about the use of

university-provided software. So even if Price value were important among students,

that determinant was not identified, either because questions about price and value

weren’t asked or because students used the software for free; since Davis et al.’s 1989

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paper, Price value was not formally identified as relevant by any of the studies until

that of Venkatesh et al. (2012) in relation to the UTAUT2 model.

Whether the survey is administered personally or at a distance (mail/online), if the

experimenter gives respondents a clue as to what outcome is hoped for, respondents

may feel under pressure to respond in a way that pleases the researcher; this is known

as ‘experimenter bias’ (Greenberg and Folger, 1988: p128). Consequently such

questionnaires are sometimes nick-named ‘happy sheets’. This is a particular problem

in a society where disagreeing and objecting is discouraged. As with some other parts

of the world, in East Africa there is a strong culture of wanting everyone to ‘save face’,

that is, avoiding embarrassment. Consequently, one must interpret responses

accordingly: a response that is merely lukewarm might, in reality, signify

dissatisfaction.

Where a survey is conducted among a sample of the population, there is a risk of bias if

some parts of the population are more highly represented than others (Walliman, 2011).

For example, an online survey may be more likely to be completed by the technically

savvy and perhaps less likely to be completed by those on low incomes. The risk of

sampling biases is higher if the sample is small. Consequently one may not be able to

generalise from the survey results.

5.3.3. Interviews and structured interviewsAlthough questionnaires are relatively easy to administer, they do have limitations,

especially in the lack of flexibility of response (Walliman, 2011). Interviews permit a

range of responses: at one extreme, in open, unstructured interviews the interviewee is

allowed to discourse in order to gain insights; in a more controlled structured interview

the interviewer reads out a set of pre-determined questions; and at the other extreme, a

structured interview may be just an oral form of a personally administered

questionnaire.

Interviews provide the advantage of allowing flexibility to explore topics that crop up

in conversation, even if those topics had not been considered in advance. They also

provide the interviewee with flexibility when responding to questions. Disadvantages

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include the time required for a reasonable interview, the lack of anonymity, and the risk

of introducing interviewer biases.

Where an interview is conducted face to face, the participants have the opportunity to

fully understand questions and answers through discussion and clarification. Visual

aids can help with this too. The interviewer may gain additional feedback through body

language and para-lingual clues. This can be expensive when interviewees are

geographically widespread (Walliman, 2011). Interviews can also be undertaken by

telephone or VOIP (Internet audio calls). Clues from para-lingual communication are

often lost, but these technologies permit relatively inexpensive ways to contact people

over long distances.

Experimenter bias is perhaps an even greater danger with interviews than it is with

questionnaires. The interviewee’s responses may be affected by the way the interview

is conducted and how questions are phrased. The analysis of results may be affected by

the way the researcher identifies themes; analysis biases can be reduced with the aid of

relevant software – such as NVivo – since it can automate some of the analysis process.

5.3.4. Observation and think aloudIt can be useful to observe people undertaking a task and noting their actions, problems

and solutions. For example, by watching people use a technology, one can identify

what they actually use it for (as distinct from what they say they will use if for), what

problems they experienced, and how they solved or worked around the problems. A

variation on this is to ask the subject to say out loud what he/she is thinking.

5.4. Application of research methodology theory

5.4.1. Philosophical foundationsCreswell says the choices an author makes will be influenced by the author’s

‘worldview’ or epistemology, strategy, the nature of the research problem, the author’s

personal experiences, and the audience for whom the thesis is written. Prior to

undertaking this project, this author would probably have considered himself

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pragmatic, but after examining the options, the main epistemology is advocacy: the

project seeks to empower non-professionals to contribute towards disaster management

in their localities by mapping. A constructivist stand point is adopted for finding out

what factors are important to successful mapping initiatives.

It appears that many technology projects, including GIS-related projects, are often

based on a realist ontology and positivist epistemology, and therefore use quantitative

analysis of independent data to test theories. For example, in a project that develops a

computational GIS algorithm, if data can be collected from objects that exist separately

from the researcher there will be less bias and the results therefore more objective; in

such a project researchers may successfully argue that results must be presented in a

statistical form, rather than narrative form, in order to hold any authority (Flowers,

2009). However, since this project has strong social science and informatics

components, those approaches may not be the best for this project. Flowers argues that

in social science research a veneer of objectivity may be thin since the researchers’

values and preferences will influence the process and ultimately make it difficult to be

truly objective. The same is often true of informatics research.

The project is mainly about crowdsourced mapping initiatives in developing countries,

with a special focus on East Africa. The philosophy is very much to avoid imposing a

solution from the outside, but to devise a solution in cooperation with local people that

is empowering and sustainable. This conforms closely to the advocacy/participatory

paradigm. In practice this project uses techniques associated with constructivist

realism: surveys and case studies are analysed. Since the project bridges informatics

and social science, the observations are bound to be constructed socially.

A key aim of this project is to find ways to develop maps of developing countries in

general, and East Africa in particular. If local nationals are to do this, it will be

necessary to take into account the cultures and norms of the countries in question, so

cultural relativism also needs to be considered. Despite best attempts to avoid

ethnocentricity and instead adopt a cultural relativist approach, it is human nature for

perceptions of East African culture to be coloured by the author’s own culture.

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5.4.2. Research methodsVarious technology acceptance models have been explained in Chapter 4. A key aspect

of this research project is the adaptation and extension of such models for use in

disaster preparedness in developing countries. This project mainly follows grounded

theory (Glaser and Strauss, 2009): the researcher documents a phenomenon using ‘open

coding’ and then ‘selective coding’ (see Chapter 6); by comparing those notes with an

existing technology acceptance model, a new hypothetical model of the underlying

mechanism is constructed based on notes that have been ‘theoretically coded’ (see

Chapter 7). The research strategy is abductive (Blaikie, 2007) by recording accounts of

those experienced in disaster management and mapping initiatives, and producing a

technical account. This contrasts with the approaches taken in most of the technology

acceptance models described in Chapter 4. Most of them used deductive strategies by

proposing a model comprising various constructs, hypothesised the determinants of

those constructs and then questioned users to identify which determinants were the

strongest. Although that approach has many strengths, it can suffer from hypothesis

blindness, that is, if constructs and determinants are missing, they cannot be confirmed

by the respondents’ answers. For example, the construct Price value was not

hypothesised in any technology acceptance models prior to 2012, and so not tested in

questionnaires. In this project constructs are identified abductively though open-ended

questions to relevant people and by an analysis of actual case studies.

Mostly this project relies on qualitative data. Measuring opinions about the proposed

enhancements to UTAUT2 could be done numerically, perhaps using multivariate

analysis of responses from a large number of respondents. All but one of the models

discussed in Chapter 4 were justified quantitatively, albeit often among a sample

comprising only university students. However, the population that could reasonably be

able to evaluate a new model designed for disaster preparedness is probably a relatively

small community of experts with experience of crowdsourced mapping in developing

countries and of disaster mapping. Statistical analysis of a small number of responses

would be unreliable whereas more qualitative responses provide a potentially richer

resource.

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The success of a mapping initiative could also be measured numerically. For example,

one could tally the number of features or coordinates entered as a result of each

initiative. However, those numbers may not tell us anything of value. If one counted

features entered, it might not reveal much about how large those features are, how

much effort it took to collect, how accurately those features have been recorded, or how

important they are. Counting coordinates entered would give an indication of the

quantity of the data, but not about its accuracy or utility.

Using a meandering river as an example, that single feature (low number) might require

hundreds or thousands of coordinate points (high number). If the coordinate points

were taken from satellite images, the feature could be mapped in an hour from the

comfort of one’s home (slight effort, low cost). If the river were not visible on satellite

images, someone would have to travel to the area with a GPS, negotiate overgrown

river banks, traverse rapids, avoid waterfalls, and risk water-borne diseases (huge

effort, cost and personal risk, possibly low accuracy). If the river floods often and

causes extensive damage then that feature might be valuable (high utility), but if the

local peoples are accustomed to working around floods, the floodwater might bring

benefits to agricultural soil (low utility for disaster management). If the river has high

banks it might never flood (low utility for disaster management). Therefore tallying the

number of features alone provides little information about the success of a mapping

initiative.

Knowing and recording the name of a feature may be important, and satellite images do

not help identify names. For example, on the night of 1 March 2010 a landslide hit the

Nametsi, Namakansa and Kubewo villages in Uganda killing an estimated 300-400

villagers and displacing 2000 (Bariyo, 2010; Mafabi, 2010; Atuyambe et al., 2011).

Those villages did not appear on any maps. The only way to record them on a map was

to visit them; this involved driving along dirt tracks for a couple of hours, and then

walking eight kilometres from the nearest track to the landslip site and back. Although

a number of villages were recorded en route, subsequent major landslides destroyed

villages elsewhere (that similarly did not appear on any maps). So is the utility of those

features high or low? Since 2010, knowing the location of one landslip has

demonstrated relatively low utility, but – over time – records of the locations of all

landslips should help with hazard analysis and disaster mitigation. Knowing the routes 192


and surface conditions of roads and tracks in the area might be of use to disaster

response organisations.

The unpredictability of disasters makes it difficult to assign a value to the utility of any

given feature. It may never be needed, or perhaps one day it will be crucial to disaster

management. The logical way forward is to record as much disaster-relevant GI as

possible, preferably spread over a large area. In section 2.5 “Mapping for disaster

management”, a variety of features were listed that are commonly useful for disaster

management.

In this project a quantitative survey was taken of participants in a training course to

seek their views about mapping technologies. This forms only a small part of the

project, though. The more significant parts of the project were examined as case studies

and structured interviews.

This project has clearly used an iterative action research approach. Chapter 6 explains

how these ideas developed during the life of this project. Initially it was hoped that

capacity building could be achieved with a simple training course, and that the trainees

would subsequently map their localities with GI that would be relevant for disaster

preparedness. When that failed to produce the expected results one-to-one mentoring

was tried. When that failed, a range of other methods were examined and analysed for

success criteria. Failed case studies were as valuable as the successful ones because

they helped identify ways in which resources might be wasted and approaches that are

more fruitful instead.

In Chapter 7 the literature review, the interviews and the case studies are mapped onto

the technology acceptance model UTAUT2. Where it is found to be inadequate in some

ways, by a process of abduction – and by allowing the data to speak for itself –

UTAUT2 is adapted and extended to become the Theory of Acceptance and Sustained

Use of Technology (TASUT).

Although ethnography could have been a useful technique in this project, it was not

possible for this author to become part of the local society in the Ugandan fieldwork.

Three visits were made of about 10 days each. Proper integration into local society was 193


unattainable due to practical and cultural issues. Practical issues included the distance,

cost and time limitations. Cultural issues included not being a Ugandan national, not

looking or sounding like one, unfamiliarity with all the local customs, and not speaking

the local languages.

Nevertheless this thesis attempts to interpret observed attitudes and phenomena in the

light of local culture, and could thus be described as an ‘etic’ account. This was aided

by case studies about people who work long term in East Africa, and interviewees born

and raised in the region. In order to triangulate the results, no single approach has been

used exclusively. In this project several case studies were initiated and analysed, some

others were examined, and the opinions of experts were used to identify motivators and

obstacles to mapping for disaster preparedness. From all the outcomes common themes

were identified.

5.4.3. Research toolsAlthough the concept of controlled experiments is valuable, in searching for best

practice in encouraging people to map their localities for disaster preparedness it is not

possible to conduct fully controlled experiments, because the numbers of people

involved in the fieldwork case studies are too small. However, by looking at a range of

case studies one may be able to discern factors that may be considered akin to

independent variables that affect the dependent variable, i.e. the amount of relevant GI

added to a map over time. In overall terms, though, the observations are qualitative not

quantitative.

The kinds of information required for this project related to establishing the mapping

needs of local organisations in the study area, establishing how best to motivate

communities to map their areas, and evaluating various approaches. A relatively small

number of expert people were able to provide each category of information, and

interaction with each person had to be tailored to the particular purpose and their areas

of expertise. Most of the interviews with UK experts were held face-to-face. Interviews

with people from Uganda were mostly held during trips to the region, but some relevant

Ugandans were also interviewed when they visited the UK. Some experts in the USA

and Germany were interviewed either by telephone or using Skype (a VOIP

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technology). All interview summaries were agreed in writing with the interviewees. A

few experts were unavailable for interviews and so were contacted solely by e-mail.

The interview summaries and e-mail exchanges were subsequently analysed with the

aid of NVivo 10 software. Triangulation is achieved by comparing the experiences of

the experts with various empirical case studies of mapping initiatives in Uganda and

Kenya.

5.5. Ethical considerationsThe project’s aim of producing an improved technology acceptance model appears to

have few ethical implications. In general, society will benefit (or at worst, not be

harmed). If the improved technology acceptance model is found useful, it should help

those planning mapping initiatives to identify ways to motivate communities and

reduce barriers to participation. As a result, more GI should become available for

disaster preparedness, and all the concomitant benefits mentioned above should accrue.

If mapping initiatives adopt the guidelines for applying the model, more GI should

become available for disaster preparedness; hazards and vulnerabilities can be more

easily located; safeguards can be improved; the likelihood and severity of disasters in

the mapped areas might thus be reduced; and disaster response should be more

effective and efficient.

For some people there may be an ethical reservation about profit-making organisations

such as Google ‘exploiting’ volunteers to create GI that it subsequently uses for

commercial purposes. This is one of the factors behind the recommendation (later in

this thesis) that GI be stored in systems under a Creative Commons or Open Data

Commons licence.

Concerning the ethics of the methodology adopted, all the individuals who took part in

observed field exercises gave informed consent to be included in this research project,

and knew that the author’s intention was to improve mapping for disaster preparedness.

Although interviews run the risk of introducing interviewer biases, the researcher was

careful to ask open-ended and value-free questions, especially when interviewing

people from Uganda. A summary of each interview was sent by e-mail to the relevant

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interviewee to check and correct. All references to interviewees and survey respondents

have been anonymised.

The author has made every effort to attribute ideas and quotations in this thesis to the

original authors. Where an author has quoted someone else, and the original work is

available, the original has been checked and cited; if the original work is no longer

available then the citation is qualified with the words “cited in…” followed by the

secondary citation. Direct quotes are indicated – in accordance with University

guidelines – by the presence of double quotation marks or indentation.

5.6. Chapter summaryMost of the data collected have been qualitative: interviews with various stakeholders

in the narrative tradition, and analyses of case studies. There was also a small survey of

delegates to a training course that is analysed quantitatively. Detailed findings from this

primary research are described in Chapter 6.

5.6.1. Interviews and correspondence26 people were consulted during the requirement-finding stage of this project (many of

them more than once). Their identities have been anonymised, but an indication of their

roles is given at the beginning of Chapter 6. In summary:

6 interviewees were from government organisations in East Africa:

o 5 of them from district local government; and

o 1 from central government.

14 were from NGOs:

o 1 of them from a quango in East Africa;

o 13 from NGOs, mostly people who live in East Africa or had deployed

there for significant periods of time, including,

3 from MapAction;

2 from Humanitarian OSM Team;

1 from Oxfam.

3 were from private-sector businesses that created or used GI in East Africa.

196


3 were from university departments that conducted research into disaster

management and/or economic development.

There were 34 separate interactions with the interviewees. 16 interviews were

conducted face-to-face; 3 orally by telephone or VOIP; and 15 using e-mail. The

content of the interviews has been analysed thematically in Chapter 6 with the aid of

NVivo 10 software.

5.6.2. Survey In January 2011, 14 delegates to a training course in Mbale, Uganda, completed a

questionnaire about the mapping technologies they had been trained in. The

respondents were from various government and non-governmental organisations in the

Mbale region of Uganda:

1 was from Mbale District Health Office.

6 were from Mbale District Local Government.

3 were from Manafwa District Local Government

1 was from Bududa District Local Government

3 were from local NGOs (Gumutindo Coffee Co-operative Enterprises Ltd,

Salem Brotherhood and SAO (Share An Opportunity).

Delegates answered questions about their intention to use the various types of

hardware, software and map hosting services used in the training course.

5.6.3. Empirical fieldwork case studiesSix case studies are used in Chapter 6 to identify lessons on how to encourage non-

experts to map their areas, whether explicitly for disaster preparedness or for other

purposes that would result in a map suitable for disaster preparedness.

5.6.4. Development of TASUT and guidelinesAs indicated in Error: Reference source not found, the literature review, interviews,

survey and empirical fieldwork provide the foundations of this thesis. Key findings

were tagged to aid traceability. Findings relevant to the design of the model, such as the

197

Interviews, survey(Chapter 6)

Literature review(Chapters 2-4)

Empirical fieldwork(Chapter 6)

TASUT(working version)(Chapter 7) Guideline

s(Chapter 8)

Constructs

Evaluation(includes final version TASUT)(Chapter 9)

Figure 5.1: Overview of the development of thesis.

<Key Point Model>

Issues and concerns

Introduction and Conclusion(Chapters 1 and 10)


importance of a concept and its influence on outcomes for use behaviour, were tagged

with <Key Point Model>; they are summarised in Table 7.17, Table 7.18 and Table

7.19, and the resulting model is documented in Chapter 7. Findings relevant to the

guidelines, such as advice about how to go about achieving an outcome are

documented in Chapter 8.

5.6.5. Evaluation of TASUT and guidelinesWhen the model and guidelines had been drafted in October 2014, a copy was sent to

various contacts in NGOs and universities around the world along with a questionnaire

(see Appendix B) for them to provide feedback. The questionnaire asked open-ended

questions about the TASUT diagram, the accompanying explanatory text and

definitions of terms, and the guidelines. The responses are documented in Chapter 9.

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6. Interviews, survey and empirical fieldwork

Information is very directly about saving lives. If we take the wrong decisions,

make the wrong choices about where we put our money and our effort because

our knowledge is poor, we are condemning some of the most deserving to death

or destitution.

(John Holmes, UN Emergency Relief Coordinator and Under-Secretary-

General for Humanitarian Affairs 2007-2010, quoted in MapAction, 2011b: p2)

34 interviews and exchanges of correspondence were held between July 2008 and Feb

2014, as summarised in Table 6.15. In order to preserve anonymity the indicated codes

have been used in subsequent analysis rather than the interviewees’ names.

Code Organisation Role Date Interview type

{PRI1a} Google Kenya Technologist 30 Jul 2008 E-mail{NGO1a}

MapAction Volunteer 11 Aug 2008

Face-to-face

{UNI1a} Coventry University

Research Director, Environmental GIS

6 Feb 2009 E-mail

{NGO3a}

PONT Coordinator 10 Mar 2009

Face-to-face

{DLG1a} Mbale District Local Government

Senior officer 27 Apr 2009

Face-to-face

{DLG4a} Bududa District Local Government

Community officer 7 Jul 2009 Face-to-face

{UNI2a} Ugandan Christian University, Mukono

Lecturer in Development Studies

7 Jul 2009 Face-to-face

{NGO4a}

PONT Coordinator 13 Jul 2009 Face-to-face

{NGO5a}

JENGA Project manager 13 Jul 2009 Face-to-face

{CGD1a}

Ugandan National Roads Authority

Station engineer 14 Jul 2009 Face-to-face

{DLG2a} Mbale District Local

Senior planning officer


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Government{QAN1a}

Uganda National Park Authority at Mount Elgon

Head Ranger 15 Jul 2009 Face-to-face

{NGO1b}

MapAction Volunteer 4 Dec 2009 Face-to-face

{NGO2a}

MapAction Then Chief Executive


{DLG3a} Mbale District Local Government

Environmental officer

10 Nov 2010

Face-to-face

{PRI2a} Uganda Carbon Bureau

Senior officer 12 Jan 2011 Face-to-face

{DLG5a} Manafwa District Local Government

Land-use Planning Officer

19 Apr 2012

Face-to-face

{NGO2b}

MapAction Then Chief Executive

16 Oct 2012

Face-to-face

{NGO6a}

American Red Cross

GIS coordinator 18 Nov 2012

E-mail

{NGO7a}

InterAction Senior associate 27 Nov 2012

VOIP (Skype)

{NGO6b}

American Red Cross

GIS coordinator 18 Dec 2012

VOIP (Skype)

{NGO8a}

GroundTruth Initiative LLC

Founder/trustee of Map Kibera

7 Feb 2013 Telephone

{NGO9a}

UWASNET Technical adviser 8 Feb 2013 E-mail

{NGO9b}

UWASNET Technical adviser 11 Feb 2013

E-mail

{NGO10a}

Humanitarian OSM Team

Volunteer 19 Mar 2013

E-mail

{PRI3a} Not published Trainer 24 Apr 2013

E-mail

{NGO11a}

Oxfam Digital Comms Specialist

7 Jun 2013 E-mail

{UNI3a} ITC, University of Twente

Research group leader

24 Jun 2013 E-mail

{UNI3b} ITC, University of Twente

Research group leader

5 Jul 2013 E-mail

{NGO12a}


Executive Director 25 Nov 2013

E-mail

{NGO12b}


Executive Director 6 Dec 2013 E-mail

{NGO12c}


Executive Director 9 Dec 2013 E-mail

{NGO9c}

UWASNET Technical adviser 28 Jan 2014 E-mail

{NGO13 MapAction Chief Executive 10 Feb E-mail200



a} 2014Table 6.15 Interviews

With the aid of NVivo software, interviewees’ responses were analysed under the

following themes and presented in sections 6.1 and 6.2:

What is best practice in mapping developing countries for disaster

preparedness?

o What GI needs to be collected for disaster response?

o What uses for GI do local organisations have?

o What skills are needed to record GI, and does the local population have

those skills?

o What financial and other resources are needed for recording GI, and are

those resources available locally?

o Who should own/host the GI and why?

o How can the recording of GI be sustained in the long term?

In what ways do your experiences suggest improvements in the technology

acceptance models?

o Performance expectancy.

o Effort expectancy.

o Social influence.

o Facilitating conditions.

o Hedonic motivation.

o Price value.

o Habit.

o Behavioral intention and Use behaviour.

o Other.

In January 2011, fourteen delegates on a training course in Mbale, Uganda, completed

a survey about mapping technologies; this is documented in section 6.3. A variety of

case studies are described and evaluated in section 6.4. Lessons learned from the

empirical work are highlighted.

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6.1. Interview themes related to best practice in mapping developing countries for disaster preparedness

Note: This section corresponds to the ‘open coding’ stage of grounded theory.

The headings are the questions posed to interviewees and correspondents.

6.1.1. What GI needs to be collected for disaster response?

{NGO1b} explained that MapAction’s primary objective when deployed at a disaster

zone is to provide GI for humanitarian information management. MapAction assembles

GI from a variety of sources, including national mapping agencies, UN sources such as

the Global Administrative Unit Layers, local utility companies, Google Maps and

Google Map Maker, and OSM.

{NGO2a} mentioned other sources of information assembled by MapAction:

archived base map data, including VMAP (a global digital map produced by the

US government) and the Shuttle Radar Topography Mission data;

topographic mapping – as available – from national mapping agencies, military

surveys from the US and USSR even though it may be solely rasterised;

pre- and post-event satellite imagery obtained under the International Charter

Space & Major Disasters (International Charter, 2000);

locally acquired situation data, sometimes geo-tagged, though often the

geographic location is informal.

{NGO2a} identified the following uses for MapAction’s GI during disaster response:

identifying the extent of the disaster;

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providing reference and navigation maps during the assessment phase of the

emergency, including place names, street names and points of interest are

important here;

creating a common operational picture (Who-What-Where) that is kept up to

date as the situation develops;

targeting relief assistance to avoid gaps and overlaps in provision;

enabling NGOs to make informed decisions on where to work.

{NGO2b} identified the importance of knowing the local administrative boundaries in

a disaster area, such as counties and districts. The boundaries allowed MapAction to

access census data to estimate likely casualties, numbers of displaced persons and so

on.

Reference maps produced include ({NGO2a}):

search-and-rescue;

risk assessment;

thematic (e.g. topographic, population density);

logistics planning (tools for decision making);

relief assistance records (Who-What-Where) including distribution and

operational partners.

Discoverability{NGO2b} emphasised the importance of being able to discover relevant GI quickly,

and to be able to access/download it without unnecessary delays. In general, bandwidth

in remote disaster zones is so limited that deployment teams are unlikely to receive

much additional formal GI once they reach the disaster area. If a source of GI is

licensed, the time required to agree usage may mean that it is not available to give to a

deployment team before they reach the airport. Once the team is on board an aircraft it

is quite likely it will receive no further GI for some time, perhaps until after the main

disaster response activity has ended (Teeuw et al., 2012: p131) {NGO2a}.

{NGO1b} explained that sources that make GI available under an open or Creative

Commons licence – such as OSM – are useful because they can be used without any

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significant restrictions. In the past, MapAction had used Google Earth, scanning an area

at base before deploying to a disaster area, and copying the Google Earth software’s

cache onto flash memory drives for team members. However, it was later found that

this was outside of Google’s licence and the team could no longer do this.

MapAction has conducted missions to identify GI for areas identified as being at risk.

{NGO2b} related an example from 2010 when MapAction sent volunteers to the

Kathmandu Valley in Nepal. Although this was a relatively well-mapped area, they

went to identify GI sources that could be used in the event of a disaster. The team

worked with UN agencies to create Common Operational Datasets (CODs) and

Fundamental Operational Datasets (FODs). CODS and FODS are registries of where

GI can be found, as opposed to a repository of that GI; the GI is updated over time and

the CODs/FODs allow response organisations to find out what relevant GI is stored and

where.

Quality and accuracyWhen asked about potential inaccuracies in crowdsourced GI, {NGO2a} explained that

in general any GI is better than no GI, and that minor inaccuracies are seldom a

problem. After the earthquake in Haiti in January 2010 the large numbers of volunteers

produced often unverified, contradictory and vague GI. Using Haitian diaspora to

verify the crowdsourced GI helped to reduce the problems, but crowdsourced GI is not

the whole solution.

During disaster response, according to {NGO2a}, aerial imagery is useful for quantity

but not quality. That is, it helps identify priority areas where the disaster zone is

particularly large, but says little about the human geography or human needs. Even

when identifying the extent of a disaster zone, such imagery is of limited use and needs

to be ground truthed. This takes time and early images may be out of date by the time

relief teams reach the areas; in 2005 in Kashmir, for example, it took relief teams a

month to reach the worst-affected areas.

Capacity{NGO2a} identified several capacity gaps:

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Generally not enough information management capacity within the

humanitarian system.

Lack of standards for humanitarian data.

Specialist technology that would need too much specialist training and would

not be supportable in the long term.

Limited access to basic IT tools among NGOs that are without GPS or technical

capacity.

Funding not sustained in between incidents.

Communication with the disaster-affected communities: those affected are often

left out of the communication loop.

And conversely, too many technology-led projects (‘May fly’ initiatives) that do

not deliver what users actually need at field level.

6.1.2. What uses do local organisations have for GI?A major element of this project is to identify ways to motivate communities to record

GI for any purposes. So it is important to identify organisations’ needs for GI that could

also be used in the event of a disaster.

Gap analysisA common theme among interviewees was that organisations needed GI for gap

analysis. {UNI2a}, {DLG3a}, {DLG4a} and {NGO5a} identified that local

government and NGOs might use GI to help identify needs and reduce duplication of

effort. For example, {UNI2a} suggested NGOs might identify where there was more

need to run water projects, women’s groups and so on. {DLG4a} suggested local

government could use GI to decide where to drill water bore holes. {NGO5a} had

personal experience in projects that mapped – with the aid of GPSs – the spread of

malaria.

{CGD1a} was responsible for the trunk roads in the District of Mbale. He needed to

identify problem areas and prioritise resources for building and maintaining trunk

roads. He suggested that local government might want the same for the roads they were

responsible for.

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Administration and finance {NGO4a} worked for a coordinating NGO that worked with a large number of partner

NGOs. He wanted to plot the location of the partner NGOs’ offices and their projects.

In addition to gap analysis, {NGO5a} wanted to plot his NGO’s projects on a map as a

visual aid for fundraising. The need to record where aid monies had been spent is often

a requirement of grant schemes. {NGO7a} runs an online system for convening NGOs

that work in international development. The NGO Aid Map (www.ngoaidmap.org)

records where humanitarian aid money is being spent and provides a brief explanation

of each project. Initially the system was mapping locations of projects relating to

pandemic preparedness and food security. {PRI2a} explained that carbon offset credits

made under the Kyoto Protocol’s Clean Development Mechanism have to be auditable.

The geographic location of individual schemes has to be recorded so an auditor can

visit and check the actual reduction in carbon footprint.

Census and populationThe literature suggests that population census data is important (IASC, 2010;

Redmond, 2005). {NGO2b} emphasised the need for census data during disaster

response in order to estimate likely casualties and survivors. {DLG2a} explained that

census officers were visiting (in 2009) every household in Uganda to record

information about social status, living conditions, and the age, marital status and

education level of each household member. That information needed to be recorded or

summarised in a geographic database. This project was part of the Peace Recovery and

Development Programme that aimed to help Uganda recover from past insurgency by

the Lord’s Resistance Army.

Disaster management{NGO6a} stressed that the Red Cross’s work in Uganda was not just trying to teach

people to map; they were teaching people to prepare better for disasters, and that

happened to include mapping. {UNI2a} agreed that local government and non-

government organisations would benefit massively from GI for disaster preparedness.

She stressed, though, that disaster preparedness is still an emerging issue in developing

countries.

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{NGO7a} recognised that a by-product of the NGO Aid Map would be information

about what organisations are active in each area. In the event of a disaster, response

organisations would know who is active in a given location and what their

competencies are. {DLG3a} added that GI could be used for disaster vulnerability

analysis, especially identifying people and localities subject to economic, social and

environmental vulnerabilities.

Sometimes GI of a particular area is needed for disaster management in a neighbouring

area. For example, {NGO6a} explained how the American Red Cross were mapping

the Manafwa River and tributaries in the Mbale Region as part of a flood early warning

system for the marshy Butaleja District which is just downstream.

6.1.3. Does the local population have the skills to record GI?

{DLG2a}, then District Planner at Mbale District Local Government, had been tasked

by Ugandan central government to develop a digital map of the Mbale District, in

particular to store census data geographically. This initiative had been set up at short

notice, and he was of the opinion that Mbale District Local Government did not at the

time have the skills, procedures or technologies to record its census data in a GIS.

However, when he learned about modern mapping technologies, he was enthusiastic

about their relative economy and simplicity.

{QAN1a} was Head Ranger for the Ugandan quango, Uganda National Park Authority.

The Authority already had a successful GIS. {QAN1a} described how Park Rangers

record incidents and sightings in the Mount Elgon National Park; the system is standard

throughout the national parks in Uganda. Each Ranger has a GPS device which they

use to record the location of events; the device displays a waypoint number and the

Ranger records this on a record sheet along with a coded description. Relevant events

can include activities that are illegal within the National Park (such as tree cutting or

land cultivation), forest fires and mammal sightings. The data are uploaded into their

management information system for analysis, and into ESRI Arc View GIS to identify

trends and monitor warden activity. At the time of interview, the Authority’s system

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had been in daily use for over a year. {QAN1a} explained that most Rangers have O-

Level education.

Opinion was divided about whether skills could be found locally to collect GI, record it

in a GIS, and sustain the system in the long term. MapAction has run capacity-building

projects in developing countries since 2004, when they did their first urban poverty

mission in Delhi, India {NGO2b}. Since then MapAction had run capacity-building

deployments in Mozambique, Malawi, Northern India, Western Kenya, Northern Iraq,

Angola and other places. The Kenya project was specifically to develop community-

level crisis mapping skills. Based on his experience of capacity-building projects in

developing countries, {NGO1a} was firmly of the opinion that local populace was

capable of using and maintaining a GIS. His colleague {NGO2b} was less enthusiastic.

He felt that the logic for training-led initiatives was not robust and that past capacity-

building projects would be reviewed before proceeding with any more due to concerns

about sustainable outcomes. {NGO9c} thought similarly that staff at NGOs had little

spare capacity to learn mapping skills and mapping technologies.

Whatever the level of skills, {UNI1a} thought that local government was less well

skilled than NGOs; NGOs had more highly-skilled staff. {UNI1a} claimed that NGOs

are often better positioned to respond promptly to data requests than governments.

NGOs see the need to improve data exchange and want to do something to improve the

situation. In some cases NGOs have better equipment and more highly skilled staff than

government organisations. {NGO2b} warned that government trainees would often use

their new GIS skills to gain promotion; unfortunately this meant that their mapping

skills left with them and organisations were left with few able to record GI.

On Humanitarian OSM Team projects, {NGO12a} had found that NGOs, voluntary

organisations and university students were far more willing to go out and collect GI; it

wasn’t usually government officials who went out actually collecting data.

Early in 2012 {NGO6b} lead a mapping exercise in Uganda. He paid a team of 12 local

people to collect GI for a flood early warning system. After initial training in the use of

GPSs and data coding standards, the team were dispatched around the area on

motorcycle taxis (‘boda bodas’). Six of the team transferred the GI into the open-source 208


GIS system QGIS. During this process, errors would sometimes come to light, and

team managers would show the surveyors what the problems were. {NGO6a} said that

when the surveyors saw how the data was being used in practice, they could more

easily understand how to record it properly, and then the quality of GI improved

immensely. The GI was subsequently transferred from QGIS into OSM by interns in

the head office in Washington DC.

6.1.4. What financial and other resources are needed for recording GI, and are those resources available locally?

{NGO8a} had developed a map of three slum areas in Nairobi, Kenya: Kibera, Mathare

and Mukuru. She explained that the culture among local people in the Nairobi slums

(and elsewhere) is that people expect a stipend when volunteering with NGOs, even if

the sums are small. Consequently, all team members on the Map Kibera, Map Mathare

and Map Mukuru projects were remunerated. She stressed that establishing the right

level of remuneration is crucial. After the Haiti earthquake, some response

organisations paid stipends to local volunteers amounts that were – in her opinion –

rather generous, and that made it more difficult for other organisations to recruit

volunteers. Similarly, when {NGO6a} led a mapping exercise in Uganda for a flood

early warning system, he paid his team of 12 local people to collect GI.

Financial funds would be required for collecting, recording and maintaining GI.

{NGO3a} highlighted that misappropriation of funds can be problematic in Uganda

{NGO3a}. This is a cultural problem that permeates whole organisations; junior staff

see senior staff doing this and follow their lead. {NGO3a} suggested that

misappropriation was a symptom of low pay rates.

{NGO7a} explained how the US Department of State’s Humanitarian Information Unit

(HIU) (www.state.gov/s/inr/hiu/) had been able to donate high-resolution aerial

imagery of Uganda for the Red Cross mapping projects in Gulu and Lira in 2012.

{PRI3a} confirmed the findings of the fieldwork case studies that aerial images were

highly useful for initial mapping of features, even if they needed subsequent ground

truthing.

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{DLG2a} and {DLG3a} noted that Mbale District Government had ArcView 3.2,

though the licence was fixed to a single PC, the PC no longer functioned, and the GI

was out of date.

6.1.5. Who should own/host the GI and why?The crowdsourcing literature review (section 2.4) highlighted that control and

ownership of GI is an important issue, especially where local people are asked to

contribute to the database. Only two of the interviewees volunteered any views about

ownership. {NGO8a} made two points. First, {NGO8a} had found that a lot of time,

effort and skill was required to impart a sense of ownership of Map Kibera among local

communities. Much of the literature on this matter tended to underplay the difficulty.

She believed it helped that their mapping teams were drawn from each of the localities

within the Kibera slum. She had also found that the appointment of a local person in

Nairobi as Executive Director had helped overcome barriers.

Second, in the context of remunerating team members, {NGO8a} explained that local

people’s scepticism about helping NGOs for free was understandable given that many

organisations use public volunteering to build their own databases for private profit.

For example, Google Map Maker is crowdsourced by volunteers, but the GI becomes

the intellectual property of Google and is eventually subsumed into the main Google

Map. Consequently, the Map Kibera, Map Mathare and Map Mukuru projects all use

OSM because its Creative Commons licence makes that information freely available to

the community. {NGO8a} thought that it was important to demonstrate the value and

usefulness of volunteered GI to those who live in the localities. Paper copies of maps

were useful for this, especially where Internet access is slow or unavailable.

When asked about hosting and maintaining GI of the Mbale Region, {UNI2a} thought

that perhaps PONT should do this since it is the main coordinating NGO in the Mbale

region. However, the coordinator at PONT, {NGO4a}, suggested that GI would be best

stored on an Internet-based system so that it would be available to all and sustainable.

The other interviewee who commented about ownership – {NGO9c} – was very

supportive of open access or Creative Commons licence. He warned that moves in

Ugandan government to turn government authorities into agencies could reduce the

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availability of freely available GI. He predicted that the agencies would see their data

as a source of revenue.

6.1.6. How can the recording of GI be sustained in the long term?

The literature suggests – and personal experience confirms – that it is difficult to

maintain high-technology equipment in working order in developing countries,

especially where there is not a good network of suppliers and retailers. For example,

{DLG2a} and {DLG3a} admitted that Mbale District Government’s copy of ESRI

ArcView and GI was out of date. The system had not been used for some time and they

admitted that the PC was no longer in working order.

The experience of {UNI1a} was that sustainability is rather variable in different parts

of the world. In Botswana, China and South Africa he found strong in-country

infrastructure; Costa Rica was also good and somewhat protective/independent.

Sustainability was much weaker in The Gambia; Montserrat depends almost

completely on people from the UK (even though some people are based locally).

Interviewee {NGO1a} had personal experience of GISs being maintained in the

medium to long term in Malawi. He was firmly of the opinion that GISs can be

maintained by local communities in Africa provided financial support was secure.

{NGO3a} confirmed that financial support for maintenance of equipment is important.

One factor that can result in lack of maintenance is that funds may be misappropriated,

so mapping initiatives should be designed with this in mind.

{NGO6a} explained that the American Red Cross linked their mapping initiative in

Uganda specifically to disaster management but found it useful to give local NGOs (in

this case, Uganda Red Cross) additional use cases that were accessible and realistic.

Their support was needed for the longer term. Even so, {NGO6a} agreed that it was

incredibly difficult to build a sustainable knowledge base. The Humanitarian OSM

Team had found it useful to have a local NGO to help engage the local community; in

their programme in Eastern Indonesia, they used an NGO called ACCESS since they

were already engaged with the communities {NGO12a}. {NGO12a} said that it would

be difficult to do much data collection without remunerating the contributors. She

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thought people might volunteer in the short term, but that couldn’t be relied on in the

longer term.

6.2. Interview themes related to an improved technology acceptance model

Note: This section corresponds to the ‘selective coding’ stage of grounded

theory using categories from UTAUT (Venkatesh et al., 2003).

With the aid of NVivo 10, the same interviews and correspondence that were analysed

in the previous section have been reanalysed in this section, so some opinions already

stated may be repeated here.

6.2.1. Performance expectancy (Perceived usefulness)

<Key Point Model> During disaster preparedness, the benefits one might hope to gain

from collecting, collating and making GI freely available are:

identifying local needs {DLG2a};

providing information for gap analysis {CGD1a} {DLG3a} {DLG4a}

{NGO5a} {UNI2a};

enabling NGOs to make informed decisions {NGO2a};

identifying natural resources such as water sources {NGO9a};

vulnerability analysis {DLG3a} {NGO11a};

creating early warning systems, such as the Red Cross’s flood early warning

system in the Manafwa Basin {NGO6a};

providing information for finance, audit and administration {NGO4a}

{NGO7a}.

<Key Point Model> Performance expectancy can go wider than one mapping project.

For example, GroundTruth has had some success in Tanzania training urban planning

students in the use of GPS and GIS. {NGO8a} said that the students used this

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experience for their immediate studies and also to gain positions as interns. {PRI3a}

had taught mapping skills to biology students in Eastern Uganda so they could use this

technology in their fieldwork. {NGO6a} at the American Red Cross was hoping that

the Ugandan Red Cross would be able to use the experience it has gained on projects to

map the Manafwa River Basin (see 6.4.5), Gulu and Lira on subsequent projects.

<Key Point Model> {NGO2a} identified the following uses for MapAction’s GI during

disaster response:

identifying the extent of the disaster/problem;

providing reference and navigation maps during the assessment phase of the

emergency, including place names, street names and points of interest are

important here;

creating a common operational picture as work progresses (Who-What-Where),

which requires them to update the GI as the situation develops;

targeting relief assistance to avoid gaps and overlaps in provision;

enabling NGOs to make informed decisions on where to work.

6.2.2. Effort expectancy (Perceived ease of use)Although several points were made by interviewees that are related to Effort

expectancy, those directly about this construct were in relation to disaster response

rather than disaster preparedness.

HOT encourages organisations to use OSM for daily use so the GI is available in the

event of a disaster. “Our focus with a lot of [projects] is to find organizations already

mapping the area and then show them how using OpenStreetMap to do it makes things

easier. Then everyone can utilize the data collected.” {NGO12b}

{UNI3b} highlighted that all kinds of GI sources can be used for disaster management:

We must be clear that spatial data serve a variety of purposes. Most information

related to preparedness is either physical (transport network information,

location of support infrastructure such as shelters, police, etc.) or social (census-

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type information that says something about vulnerability to hazards, etc.). These

are pretty general base data we need for many purposes, including risk

reduction/ disaster preparedness. In sum, we should be less focused on

generating preparedness information, but on understanding the multitude of

available base data, how they relate to risk and preparedness, which base data

crowdsourcing can best support, and then through what strategies people can be

best motivated to provide such information, or engage in active collection of

such data.

<Key Point Model> Repositories of GI had to be discoverable in the event of a disaster.

For example, late in 2010 MapAction sent volunteers to the Kathmandu Valley in

Nepal {NGO2b}. Although this was a relatively well mapped area, they went to

identify GI sources that could be used in the event of a disaster. The team worked with

UN agencies to create CODs and FODs for the Valley. These are registries of where GI

can be found, as opposed to a repository of that GI; the GI is updated over time and the

CODs/FODs allow response organisations to find out what relevant GI is stored and

where.

<Key Point Model> {NGO2a} thought that humanitarian organisations ought to

standardise their information systems, perhaps using common data formats. When these

organisations need to work together, correlating information is difficult if the data

sources differ so much.

{PRI1a} explained that Google’s map of the whole of Kenya was originally developed

in about ten months by seven students in Kenya and a support team in India. The

exercise was done by remote sensing (tracing roads and towns from aerial images onto

a digital map) and supplemented by local knowledge. There was no ground truthing

and no GPS devices were used {PRI1a}.

In the event of a disaster, the International Charter Space & Major Disasters can be

invoked to release satellite imagery (International Charter, 2000; {NGO1b};

{NGO2a}). Where the disaster zone is particularly large, aerial imagery may be the

fastest way to identify priority areas {NGO2a}.

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However, aerial imagery has to be ground truthed. {NGO1a} recalled that after the

Asian Tsunami hit coastal areas on 26 December 2004, UN officials were insisting that

a particular railway track must still be serviceable based on aerial images. Aid workers

on the ground reported back that the track had in fact been flipped over; it might look

fine from above but close up it was clearly unusable. {NGO2a} explained that ground

truthing takes time and images may be out of date by the time relief teams reach the

areas; in 2005 in Kashmir, for example, it took relief teams a month to reach the worst-

affected areas.

If a disaster area has no existing base map, post-disaster crisis mapping has to record

features and assess the extent of damage. If there is an existing base map, in theory,

post-disaster mapping efforts can be focussed on damage assessment. {UNI3b} agreed

in part, but noted that currently, post-disaster damage assessment initiatives tend to

assume there is no base map; assessment is based solely on aerial images. As map

coverage of developing countries gradually improves, that approach will change,

reducing the post-disaster workload.

6.2.3. Social influence (Subjective norm){NGO2b} had found generally a big difference in attitudes to disaster management

between industrialised and developing countries. Disaster management initiatives had

to overcome problems with bureaucracy, sustainability and scalability. <Key Point

Model> Partner organisations often had different agendas, engaged in power struggles,

and had different cultures and ways of working.

<Key Point Model> {NGO8a}, {NGO12a} and {NGO13a} all reported that

international organisations working in developing countries are likely to be more

successful in engaging the community for mapping projects if the organisations work

closely with the communities and consult on their priorities. On the Map Kibera, Map

Mathare and Map Mukuru projects, {NGO8a} and GroundTruth colleagues spent a lot

of time working in the Nairobi slum communities, and employed a local person as key

contact. When mapping Eastern Indonesia, HOT worked with Access – an existing

local NGO – who actually did the community engagement {NGO12a}. “This worked

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rather well since they are already very engaged within those communities,” {NGO12a}

said.

<Key Point Model> {NGO8a}, {NGO9c} and {NGO12a} all commented that there is

no culture of volunteering in developing countries. To an extent, local people’s

scepticism about helping outside organisations for free was understandable. There are

many organisations that use the public to help run surveys, gather GI and so on; they

then use the outcomes for private profit. On the Map Kibera, Map Mathare and Map

Mukuru projects, the organisers found that the culture in the Nairobi slums (as

elsewhere) is that there is no tradition of volunteering; people expect to be paid to work

for NGOs, even if the amounts are small. Consequently, all their team members are

remunerated {NGO8a}. The issue of remunerating contributors is discussed in more

detail below.

{NGO1b} gave examples of how good information management can be undermined by

political influences. For example, during a recent deployment in El Salvador the local

authorities wanted to play down the casualty figures; those responsible for managing

and preventing landslides were embarrassed by the figures. Conversely, some response

organisations would prefer higher estimates as this helps with fundraising.

It might be expected that using GI for gap analysis (identifying areas in need) would be

an attractive proposition. Sometimes cultural and political pressures can actually turn

this on its head. For example, {NGO10a} related how the Humanitarian OSM Team

was told that mapping a particular area of Indonesia was not welcome because it had

yet to receive promised aid from central government and this was a source of

embarrassment.

6.2.4. Facilitating conditions (Perceived behavioral control)

{NGO1a} and {NGO2b} – both from MapAction – differed about the extent to which

local populations in developing countries were capable of running and maintaining a

GIS. {NGO1a} believed local populations were capable, provided the financial support

was secure. {NGO2b} noted that successful mapping projects were rare.

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<Key Point Model> {DLG2a} was responsible for the census and mapping aspects of

the Peace Recovery and Development Programme in Mbale, Uganda. He was of the

opinion that his District Local Government did not have the skills, equipment and

procedures to respond to this central government initiative. He explained that the

District needs a system that they can develop and maintain themselves.

{UNI1a} claimed that NGOs are often better positioned to respond promptly to data

requests than governments. NGOs see the need to improve data exchange and want to

do something to improve the situation. In some cases NGOs have better equipment and

more highly skilled staff than government organisations, though this is not universal.

Training was important to a degree. {NGO2a} had found that many local NGOs have

limited access to even the most basic IT tools. GIS and GPS technologies are beyond

the capabilities of many local NGOs. MapAction had been building mapping capacity

in developing countries since 2004. {NGO2b} had found that many trainees had used

their training to gain promotion, perhaps as GIS experts; unfortunately this meant that

their mapping skills left with them. {NGO2b} now felt that the logic for training-led

approaches is not robust. Before proceeding with any further such projects he instituted

a review of a recent MapAction project in Zambia where local people were trained in

the use of QGIS, an open source GIS package. As a result of that review, MapAction

now ensures it is working with local organisations that can act as intermediaries

{NGO13a}. Those intermediaries will encourage trainees to continue to use their skills

in the longer term.

The review of the project in Zambia concluded {NGO13a}:

For the majority of participants, use of GIS in day jobs increased following

QGIS training.

As a result of that training, participants developed a project to update all their

mapping using QGIS.

Only half of the participants felt that their use of data improved as a result of the

training.

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Fewer than half felt that data sharing had improved because of the training

(citing policy issues, the costs associated and the lack of forums through which

to share data).

Metadata and data management generally was not well used following the

training and the report recommended that an end to end methodology for spatial

data management could be useful.

The UTAUT construct Facilitating conditions is defined as the degree to which an

individual believes that the organisational and technical infrastructure exist to support

use of the technology (Venkatesh et al., 2003; my emphasis). The next few points are

about actual facilitating conditions, rather than beliefs about them.

<Key Point Model> HOT had experimented with providing hardware to local teams.

{NGO12a} found it useful to provide what they call ‘HOT Kits’: a robust box that

contains all the equipment needed to participate, pre-configured to work together. The

kits are left with NGOs to provide community access, though sometimes negotiating

access could be difficult. Experience with the HOT Kits was mixed {NGO12b}.

Experience in Indonesia indicated that a bank of kits would be more useful than one kit;

that way a team could work together on surveying.

<Key Point Model> The reliability of utility supplies can be important. For example,

frequent power cuts and Internet outages make it difficult to access and record GI

{NGO2a}. Problems are even more acute after a disaster, when bandwidth can be so

limited that disaster response teams are unlikely to receive much additional GI once

they reach the affected area {NGO2a}.

IPRs can restrict the use of existing GI {NGO2a} {NGO9b}. {NGO9a} was a technical

adviser working on a WASH project that would use OSM for natural resource

mapping, and for flood/drought management. Although he had access to water-related

GI from NGOs, on reviewing the copyrights it proved rather difficult to incorporate

that GI into OSM because that data would be made freely available to the public

{NGO9b}. He had also identified that the Uganda National Forestry Authority had

various GI datasets, but their charges were too high for his project {NGO9c}. He

foresaw that other public sector organisations – including the Uganda Directorate of 218


Water Resource Management – would treat their GI as a commodity to be sold in

future.

<Key Point Model> Aerial imagery of a target area is helpful but costly {NGO2a}

{NGO6b} {PRI3a}. {PRI3a} said he found it useful to be able to connect up roads

recorded on GPS by looking at aerial images on OSM.

<Key Point Model> The Uganda National Park Authority demonstrated that it is

possible for local organisations to run and maintain a GIS provided it has been set up

well, procedures have been established and staff have been trained. {QAN1a}

described how Park Rangers record incidents and sightings in the Mount Elgon

National Park; the system was standard throughout the national parks in Uganda. Each

Ranger had a Garmin GPS device which they used to record the location relevant

events such as tree cutting, land cultivation, forest fires and mammal sightings. At the

time of interview, the system had been in daily use for over a year, which demonstrates

that locals are capable handling GPS, recording GI and running a GIS provided they

have reliable facilities.

6.2.5. Hedonic motivationThis topic was not raised by any interviewees. Other forms of motivation were

mentioned and they are discussed in section 6.2.9.

6.2.6. Price value<Key Point Model> In general, hardware and software use is very price-sensitive in

developing countries. {DLG2a} was responsible for the census and mapping aspects of

the Peace Recovery and Development Programme in Uganda. Initially he was of the

opinion that the District Local Government did not have the skills, equipment and

procedures to respond to this central government initiative. He explained the need for a

system that Council staff can develop and maintain themselves. <Key Point Model>

when he learned about modern mapping technologies, he was enthusiastic about their

relative economy and simplicity. The cost of consumer-grade GPS was well within his

budget, and the software/GI was free to use.

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{NGO1b} suggested that a data logger GPS would be an economical and robust

alternative to a full-function GPS. {NGO12c} said that HOT was experimenting with

cheap Android phones and GPS data loggers.

6.2.7. Habit<Key Point Model> Collection of detailed, accurate information requires teams of

trained people {DLG2a} {NGO6b} {NGO8a}. Entering that information into a GIS

might be more effective using a smaller team {NGO6b} {NGO8a}. This is because the

software can be quite difficult to use, but becomes easier with frequent use. People who

use the software infrequently tend to make more mistakes {NGO6b}.

<Key Point Model> One of the reasons that the Uganda National Park Authority’s

previously-mentioned GIS was successful was that it was used every day. Each Park

Ranger had a Garmin GPS device which they used to record the incidents and sightings

on their daily patrols. For over a year they had been uploading the data into MIST-GIS

for analysis, and into ESRI Arc View to identify trends and monitor warden activity.

6.2.8. Behavioral intention and Use behaviorDuring the fieldwork (see section 6.4) it was relatively easy to get people to say they

intend to use mapping technologies (Behavioral intention), but more difficult to get

them actually to use it (Use behavior). This was corroborated by interviewees. For

example, in relation to a WASH project in Uganda, {NGO9c} said that it was proving

difficult to get staff at the partner NGOs to record and report GI, even though most of

them had GPS-enabled phones. In a similar vein, {NGO9c} had been promoting the

concept of open data in meetings with the Uganda Ministry of Water and Environment;

they were receptive to the idea but no action had been taken so far. {NGO2b} agreed,

and suggested investigating the Map Kibera project since it was a rare example of local

mapping that actually worked well. It would be interesting to find out why it worked

when so few similar initiatives do.

The Map Kibera project mapped the Kibera slum area in Nairobi. It was organised by

GroundTruth Initiative LLC. {NGO8a}, one of the founders of GroundTruth, explained

how they organised the Map Kibera Project. Initially Map Kibera required a lot of

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investment of time and money. GroundTruth worked within the community to develop

the map. <Key Point Model> Team members were trained in the use of GPS and

software, and received a stipend for their time. They used consumer-grade Garmin

GPSs and the JOSM editor to record features in OSM. GroundTruth initially trained

some 20 to 30 people in the use of GPS and mapping software. About 13 of them

became committed team members {NGO8a}.

GI projects are more likely to be successful in the long term if there are drivers for

change from others:

National initiatives for collecting GI, perhaps driven by central government in

each nation {DLG2a} {DLG4a}. An example of this is an initiative by Ugandan

central government’s Peace Recovery and Development Programme, that

required inter alia a digital map of all of Uganda;

International initiatives from large NGOs {NGO6a}. For example, the flood

early warning system in the Manafwa Basin, Uganda, that was driven by the

American Red Cross.

Because motivating people to map their localities can be difficult, an alternative

sometimes used by HOT is to find groups that are already mapping the area, and then to

show them the benefits of using OSM {NGO12a}.

6.2.9. Interview themes that do not feature in UTAUT2<Key Point Model> {NGO12a} said, “It would be very difficult to do much data

collection without some sort of remuneration. People may get participants for short

periods of time but I don't see how it would be effective long term.”

When HOT developed a mapping project in Eastern Indonesia, the regular team

members were almost entirely local salaried employees; even student volunteers were

paid a stipend. {NGO12a} was the only expatriate who was regularly involved; other

expatriates are brought in only if their specific expertise is needed. {NGO6a}

experienced similar attitudes in Eastern Uganda. Early in 2012 {NGO6a} led a

mapping exercise for a flood early warning system. The team of 12 local people who

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collected GI received a stipend. {NGO9c} agreed that remuneration is important,

saying,

To be honest, money is the driving (sole?) factor in basically all of Uganda’s

NGO work [...] sounds contradictory at first, but nearly all organisations are

seriously cash starved and have been founded as job opportunities and nothing

much else.

<Key Point Model> {NGO8a} also found that it can help to have the occasional big

initiative to motivate communities. For example, in the run up to Kenyan elections,

maps of the Nairobi slum projects were updated to show electoral boundaries and the

location of polling stations.

<Key Point Model> Sustainability in mapping was widely agreed to be problematic

{NGO2b} {NGO6a} {NGO8a}. {NGO6a} wrote,

Agreed that sustainability of mapping is a huge issue. That's why all our

mapping work is approached through the lens of disaster preparedness projects.

We're not trying to teach people to map, we're trying to teach them to prepare

better for disasters, and that just happens to include mapping. We tie OSM and

technical knowledge to important and accessible use cases that have long term

legs with the Red Cross National Societies we partner with, and ideally the

communities they work with. Still, it's incredibly hard to build a sustainable

knowledge base.

<Key Point Model> Although obstacles to sustained use of technologies might include

technical elements, some interviewees agreed that the root cause might not be

technical. {UNI3b} identified that psychology plays a large role in terms of how to

motivate someone, retain that motivation and so on. Similarly, based on MapAction’s

experience in many countries over a period of several years, {NGO2b} agreed that the

key problems to be addressed were more related to social science than technology. He

thought that the Technology Acceptance Model and the Theory of Planned Behavior

(see Chapter 4 “Behavioural Models”) neatly highlighted the problem of moving from

intention to actual use of mapping technologies. 222


There is plenty of evidence from log records of OSM and Google Map Maker that

teams of students can be useful for short-term mapping projects. The Fruits of Thought

organisation has held mapping parties at various universities including Livingston

International University, Mbale, Kumi University to the north, and Busitema University

to the south (Mapping Day, 2014). Sustainability for this group can be problematic.

HOT also used teams of students sometimes; {NGO12a} reported that students are

often willing to engage but often the majority of the work would have to be done by

someone else.

As a result of a review of their capacity-building projects, MapAction now ensures that

there is a local organisation that can act as an intermediary to encourage longer term

use of mapping and GIS skills {NGO13a}.

<Key Point Model> There were several stories of technical equipment languishing

unused when training atrophied or outside funding ended. As previously mentioned,

{DLG3a} explained that Mbale District Local Government had a PC with ESRI

ArcView 3.2 in 1998, but the system hadn’t been used for many years and the GI was

out of date. In the experience of {NGO3a} if equipment is not maintained it is mainly

because administrators misappropriate funds; he suggested that this is an indication of

low pay rates in developing countries.

6.3. SurveyIn January 2011, 14 delegates at a training course in Mbale, Uganda, were shown a

variety of mapping technologies. At the end of the course, they completed a short

questionnaire to record their opinions about those technologies.

Delegates were introduced to the basic concepts, how GPS devices work, how best to

survey an area, how to use the mapping systems, and how to transfer data from GPS to

the mapping systems. Technology used included:

GPS devices:

o Garmin Zumo 550;

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o iBlue 747A Data Logger;

o HTC Desire smartphone.

Cameras:

o Panasonic Lumix DMC-FZ28 camera (without geo-tagging);

o HTC Desire smartphone (with geo-tagging).

Software:

o Native PC-based software:

Garmin MapSource;

GPS Photo Tagger;

Java OpenStreetMap (JOSM).

o Smartphone app:

Trip Journal for Android.

o Browser-based:

OSM, including Potlatch;

Google Map Maker.

Delegates participated in a survey of their opinions on the various technologies they

used, and indicated which ones they would like to use in future. The results are

summarised in Table 6.16.

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Count(n=14)

Opinion(out of 5)

1. I used this GPS:Garmin Zumo 8 4.3iBlue Data Logger 3 5.0Smartphone 2 2.0

2. I used this camera:Large camera with separate GPS 0Smartphone camera 2 3.5

3. I used this software:Garmin MapSource 4 3.8GPS Photo Tagger 4 3.0Trip Journal (smartphone) 2 2.5OpenStreetMap (web) 10 3.9OpenStreetMap editor (web) 10 4.0JOSM 10 3.7Google Map Maker 8 3.4

4. I think that I would like to use this equipment and software:

Garmin Zumo 7iBlue Data Logger 8Smartphone 4Large camera with separate GPS 2Smartphone camera 0Garmin MapSource 3GPS Photo Tagger 1Trip Journal (smartphone) 1OpenStreetMap (web) 3OpenStreetMap editor (web) 4JOSM 6Google Map Maker 5

Table 6.16: Results from survey of training course delegates

<Key Point Model> It is important to note that, although all respondents indicated that

they would like to use several of the above technologies to map their locations, analysis

of OSM and Google Map Maker revealed that none had done so during the following

12 months.

6.4. Empirical fieldwork case studiesIn this project a variety of different approaches to mapping areas in East Africa have

been attempted or observed and then evaluated.

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Outside initiation then hands-off:

o Broad-based capacity-building training course.

o One-to-one training and fieldwork.

Local organisation initiation and with expatriate support:

o NGO mapping party (Mountbatten).

o NGO expatriate volunteers.

Outside organisation initiation with ongoing support:

o Red Cross flood early warning.

o Community mapping: Map Kibera.

6.4.1. Broad-based capacity-building training courseAfter gaining experience of crowdsourced mapping around Marrakech, Morocco (May

2008) and Mbale, Uganda (July 2009 and January 2011), PONT asked for capacity

building for one of the United Nations’ Territorial Approach to Climate Change

projects (UNDP, 2013). The district government and local NGOs needed to map the

locations of their various climate-related projects, and the author was asked to train 14

delegates in January 2011.

Some of the officers had some prior experience of mapping, though the GPSs from a

previous project could not record track-logs and points of interest could be displayed

only a latitude and longitude numbers; there was no map or graphical display. As

mentioned previously, Mbale District Local Government had a copy of ESRI ArcView,

but officers admitted that both the software and GI were out of date, and that the PC

was no longer in working order.

After introducing the basic concepts and a range of GPS devices, best practice in

mapping, how to transfer GI from GPS to PC, and how to use OSM were explained.

Trainees practiced recording GI using track-logs that the author had recorded

previously, and then they recorded some track-logs locally themselves. At the end of

the first day a few trainees borrowed GPSs to record GI from their hometowns; the GI

was recorded the next day. Despite giving trainees easy-to-follow guidelines on best

practice, not all trainees applied them. The track-logs were not very reliable. For

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example, road junctions were not easy to identify, and at least one group had zig-

zagged along dirt tracks and footpaths between houses but claimed they had followed

roads. The need to double check GI was evident. <Key Point Model> Even so, trainees

appeared to enjoy the event and seemed pleased to see features they had recorded

appear on OSM and Google Map Maker.

The trainees were introduced to some alternative technologies, such as a smartphone

with GPS, an alternative OSM editor, and the Google Map Maker system. Delegates

completed a small survey about the technologies as described in section 6.3.

<Key Point Model> Even though trainees seemed keen to use the mapping

technologies, records from OSM reveal that none of them actually did. It is more

difficult to be sure about Google Map Maker, but there is no evidence that any trainees

updated this system either. There may have been several contributing factors. One

possible hindrance was that the only GPS capable of recording track-logs was held in

the PONT office in Mbale; for many trainees, travelling from home to Mbale, to a point

of interest, back to Mbale, and then home could take an entire day. Locally available

GPSs might have helped.

6.4.2. One-to-one training and fieldwork.The Welsh Government asked this author to return to Mbale in April 2012 to advise

UN staff on mapping technologies in relation to the local Territorial Approach to

Climate Change project (UNDP, 2013). There would also be time to meet with staff at

the same government and non-government organisations as before.

Because no trainees had recorded any GI on OSM in 2011, an attempt was made to

contact them. The intention was to reinforce the previous training with some one-to-one

training and some fieldwork actually mapping the GI they needed. <Key Point Model>

It transpired that over half of the 2011 trainees had since changed jobs, many having

moved to another part of Uganda. Four trainees who were still in the Mbale Region

were invited to meet the author individually, but none of them attended. Two of the no-

shows worked at one of the District Government offices, but another colleague of theirs

was keen to learn about mapping. The author showed him the technologies and

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travelled together to the location of an imminent landslide to record its location with a

GPS. The location was recorded on OSM.

<Key Point Model> The author left a GPS and related software with this trainee so he

could map more GI as needed. Even though the trainee and his line manager promised

that he would use the mapping technologies, records from OSM reveal that actually he

did not. When asked later, he said that other priority tasks were competing for his time.

6.4.3. NGO mapping partyAn organisation called Fruits of Thought invited the author to address the April 2012

meeting of the Ugandan Linux User Group in Kampala en route home. Many attendees

were already familiar with GPS and OSM. The event included a review of the basic

concepts, and an introduction to JOSM, an offline editor for OSM. Each attendee used

some of the GI that the author had collected during his time in Mbale in order to update

OSM with the editor.

Fruits of Thought is the social responsibility arm of Mountbatten Ltd, Kampala. It is

run by expatriates from the Netherlands. Fruits of Thought has a good track record of

organising ‘Mapping Day’ parties in various parts of Uganda, mostly drawing on

university students (Fruits of Thought, 2012).

<Key Point Model> Attendees appeared to enjoy the event and appeared pleased to see

features they had recorded appear on OSM. Because there were so many attendees, a

large number of (pre-recorded) features were mapped in a fairly short time.

6.4.4. NGO expatriate volunteers Two volunteers had also undertaken mapping in the Mbale Region. The first was an

expatriate working in Tororo during 2012. He had developed quite a lot of expertise

with OSM. He added features, mostly relying on and interpreting whatever aerial

images were available, as a hobby in his spare time.

The second worked during 2013 as a data and supply chain coordinator working for

Kissito Healthcare (www.kissito.org), an NGO that operates healthcare centres in

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Eastern Uganda. He had graduated from James Madison University as a Geography

Major with some experience of mapping. He was tasked to map the locations of Kissito

healthcare centres in order to help visualise their operations, manage shipping logistics,

and coordinate with the motorcycle ambulances in the region. Although he had

experience of ESRI software, obtaining software through the Nonprofit Organization

Program was proving difficult, so he contacted the author to explore the use of OSM.

<Key Point Model> Even though this contributor was a Geography Major, he

experienced several problems with the technologies and needed quite a lot of help and

perseverance to be able to record the features on OSM. After using the technologies for

a few weeks, he became more familiar with them and needed little help. This reinforces

that mapping technologies are not easy to use, but familiarity is gained by regular use.

6.4.5. Red Cross flood early warningThe Manafwa River Basin is a large marshy area to the West of the Mbale Region, as

shown in Figure 6.8. It frequently suffers from flooding when the Manafwa River

carries rainwater down from Mount Elgon (American Red Cross, 2013).

Figure 6.8: Map of Manafwa River and Manafwa River Basin (c) OpenStreetMap contributors (key features highlighted).

Staff from the American Red Cross, Washington, travelled to the Mbale Region to train

colleagues from Ugandan Red Cross in mapping. Local teams mapped the communities

in the river basin area and access routes to the Manafwa River and its tributaries; <Key

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Point Model> all Ugandan contributors were remunerated. The author mapped the

River and many of its tributaries from aerial images the Red Cross had obtained for

OSM. Local teams ground truthed these watercourses and added the access routes to

OSM.

6.4.6. Community mapping: Map KiberaMost of the following information came from an interview with {NGO8a}, one of the

founders of GroundTruth Initiative (www.groundtruth.in) in 2010.

<Key Point Model> As explained in a previous section, GroundTruth worked within

the community to develop the map of Kibera so that it covered the whole slum. Initially

GroundTruth trained some 20 to 30 people in the use of GPS and mapping software.

About 13 of them joined the team. They used consumer-grade Garmin GPSs to geo-

locate features and the JOSM editor to enter them into OSM. The mapping work was

subsequently repeated in the Mathare and Mukuru slums. Initially each project required

a lot of investment by GroundTruth. It has proven difficult to gain local ‘ownership’ of

the projects, though it helps if the mapping teams have members from each of the

localities within a slum. GroundTruth has appointed a local person in Nairobi as

Executive Director, and this helps to overcome barriers.

<Key Point Model> The culture in the Nairobi slums (as elsewhere) is that people

expect to be paid a stipend to work for NGOs, even if the amounts are small {NGO8a}.

Shkabatur (2012) mentions a reservation that paying for GI might contradict the

principles of OSM, but concedes that the Map Kibera project tried to uphold this and

failed. In the first stage of the Map Kibera Project, local contributors received a stipend

as a mark of appreciation for their help. During the second stage, the Map Kibera team

attempted to identify a local self-supporting funding model, but eventually ended up

paying a stipend to the contributors. In the subsequent Map Mathare and Map Mukuru

projects, all contributors were remunerated {NGO8a}.

GroundTruth’s approach appears to be repeatable; it has subsequently helped residents

map the Tandale slum in Dar Es Salaam, Tanzania, in a World Bank project.

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6.5. Chapter summaryThe interviews have recorded in this chapter set out the opinions of various experts

based on their practical experience in disaster management, mapping and

crowdsourcing. Key points about how to ensure a successful crowdsourced mapping

initiative have been identified. The case studies have similarly identified key points;

those points were learned both from case studies that were relatively unsuccessful in

generating disaster-relevant GI (sections 6.4.1 and 6.4.2) and from those that were

more successful (sections 6.4.3 to 6.4.6). The Red Cross and Map Kibera case studies

are particularly good examples of well-organised and successful initiatives. By

analysing interviews and fieldwork thematically, this chapter has laid the foundations

for two of the project’s key deliverables, the Theory of Acceptance and Sustained Use

of Technology (TASUT) model (Chapter 7), and guidelines for applying the TASUT

model to mapping for disaster preparedness (Chapter 8).

In the next chapter, key points that are considered important to a successful mapping

initiative are categorised into ‘theoretical codes’ according to grounded theory, that is,

put under headings that explain the proposed TASUT model.

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7. Proposed TASUT modelEssentially, all models are wrong, but some are useful.

(Box and Draper, 1987: p424)

In this chapter, the information gleaned from interviews and the experiences gained

from case studies are compared with the Unified Theory of Acceptance and Use of

Technology 2 (UTAUT2) model. Where necessary, the model is changed – perhaps by

removing, merging or adding constructs – to match the lessons learned and produce a

new model, the Theory of Acceptance and Sustained Use of Technology (TASUT).

When proposing a change to a model such as UTAUT2, three fundamental decisions

have to be made.

First, should the new model start with a ‘blank sheet of paper’ and draw constructs

solely from the empirical fieldwork? Or should an existing model be adapted in

accordance with the empirical results? Given that UTAUT2 was based on extensive

experience with previous models, there is value in building on it, especially as

interviewees may have made an unspoken assumption that certain determinants were

active. However, where a construct has received little or no support in the empirical

fieldwork, it will be a candidate for deletion or merging with another construct.

The second question is related: to what extent should the new model be specific to a

given situation? Should the model be generalised to apply to many situations, or

tailored specifically to the problem domain? For example, the Theory of Reasoned

Action and Theory of Planned Behavior are deliberately quite general models; they can

be applied to decisions about almost anything (e.g. what to buy, where to travel)

whether or not related to technology. Conversely, UTAUT2 is specifically focussed on

consumer technology. This second question is represented diagrammatically in Error:

Reference source not found.

232

Figure 7.1 Alternative ways to develop a model.

Scope of existing model

Current situation:

Scope of new model

Make more generally applicable:

Make more specific:

Needs of crowd-

sourced disaster mapping

Needs of crowd-


Needs of crowd-


Ge

ner

ali

se

Refocus

Scope of newmodel


In order to make a model more widely applicable, one might simplify, generalise some

existing constructs, and add extra constructs to the original. To make a model more

specific, one might add detail, rephrase existing constructs to refer to specific problems

with crowdsourced disaster mapping and remove constructs not considered relevant.

In practice the proposed TASUT model takes the middle ground. As far as possible, the

proposed model should be generalisable so that it can be applied in other contexts and

in other parts of the world. The words ‘crowdsourcing’, ‘mapping’, ‘disaster

preparedness’ and ‘East Africa’ are not used and the model can be adapted for use in

other situations. Nevertheless it has been developed from experiences in crowdsourced

233


mapping for disaster preparedness in East Africa and some aspects of the model may

not be sufficient in other contexts.

The third question is, how important is parsimony? Should the model show only those

determinants that are most relevant to the situation under consideration? To what extent

should additional determinants be added if they improve accuracy only slightly? For

example, the original TAM and original UTAUT may not be comprehensive, but they

are relatively easy to understand and remember. TAM3 and UTAUT2 are more

comprehensive but more complex. In devising a new model, it may be the considered

that a construct that was important in other circumstances is less important to

crowdsourced disaster mapping and thus be removed; this does not mean the construct

is irrelevant, but simply that a model’s complexity needs to be managed. Conversely, a

factor that was previously acknowledged but not shown explicitly may be considered to

be important enough to appear in the new model. However, it would be easy to add

constructs to the model to reflect every nuance identified, but the model would then

become unwieldy and probably less generalisable.

As indicated in the answer to the first question, the design of the proposed model aims

to be parsimonious, but sometimes a construct is found to be sufficiently important that

it cannot be ignored. The proposed model in section 7.5 has 13 constructs; UTAUT2

has 9 constructs (and 3 moderators). So the proposed TASUT model is not as

parsimonious as the one on which it is based, but it does ‘reach’ further temporally

because it models use behaviour in both the short and long term.

7.1. Analysis of key pointsIn the literature review chapters, where an issue has been identified as important for

modelling technology acceptance it was marked with the <Key Point Model> symbol.

These are summarised in tables 7.1, 7.2 and 7.3.

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Sec-tion Requirement

Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct2.2.2 For collaboration to be

beneficial, certain conditions must exist

Y Actual facilitating conditions

2.4.2 Contributors should not be inconvenienced or exposed to danger

Y Motivation

2.4.2 Contributors should not be subjected to harmful effects

Y Motivation

3.1 Technical difficulties could be overcome if there is motivation

Y Motivation

4.2.2 Motivators should be geographically and temporally near

Y Motivation

4.2.3 Fear as a motivator should be used carefully

Y Motivation

4.3.1 Sustained use requires knowledge, persuasion, decision, implementation, confirmation

Y Sustained use behavior

4.3.1 Sustained use a separate concept from initial use

Y Y Sustained use behavior

4.4.2 Stable conditions are needed Y Stable context

4.4.2 Actual behavioural control is important


235



Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct4.4.3 Actual use is dependent on

factors other than intentionY Y Y Determinant

4.4.10

Actual behavioural control is important


4.5.1 Concern for basic needs and safety outweigh higher-level needs

Y Motivation

4.5.2 Consider intrinsic and extrinsic motivation

Y Motivation

4.5.3 Extrinsic motivation is important when intrinsic motivation absent

Y Motivation

4.5.5 Consider various types of intrinsic and extrinsic motivation

Y Motivation

4.6.1 Distinguish between initial use and sustained use

Y Sustained use behavior

4.6.2 Satisfaction is important for habit

Y Y Satisfaction

4.6.3 Habit is important for sustained use

Y Y Determinant

4.6.3 Satisfaction, frequent use and stability are important for habit

Y Y Satisfaction, Frequent use, Stable context

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Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct4.6.3 Habit, satisfaction and

avoiding niche usage are important for sustained use

Y Y Y Satisfaction, Frequent use

Table 7.17: Key points from the literature review in chapters 2, 3 and 4


Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct6.2.1 Shared GI is useful for daily

work of GOs and NGOsY

6.2.1 GI benefits go wider than one project

Y

6.2.1 GI is useful during disaster response

Y

6.2.2 Repositories of GI need to be discoverable in event of disaster

Y

6.2.2 Correlating information in different formats is difficult

Y

6.2.2 Organisations engage in power struggles and have different cultures

Y

237



Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct6.2.2 More likely to engage

volunteers if consult communities’ priorities

Y

6.2.2 No culture of volunteering Y

6.2.3 Organisations need skills, equipment, procedures

Y

6.2.4 Pre-configured equipment helps

Y Y Actual facilitating conditions

6.2.4 Unreliable utilities are a hindrance


6.2.4 Aerial images are useful Y Y Actual facilitating conditions

6.2.6 H/w and s/w use is price sensitive

Y

6.2.7 Software is easier to use when used frequently

Y Y Y Sustained use

6.2.7 Daily use encourages habit and sustained use

Y Y Sustained use

6.2.86.2.9

Team members expect remuneration

Y Y Motivation

6.2.9 A big initiative is a social motivator

Y Y Motivation

6.2.9 Sustained use is problematic but important

Y Sustained use

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Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct6.2.9 Social issues play a large role

in motivating peopleY Motivation

6.2.9 Lack of maintenance hinders sustained use

Y Actual facilitating conditions, Sustained use

6.2.9 Repeat training is needed for sustained use


6.2.9 Secured funding for maintenance is needed for sustained use

Y Y Y Actual facilitating conditions

Table 7.18: Key points from interviews in Chapter 6


Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct6.3 Stated intention does not

necessarily lead to actual useY Y Y Other determinants are

needed6.4.1 Trainees appeared to enjoy

using technologiesY

6.4.1 Trainees did not actually use technologies

Y Y Y Y Y Actual facilitating conditions, other determinants are needed

239



Performance

expectancy

Effort expectancy

Social influence


Hedonic m

otivation

Price value

Habit


Use behavior New construct6.4.2 Most trainees changed job

soon afterwardsY Stable context

6.4.2 Trainee did not actually use technologies

? Y Y Y Actual facilitating conditions, Motivation

6.4.3 Mapping party-goers appeared to enjoy communal aspects of event

Y Y

6.4.4 Volunteer experienced several problems

Y Y

6.4.5 Contributors were remunerated

Y Motivation

6.4.6 Difficult to gain local ‘ownership’ of project

Y Y Motivation

6.4.6 More likely to engage volunteers if members come from each locality within the target area

Y

6.4.6 Contributors expect remuneration

Y Motivation

Table 7.19: Key points from empirical fieldwork in Chapter 6

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Performance expectancy 1


Figure 7.2: Unified Theory of Acceptance and Use of Technology 2 (Venkatesh et al., 2012).

Use behavior

Effort expectancy 2

Social influence 3

Facilitating conditions 4

Experience

AgeGender

Moderating variables

Hedonic motivation

Price value

Habit

Notes:1. Moderated by age and gender.2. Moderated by age, gender and experience. 3. Moderated by age, gender and experience.4. Effect on use behaviour is moderated by age and experience.


An interpretive approach was taken to analysing the data from the literature, interviews

and case studies. In the above tables the key points are mapped to the UTAUT2

constructs to identify whether the model caters for each of them; where a key point

requires a new construct, this has been identified in the table.

7.2. Changes to existing constructsThe key points listed in Section 7.1 are examined here to identify the extent to which

they are supported by UTAUT2 (reproduced in Error: Reference source not found for

ease of reference) and their effect on an enhanced technology acceptance model.

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7.2.1. Performance expectancy, Effort expectancy, Social influence and Facilitating conditions (UTAUT)

Performance expectancy – the expectation that the technology will produce useful

results – is supported by the key points from the interviews. If team members can

identify a use for GI in their everyday activities there will be a reason to use it.

Interviewees listed various uses for GI, including identifying local needs, gap analysis,

decision support, vulnerability analysis, supporting financial audit and so on (section

6.2.1).

Effort expectancy – will the system be easy to use? – was not explicitly mentioned by

many interviewees in relation to disaster preparedness (section 6.2.2). However, the

author suspects this was a significant reason mapping technologies were not used in the

Ugandan broad-based and one-to-one training case studies. In the NGO expatriate

volunteers case study (6.4.4) it was noticeable that a number of apparently minor

problems caused significant difficulties, even though the contributor was a Geography

Major. Conversely, the Ugandan National Parks Authority’s successful MIST-GIS was

set up professionally and the staff found it easy to use {QAN1a}.

Regarding disaster response, MapAction interviewees stressed that GI had to be

discoverable. Storing GI produced during disaster preparedness in a major

crowdsourced system, such as OSM and Google Map Maker, allows disaster response

organisations to find it quickly and easily. Two interviewees mentioned that they

preferred open access or Creative Commons sources since they avoid potential

problems when adapting and distributing GI. There was also support for GI to be stored

in formats that conform to international standards.

Social influence is potentially a major issue in any international work, especially when

people from significantly different cultures interact. If a mapping initiative is organised

by people from an industrialised country, there may be false assumptions about needs

and social conventions in developing countries. A mapping initiative should consult

local communities to determine their priorities. Also, since there is little culture of

volunteering in poorer sections of society in East Africa (6.2.3), contributors may need

242


to be remunerated. Local politics may hinder the effectiveness of local organisations.

Sometimes a big initiative is useful for motivating local communities.

In this thesis, the term Facilitating conditions has a broad meaning, including the

appropriate provision of funding, equipment, aerial images, training, operating

procedures, licences and permits. ‘Appropriate provision’ includes availability, ease of

access, sufficiency, compatibility, ongoing maintenance, and sustainability. Because

Facilitating conditions are so varied, it may be difficult to maintain them all. There

may not be the necessary skills, equipment or procedures in local organisations,

especially cash-strapped government organisations. The author suspects that difficulty

in accessing a shared GPS was a contributory factor in why the broad-based training

case study did not result in any usage. Funding needs to be provided for the short and

long term. Team members need to be protected from danger, both in terms of unsafe

working conditions and threats of violence (Chambers, 2006: p7).

All four of these determinants received support in the interviews, case studies and

literature, though there was somewhat less support for Effort expectancy than for the

others. In a parsimonious model, this determinant is a candidate for deletion or

combination with another determinant (such as Price value below).

7.2.2. Hedonic motivation, Price value and Habit (UTAUT2)

Hedonic motivation – is the system enjoyable to use? – was not volunteered as a factor

in any interviews and did not appear to be a major factor in any case studies. From the

broad-based training case study (6.4.1), it was clear that participants enjoyed the

mapping activities. However, the fact that none of the participants used the

technologies afterwards indicates that hedonic motivation alone is not significant. Some

other forms of motivation have been identified, though, and are discussed in

section 7.3.

Price value was also not explicitly mentioned as a major issue. Consumer-grade GPSs

are relatively cheap – especially GPS data loggers – so in this specific case Price value

was not a significant determinant of Behavioral intention. In a parsimonious model,

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this construct is a candidate for deletion or combination with another construct (such as

Effort expectancy above).

Habit – the frequent use of a technology – was found to be important in the literature

(sections 4.4.9 and 4.6.3), interviews (6.2.7), and one of the case studies (6.4.4). For

example, because the Ugandan National Parks Authority uses MIST-GIS every day,

employees’ skills don’t atrophy. Habit is considered to have relatively little conceptual

overlap with intention (Limayem et al., 2007: p709) and so provides independent

impetus to technology usage. So Habit is particularly important in supporting sustained

use.

Out of the three new constructs that Venkatesh et al. introduced in 2012, only Habit

received much support, and that was for sustained use rather than initial adoption. The

Hedonic motivation construct alone appears not to be important enough for a

parsimonious model, but – as discussed later – might to be expanded to include other

motives. In this particular problem domain, Price value similarly appears not to warrant

special mention in a parsimonious model. However, there seems to be a case for a new

construct that incorporates Price value, Effort expectancy and related concepts.

7.2.3. Behavioral intention and Use behavior (UTAUT and UTAUT2)

Trainees’ statements about their Behavioral intention – the conscious planning to use or

not use a technology – was found not to be a major determinant of actual use in the

case studies (for example, see 6.4.1 and 6.4.2) or in the experience of some

interviewees (6.2.8). This is contrary to most of the published studies of technology

acceptance models apart from Gupta et al. (2008). It may be the case that cultural

differences between East Africa and where the previous studies were conducted

(mostly in the USA) mean that people are less likely to say what their real intentions

are, or perhaps that other determinants of use are stronger. For example, if actual

facilitating conditions are unfavourable, or other determinants are absent, then Use

behavior cannot be guaranteed to follow on from Behavioral intention.

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A key to encouraging contributors to progress from intention to actual Use behavior is

for the users to have a motivation to use. That motivation does not have to be financial.

For example, a big initiative or mapping party may be effective. Motivation is

discussed in more detail below.

One of the ultimate objectives of the model is to encourage actual Use behavior, so it

must appear in the proposed model. In many of the earlier studies of technology

acceptance models, students were surveyed about their use of a technology over a

relatively short period, typically a semester. In such a situation, the distinction between

initial use and sustained use was not important, but in the literature and case studies,

Sustained use behavior was important so it will be considered in the next section.

7.3. New/revised constructsIn mapping for disaster preparedness, the GI needs to be kept up to date over periods

measured in years rather than weeks. Therefore Sustained use behavior is sufficiently

important for it to be shown separately, even in a parsimonious model. Determinants of

sustained use may differ from determinants of initial use.

In some situations we may need to go beyond Sustained use behavior. McCall and

Dunn’s Participation Ladder (2012) (section 4.6.4) highlighted the potential need for

Self-mobilised behavior too. In situations where extrinsic motivation is limited in

quantity and time, an additional construct may be warranted. None of the interviews or

case studies identified any successful examples of self-mobilisation in East Africa, so it

does not appear in the proposed model.

There is a problem with the original Price value construct: Venkatesh et al. (2012)

defined it as “consumers’ cognitive trade-off between the perceived benefits of the

applications and the monetary cost for using them”. However, perceived benefits are

also addressed by Performance expectancy, which leaves only monetary cost. The

constructs Effort expectancy and Price value appear to have some relevance, but they

were not mentioned explicitly by many interviewees (see 6.2.2 and 6.2.6), and were not

overt factors in the case studies. For the sake of parsimony, the two constructs have

245


been merged into one that covers all the costs whether monetary or in terms of personal

effort. For the sake of clarity this will be called Costs.

As mentioned previously, Hedonic motivation did not feature strongly, but one should

bear in mind that this construct appeared in UTAUT2 because it was devised for

consumer technology, where enjoyment would be an important concept. The interviews

and case study relating to Map Kibera (6.4.5 and 6.4.6), and the literature (4.2 and 4.5),

suggest that other forms of motivation – especially remuneration – are a major factor in

encouraging people to move from mere intention to initial and then sustained use. In

addition to hedonic motivation, Kaufmann et al. (2011) identified four other types of

motivation in relation to crowdsourcing; see Error: Reference source not found on page

Error: Reference source not found. Also, with special relevance to crowdsourced

disaster preparedness mapping, Extended Parallel Process Model (Witte, 1992)

identifies fear of (or concern about) potential disaster as an important motivator, and

there are several references to people motivated to contribute for the greater good.

Therefore there is a case for including a Motivation construct.

As indicated in Error: Reference source not found, the IS Continuance Model

(Bhattacherjee, 2001) relates Satisfaction directly to sustained use. The Predictive

Power Model (Limayem et al., 2007: p714-715) shows Satisfaction as one of four

antecedents of Habit; the other antecedents are Stable context, Comprehensiveness of

usage and Frequency of past behavior. Of these, Frequency of past behavior and

Stable context were specifically identified in the interviews. It is a matter of judgement

whether these are sufficiently important to be shown in a parsimonious model. These

could be rolled up into one over-arching construct, but the antecedents are shown

individually for two reasons. First, Regular use and Habit appear to be significant

determinants of sustained use so it may be worth emphasising them. Second, another of

Limayem’s antecedents – Satisfaction – is partially determined by Performance

expectancy.

246

Figure 7.3: Possible relationships between Performance expectancy, Habit and Sustained use behavior.


Stable context


Habit

Regular use

Satisfaction

Bhattacherjee, 2001 (dashed line)

Limayem et al., 2007 (solid lines)


Facilitating conditions from UTAUT and Perceived behavioral control from TPB and

TRA2 are similar, especially in that they are about the individual’s beliefs:

Facilitating conditions: the degree to which an individual believes that the

organisational and technical infrastructure exist to support use of the technology

(Venkatesh et al., 2003: p453).

Perceived behavioral control is the subject’s assessment of whether there are

sufficient resources (time, money, skills etc.) (Ajzen, 1991: p184).

As suggested by TRA2 (Fishbein and Ajzen, 2009), the interviews and case studies

confirmed that actual facilitating conditions – not merely beliefs about them – are

important too. There seems to be two ways to include actual facilitating conditions into

the model, either by redefining the Facilitating conditions construct to include both

actual conditions and beliefs about them, or by showing the two constructs separately:

Perceived facilitating conditions and Actual facilitating conditions (similar to

Perceived behavioral control and Actual control in TRA2).

The effect of Perceived facilitating conditions is mainly on intention, whereas Actual

facilitating conditions can help or hinder use (in the short and long term). The

distinction is sufficiently important to warrant showing Perceived and Actual

facilitating conditions as separate constructs.

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Figure 7.4: Constructs that need to be linked with Behavioral intention, Use behavior and Sustained use behavior.


Use behavior

Stable context

Performance expectancySocial influence

Perceived facilitating conditions

Motivation

Cost

Habit

Actual facilitating conditions

Regular use

Satisfaction


Error: Reference source not found summarises the constructs before they are connected

up by determinants.

7.4. DeterminantsHow the ‘originating’ constructs determine the three main ‘outcome’ constructs –

Behavioral intention, Use behavior, and Sustained use behavior – needs to be

considered. As with previous models, Performance expectancy, Social influence and

Perceived facilitating conditions are determinants of Behavioral intention and

consequently Use behavior. Use behavior is a prerequisite of Sustained use behavior.

Financial and personal Cost appears to be a determinant of both Behavioral intention

and Use behavior. It is not a major determinant of Sustained use behavior because by

then initial financial costs will be sunk, and the system will be familiar and therefore

easier to use.

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Satisfaction is partly about whether original expectations are met, so there is a link

between Performance expectancy and Satisfaction; high expectations may be good, but

if those expectations are not met the resulting dissatisfaction may result in ‘over-hyped’

technology not being used in the longer term.

Although perceptions about facilitating conditions – the availability of funding,

training, equipment, etc. – would influence a user’s intention, Actual facilitating

conditions are what influence initial and sustained use.

It is not clear why few earlier models included Motivation, yet it is key to establishing

intention and to encouraging actual use in the short term and to sustaining use in the

long term – important themes in this thesis.

Limayem et al. (2007: p714-715) and interviews suggest that Habit has three primary

antecedents: Satisfaction, Frequent (regular) use and Stable context. In turn, Habit has

been found to be a determinant of Sustained use behavior. It is not a determinant of

initial Use behavior because Habit requires time to form.

The Predictive Power model additionally shows Satisfaction as a determinant of

intention. Since Behavioral intention is meant to model attitude after

training/introduction but before frequent use, the user cannot yet assess Satisfaction.

Therefore the proposed model will not show a link between Satisfaction and

Behavioral intention.

7.5. Theory of Acceptance and Sustained Use of Technology (TASUT)

Error: Reference source not found shows the first working version of the TASUT

model that is based on the preceding sections. (This model is further refined in Error:

Reference source not found as a result of feedback from evaluators.)

249

Stable context



Figure 7.5: Working version of Theory of Acceptance and Sustained Use of Technology.


Social influence


Motivation

Cost

Habit

Use behavior


Frequent use

Satisfaction


DefinitionsBehavioral intention is the degree to which a person has formulated conscious plans to

perform or not perform some specified future behaviour. (Venkatesh et al., 2003).

Use behavior is the observable act of a person using a technology immediately after it

has been introduced. (Based on Fishbein and Ajzen, 1975: p13.)

Sustained use behavior is the observable act of a person continuing to use a technology

after outside initiatives and support have ended.

Performance expectancy is the degree to which using a technology is expected to

provide benefits to users in performing certain activities. (Based on Venkatesh et al.,

2012.)

Social influence is the extent to which users perceive that important others (e.g., friends

and colleagues) believe they should use a particular technology. (Based on Venkatesh

et al., 2012.)

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Perceived facilitating conditions refer to users’ perceptions of the resources and

support available to perform a behaviour. (Based on Venkatesh et al., 2012.)

Actual facilitating conditions refer to the resources and support actually available to

perform a behaviour.

Costs includes all the costs of using a technology, whether monetary or in terms of

personal effort.

Motivation encompasses all the factors that drive people to take an action (Kauffman et

al., 2011).

Habit is the extent to which people tend to perform behaviours automatically because

of learning (Limayem et al., 2007: p709).

Satisfaction is the degree to which the user’s positive expectations about a technology

are confirmed. (Based on Limayem et al., 2007: p708.)

Frequent use means the technology is used many times in a given period, and this helps

the user to remember how to use it.

Stable context refers to situational cues and relevant goals of the individual that are

similar (or the same) across consecutive situations. (Based on Limayem et al., 2007:

p715.)

Guidance on how this model might be applied to a mapping initiative is given in

Chapter 8.

7.6. Possible further enhancementsSome aspects of Error: Reference source not found could be developed further if

evidence were found to support changes.

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Figure 7.6: Possible need to include Self-mobilised behavior in some situations.

Use behavior


Self-mobilised behavior


Figure 7.7: Possible inclusion of aspects of motivation.



Use behavior


Hedonic motivation

Community-based motivation

Immediate payoff

Delayed payoff

Social motivation


7.6.1. ConstructsBeyond sustained use, Self-mobilised behavior could be important in some situations.

McCall and Dunn (2012) suggest that self-mobilisation is likely to come about only

after sustained use. None of the interviews or case studies identified any examples of

self-mobilisation in East Africa, so it is difficult to identify what the determinants of

self-mobilisation might be.

Motivation appears to be key to initial and sustained use, and a less parsimonious

model might devote more space to it, perhaps showing intrinsic and extrinsic

motivation or even more detail. As the extract in Error: Reference source not found

shows, this could create a cluttered diagram, especially as more and more motivators

are identified.

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There may be other motivators such as personal safety, which Maslow (1943)

suggested is fundamental. In the East Africa case studies, personal safety was not raised

as a particular concern. The only situation where safety was raised as an issue was

when reviewing options for recording watercourses with GPS, which could be quite

dangerous; the release of aerial imagery provided a safer alternative. In other disaster

preparedness situations, perhaps where lawlessness and banditry is rife, the personal

safety of the contributors could be sufficiently important to warrant it being shown as a

construct in its own right. For example, in Lindsay et al’s (2011) study of policing

technologies, ‘Officer safety’ and ‘Health and safety’ were indeed sufficiently

important to warrant separate constructs in their adaptation of TAM. Another potential

motivator might be the spiritual or religious beliefs of a person, though the literature is

mixed on this point. Maslow (1969) put spiritual ‘self-transcendence’ as the highest

motivator to act on behalf of other. Conversely, Misanya and Øyhus (2014) identified

that religious belief could significantly reduce a community’s propensity to prepare for

disasters. Their study in the Nametsi community in the Mbale region found that, “the

great majority explained the causes of the landslide from an indigenous perspective,

underpinned by a belief that God and other divinities played a part in the phenomenon”

(ibid: p6).

Interviews and case studies suggest that there is often a difference between what people

say they intend to do and what they actually intend to do. This distinction was not

identified in the technology acceptance literature. A possible explanation is that most of

the technology acceptance studies were conducted in the USA, where it is culturally

acceptable to say that you don’t like or won’t use something. In East Africa, where

there is a strong culture of wanting to ‘save face’, it is not culturally acceptable to say

that you don’t like or won’t use something. Whether the distinction between actual and

stated intention warrants explicit modelling, as suggested in Error: Reference source

not found, or should simply be acknowledged in supporting text, is a matter of personal

preference.

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Actual behavioral intention

Figure 7.8: Possible distinction between stated intention and actual intention.


Use behaviorStated

behavioral intention


7.6.2. Moderating variablesThe effect of Gender, Age and Experience on the above determinants did not crop up in

interviews. Although it is entirely possible these do have a moderating effect,

conjecture about what those effects would be is unreliable because the numbers of

people who took part in the empirical fieldwork were small. A fourth variable,

Voluntariness, featured in the original UTAUT. It was removed from UTAUT2

because the latter was about consumer technology; voluntariness was true of

everybody. In this case study on crowdsourcing, voluntariness is similarly true of

everybody and so is probably irrelevant.

Limayem et al. (2007: pp717-720) proposed that Habit could conceivably be a

moderating variable rather than a determinant. They created three research models for

IS Continuance. In the second model, Habit was a direct determinant of continued use.

In the third model Habit was a moderating variable on the link between intention and

continued use (Error: Reference source not found). Limayem et al. said that Habit

reduces the need for the person to access intention. The third model accounted for

slightly more variance than the second, though in both the figures were relatively low.

It is clear from the literature that moderating variables are useful for understanding how

some determinants are more relevant to some people than others. If the proposed model

were to be developed further, the role of moderating variables could be considered. In

the absence of evidence on which variables moderate which determinants, they are

omitted from the proposed model.

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7.7. Chapter summaryIn this chapter, the various key points from the literature, interviews and empirical

fieldwork case studies have been compared with UTAUT2 and changes have been

proposed in order to produce the TASUT model. Most of the changes aim to ensure

that a statement of Behavioral intention results in actual Use behavior, and – ideally –

Sustained use behavior. To achieve this, several new constructs have been adapted

from other models, such as Motivation, Habit and Actual facilitating conditions. These

additional constructs were not hypothesised solely from theoretical models, but were

generated abductively from interviews with experts and a range of empirical case

studies to generate the new model.

Chapter 8 provides guidelines on the practical implications of applying TASUT to

mapping initiatives. The model will be evaluated in Chapter 9.

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8. Applying TASUT to mapping developing countries

8.1. ContextThis chapter provides guidance on how to apply the proposed Theory of Acceptance

and Sustained Use of Technology (TASUT) model (see Chapter 7). It draws together

lessons gleaned from the literature review (chapters 2, 3 and 4, which provide citations

to the sources), and from the interviews and empirical fieldwork (Chapter 6). These

guidelines will be evaluated in Chapter 9.

The target audience for this guidance is any manager of a crowdsourced mapping

initiative. ‘Crowdsourced’ means that geographic information (GI) can be contributed

by non-experts, typically the public but also volunteers and employees of government

and non-government organisations. The usefulness of low technology/no technology

solutions are acknowledged, but where modern technologies are appropriate this

chapter provides guidance on how to apply the TASUT model. Managers of mapping

initiatives may want to be familiar with other guidelines that focus on different aspects:

Field Guide to Humanitarian Mapping (MapAction, 2011b).

http://www.mapaction.org/resources/fieldguide.html

Building Resilient Communities (World Bank, 2009b).

http://siteresources.worldbank.org/INTSF/Resources/Building_Resilient_Comm

unities_Complete.pdf

Professional standards for protection work carried out by humanitarian and

human rights actors in armed conflict and other situations of violence (ICRC,

2013).

http://www.icrc.org/eng/resources/documents/publication/p0999.htm

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8.2. Behavioral intention and initial Use behavior

A mapping initiative might require a lot of investment of time and money to make it

work, though the end result will be worth the effort. Managers should expect

disappointments on the way. Even relatively successful projects can have an attrition

rate of around half their trainees. To turn (stated) intention into actual use – both in the

short and long term – it is necessary to provide appropriate conditions (section 8.7),

address motivation issues (8.8), and encourage habitual use of the technologies (8.9).

A mapping initiative needs to organise actual collection and recording of GI in the

longer term; a ‘hit and run’ capacity-building exercise is unlikely to produce any useful

results. Mapping initiatives that are merely training led are seldom effective in

encouraging people to contribute (6.1.3; 6.1.6). It can be relatively easy to get people to

attend a training course, and trainees may sound enthusiastic about the technologies.

However, experience from past mapping initiatives suggests that the kind of obstacles

to a project may experience include:

Education systems are not practically based, so trainees believe they can sit and

listen, but they may be reluctant to develop practical skills.

Some government staff attend training courses mainly because they receive an

allowance, even if they have no intention of using the skills.

Turning enthusiastic intentions into actual worthwhile activity is difficult.

Trainees soon move on to new jobs, especially if their training/acquired skills

give their careers a boost, so may not remain in their current posts for long.

(6.1.3)

It is relatively easy to get people to say they will use mapping technologies (Behavioral

intention), but more difficult to get them actually to use it (Use behavior). In many

developing countries, where there is a strong culture of wanting to ‘save face’, it is not

culturally acceptable to say that one does not like or will not use something. (5.3.2,

6.4.1) Adapting some ideas from a study by Fishbein and Ajzen (2009: pp56-60),

points to consider include:

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Asking about intentions on more than one occasion should help determine who

really intends to take part in the mapping initiative; a one-time response may

not be a good indicator of future behaviour.

Getting trainees to produce a plan of when they will undertake a mapping

survey could improve the likelihood that they will actually do it.

If someone has to borrow a GPS device, travel some distance to the survey area,

and go through several steps to record features onto the system, they are likely

to become disillusioned.

Even crowdsourced systems that are supposed to be easy to use can pose

obstacles to qualified, experienced users. If there is no one to turn to for help,

contributors can become discouraged.

GPS devices and aerial images are relatively expensive for members of the

public.

It is easy to postpone mapping for disaster preparedness. In a society where

simply making ends meet is difficult, investing time and effort to reduce risks

that may – or may not – happen in the future receive lower priority. (4.4.10;

4.4.11)

If people record GI as part of their regular, long-term activities – e.g. recording features

for their work responsibilities – and if the GI is stored on open-access sources, then the

GI would also be available in the event of a disaster. For example, many organisations

could use GI for gap analysis: identifying areas of under provision. The need to record

where aid monies have been spent is often a requirement of grant schemes. Recording

census data geographically might be useful to governments. A variety of pre-disaster

uses for GI are discussed in section 8.4. (6.2.2)

Because motivating people to map their localities can be difficult, an alternative is to

find groups that are already mapping the area, and then to show them the benefits of

using modern systems, especially systems with Creative Commons or Open Data

Commons licences that freely allow a wide variety of uses. (6.1.6; 6.2.8)

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8.3. Sustained use behaviorSustained use of a new technology will likely require contributors to go through five

stages of decision making (Rogers, 2003): knowledge, persuasion, decision,

implementation, confirmation. (4.3.1) To assist in that, a project needs people who can

gain the trust of contributors and inspire them. Some suggestions for improving

sustained usage (Limayem et al., 2007: p732) include:

getting users quickly into the habit of using a new technology;

fostering satisfactory experiences, avoiding discouragement as a result of

problems;

encouraging users to use a technology in as many situations as possible and

useful, so avoiding niche usage. (4.6.3)

Where an ongoing mapping initiative needs to be sustained, relationships between the

coordinating organisation and the contributors are usually “strongest when they are

mutually beneficial and characterized by ‘win-win’ outcomes” (Heath and Coombs,

2006: p5). Those in charge of crowdsourcing initiatives should perhaps think of

themselves more as curators than managers (Brabham, 2013). A local NGO acting as

intermediary is useful for sustaining activity in the long term. It is also worth

considering an initiative to “train the trainers” (Twigg, 2004). This would be a longer-

term initiative that should result in a more sustained capacity. It is also likely that the

participants will have a deeper understanding of the topic and a greater grasp of the

skills since they know they will have to pass these on to others after they have been

trained. Trainees often use their new GIS skills to gain promotion; although this is

laudable, unfortunately this means that their mapping skills leave with them and there

will be fewer left behind able to record GI (6.2.9).

8.3.1. Ethical considerationsA range of ethical issues need to be considered, such as discrimination, respect, and

avoiding harmful effects. The ICRC’s professional standards and guidelines (ICRC,

2013) provide advice and guidance:

http://www.icrc.org/eng/resources/documents/publication/p0999.htm (2.4.2)

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Two major issues are participation and privacy. Clearly it is important to encourage

contributors to participate in a mapping initiative. However, Chambers (2006: pp6-7)

gives some examples of potential problematic situations that organisers need to be

aware of and avoid, including:


undone);

exposing contributors to danger, for example:

o urban dwellers who were analysing violence had to be stopped for their

own safety when local thugs began to take an interest;

o women who took part in participatory activities were abused and beaten

by husbands after the outsider had left;

raising expectations even though only outsiders benefit; and

the ethical concern of taking knowledge from local contributors and using it to

benefit outsiders, especially profit-making organisations. (2.4.2)

The twin concerns about privacy are the privacy of contributors and the privacy of data

subjects. Contributors should be careful not to allow their personal information to

become publicly available in social media. Privacy controls are often set with little

protection as default, and activating the controls can involve navigating many levels of

complex menus. In some crowdsourced mapping systems, the user ID is linked to each

person’s social network profile. An example is Google Map Maker, which requires a

Google user ID; people normally use the same user ID for Map Maker, Google+,

Hangouts, YouTube and so on. Someone who follows a Map Maker contributor might

be able to infer a lot of information from the related social media. (2.4.2)

Most features mapped are impersonal, but some features can give away information

about individuals. When crisis mapping is related to armed conflict or social disorder,

some GI may indicate to which side a person or family shows allegiance and

potentially lead to subsequent violent retribution. (2.4.2)

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8.4. Performance expectancyIt is important to define clearly in advance the parameters of the perceived problem and

solution (Brabham, 2013). The problem under consideration needs to be well framed

and contributors will need clear parameters for their contributions. It would be a wasted

opportunity if contributors were to submit GI that was of no relevance and yet fail to

submit disaster-relevant features. (2.4.1)

8.4.1. Non-disaster uses for GIIt is unlikely that a GIS will be successful if used solely for one purpose (Mitchell,

2006). There will be economies of scale if the cost of systems, data, training,

maintenance and so on can be shared for various purposes such as municipal planning,

civil engineering, grant applications, disaster preparedness and so on. Finding multiple

uses for GI will also help with motivation and sustainability. Among the benefits one

might hope to gain from collecting, collating and making GI freely available are:

identifying local needs;

providing information for gap analysis;

enabling NGOs to make informed decisions;

identifying natural resources such as water sources;

providing information for grants, finance, audit and administration. (6.1.2;

6.1.6; 6.2.1; 6.2.2)

Despite the magnitude of a new mapping initiative, nevertheless what may appear to be

minor details can be important in improving motivation and engagement, for example,

recording business opening hours (Shkabatur, 2012: pp12-13). (6.4.6)

8.4.2. Pre-disaster uses for GIThe main objectives of mapping for disaster preparedness are to provide GI for risk

management and to provide a base map for disaster response. GI for risk management

includes assets, hazards, vulnerabilities, potential impacts and potential

countermeasures. These will vary dependent on local circumstances, but typically will

include (Cova, 1999; Hacklay et al., 2014; Teeuw et al., 2012; Jagger, 2007;

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Karatunga, 2005; Montoya, 2003; Shekhar et al., 2012; Twigg, 2004; World Bank,

2009b; author’s primary research):

Assets:

o administrative boundaries, such as counties and districts;

o census data within each boundary to calculate the population of areas

affected by a disaster;

o demographic information, such as poverty levels;

o land marks and settlements for navigation and context;

o transportation network for emergency access and evacuation;

o bridges and fords in case any become damaged or impassable.

Hazards:

o land use, vegetation etc. to predict fires, famine etc.;

o potential sources of flooding.

Vulnerabilities:

o such as extent of flood plains, historical seismic activity, unstable

hillsides, chemical factories, disease outbreaks, extent of previous

disasters, and the population/demographics of potentially vulnerable

areas.

Potential impacts:

o topology or hypsography:

to identify the physical constraints of the disaster area; and

to assist with modelling potential floods, lahars, landslides.

Potential countermeasures:

o emergency services and local authority buildings so relief workers know

where to go for help or to attend meetings;

o health centres and hospitals for casualties;

o other disaster-relevant resources, such as search-and-rescue equipment,

stockpiles of relief goods, information systems and disaster response

plans;

o schools, sports fields and stadia as possible locations for response

organisations, field hospitals, community shelters, feeding stations,

resource storage areas and safe zones for internally displaced persons;

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o water, sanitation and hygiene (WASH) resources for disease


o natural resources such as springs and rivers;

energy and communications infrastructure. (2.2.6; 6.1.2; 6.2.1)

Sometimes GI for one area is needed for disaster management in a neighbouring area,

for example, water courses upstream of a flood plain. (6.1.2)

The Participatory Vulnerability Analysis approach from ActionAid International (2005:

p11) analyses the causes of vulnerability systematically using four steps:

1. Evaluating hazards to determine the level of exposure to risk, causes and

effects.

2. Examining unsafe conditions (factors that make people susceptible to risk at a

specific point in time).

3. Tracking systems and factors (dynamic pressures) that determine

vulnerability, resilience and root causes.

4. Analysing capacities and their impact on reducing vulnerability. (2.2.4)

It may be quicker, cheaper, more effective and more empowering to take the advice of

locals about hazards and vulnerabilities than to create and rely on a geospatial or

mathematical model of a target area. Local communities will know which areas are

prone to flooding, landslide and so on. However, locals may not take into account the

effect of recent changes in the environment or new developments. It is difficult to

predict infrequent-but-cataclysmic events that are outside their experience. Team

members should take care not to see themselves as experts who decide what the

hazards and vulnerabilities are without any dialogue with local inhabitants. By failing

to identify community needs, priorities and capacities, team members could impose or

promote solutions that are inappropriate (Twigg, 2004; Taleb, 2010) (2.2.4).

During disaster preparedness mapping initiatives, it is worth setting out major

objectives, such as the types of features to be recorded and which areas to prioritise.

There may be a tendency for contributors to focus their GI collecting effort on the

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minutiae of popular areas, such as the location of every tree in an urban area, while

entire villages in vulnerable areas go unmapped (Hacklay et al., 2014). (2.2.4)

In addition to the location of each feature, information about each feature (tags) also

can be important. Taking the example of a hospital, disaster response organisations

would need to know what type of hospital it is, its size, the number of emergency beds,

the number of intensive care beds, and even the number of doctors and nurses.

Response organisations use this information to decide where to set up field hospitals,

where to send casualties, where to send medical supplies and so on. (2.2.2)

8.4.3. Post-disaster uses for GIIt is important to understand the GI that will be needed post disaster in order to know

what GI needs to be collected in advance. After a disaster, response organisations’ GI

needs will depend – to an extent – on the nature and severity of the disaster. Typically

GI is needed for these purposes:

Identifying the extent and severity of the disaster:

o estimates of deaths, injuries and displaced persons (partly based on the

administrative boundaries and census data mentioned above);

o the spread of hazards such as spills, infestation, infection or epidemic;

o damage to infrastructure, such as roads, bridges, water supplies;

o damage to disaster-relevant resources, such as hospitals.

Providing reference and navigation maps during the assessment phase of the

emergency. Place names, street names and points of interest are important here.

Creating a common operational picture among response organisations as relief

work progresses, often referred to as Who-What-Where(-When). This requires

them to update the GI as the situation develops.

Targeting relief assistance to avoid gaps and overlaps in provision, and enabling

NGOs to make informed decisions on where to work. This can include:

o areas that have been searched in order to minimise searching the same

areas twice;

o records of where relief aid has been distributed.

Public relations information:

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o to gain support from local authorities and politicians;

o to raise funds from the public and overseas governments. (6.1.1; 6.2.1)

Population density and demographic information are particularly important. During

disaster response, knowing the number of survivors in the disaster zone is possibly

more important than knowing the number of deaths. The survivors will need to be

evacuated, treated medically, fed and housed (2.2.2). Knowing the relative economic

vulnerability, age profile, and livelihoods can help when prioritising resources.

Redmond (2005: p1259) says:

Poverty is the single most important factor in determining vulnerability: poor

countries have weak infrastructure, and poor people cannot afford to move to

safer places. Whatever the disaster, the main threat to health often comes from

the mass movement of people away from the scene and into inadequate

temporary facilities. (2.3.1)

It is important that relevant GI can be discovered quickly by disaster response

organisations, and for them to be able to access/download it without unnecessary

delays. In general, bandwidth in remote disaster zones is so limited that deployment

teams are unlikely to receive much additional formal GI once they reach the disaster

area. If a source of GI is licensed, the time required to agree usage may mean that it is

not available in time to give to a deployment team before they reach the departure

airport. Sources that store GI using standard data formats and make that GI available

under an open or Creative Commons licence – such as OSM – are useful because the

GI can be used without any significant restrictions. (2.5.2; 6.1.1; 6.2.2)

8.4.4. Data storageImportant decisions need to be taken about where to store GI. Base map features that

are of general interest to anyone, and also of use to disaster response organisations, are

best stored in a publicly accessible system such as OSM or Google Map Maker. They

can be enhanced and updated by contributors over time and are held on secure servers.

Private GI and GI that is vital to an organisation might be better stored in a secure,

private system. Such GI might include personal, commercial or financial information

265

etc…

Base map(Public system)

Private highway data(Private GIS)

Highway authority Development NGO Disaster NGO

Private financial data(Private GIS)

Post-disaster data(Private GIS)


Figure 8.1: Possible division of storage.


not to be disclosed to the public, or proposals for roads and buildings that do not yet

exist, or data of strategic importance to the originator that must not be susceptible to

deletion by the public. GI from the private system can be merged with the base map

from the public system when producing maps, as suggested in Error: Reference source

not found. (2.5.2; 3.2.6; 3.5.1)

8.4.5. Data qualityGood data quality can encourage other contributors. Conversely, Loukis et al. (2010)

found that if features added to a digital map are limited in quantity and quality, citizens

are dissuaded from contributing more. Among the overall conclusions, they said, “The

use of maps to pinpoint relevant data seems to be a well-accepted practice by citizen

users in order to broaden their understanding of complex local problems and spatial

suggestions” (ibid: p209). (4.4.3)

Training will help to reduce data errors. Inexperienced contributors often make basic

mistakes at first though. Dialogue and constructive feedback will help improve data

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quality. If contributors can see how the data is going to be used in practice, it should

help them more easily understand how to record it properly, and so improve the quality

of GI. Some thought needs to be given to how contributed GI can be validated and

verified. For some features, a visual comparison with GPS track-logs and aerial images

may be sufficient; if there are time and resources, all new GI should be ground truthed

by someone other than the original contributor. Special attention should be paid to

features identified solely from aerial images: it is easy to mistake one building for

another from above, the condition of a road may not be apparent from an aerial image,

or conditions might have changed since the aerial image was taken. (6.1.3; 6.2.2; 6.2.9;

6.4.1)

8.4.6. Other benefitsThe benefits from one initiative often continue in other situations. GI generated can be

used for many purposes. Likewise, skills and experience gained in one project may be

useful on subsequent projects. (6.2.1)

8.5. CostsIn this context ‘cost’ is used quite broadly, encompassing finance and personal effort.

8.5.1. FinancialFinancial costs needn’t be high by the Western standards, though even modest costs

can pose an obstacle for local organisations. The need to pay a stipend to local team

members is discussed in subsequent sections; again, the sums may be small by Western

standards.

If high-resolution aerial images are already available in OSM or Google Map Maker, it

is possible to map an area using just local knowledge and a PC with Internet

connection. To locate features more accurately it is useful to issue teams with GPS

devices. For humanitarian work, consumer-grade GPSs costing perhaps £100 should be

adequate, though it is important to ensure the device is able to record track-logs. GPS

data loggers are usually even cheaper. Team members who own a smartphone can use

an appropriate track-log app such as OSMTracker or Traveler. (6.2.6)

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A PC with a reasonable specification and a link to the Internet is needed to transfer GI

from the GPS to a mapping system. OSM and Google Map Maker editors are free to

use. If the project requires GIS, proprietary GISs can be expensive but free open-source

GISs exist too. A base map of a region from a commercial source can also be

expensive. OSM and Google Map Maker are free to use, but the terms of their licences

are important. (3.4.2)

A mapping initiative needs to consider secure funding in the longer term. How will

team members be remunerated in the long run? How will equipment be maintained and

replaced in subsequent years? Who will pay for software licence renewals? In some

projects in the past, if equipment was not maintained this was mainly because

administrators misappropriated funds; protecting funding from misappropriation is

important. (2.3.2; 6.1.4; 6.2.9)

8.5.2. Personal effortIt is important not to underestimate how difficult to use some mapping technologies

are. Transferring features from a GPS to a mapping system can be very complex and

time consuming at first. If hardware has to be borrowed, or data has to be converted

from one format to another, each step adds yet one more obstacle to overcome. On the

other hand, if users use mapping technologies on a frequent basis, they become more

accustomed to them. Ideally, if there are organisations already mapping in the target

area, it can be effective to encourage them to map disaster-related features too; ideally

they would store their GI in a secure, readily-discoverable system under a Creative

Commons or Open Data Commons licence such as OSM. (6.4.1; 6.4.4)

Validating and verifying GI – especially where that involves ground truthing – is an

additional drain on staff resources (see 8.4.5).

8.6. Social influenceAn occasional ‘mapping party or other big initiative can help to motivate communities.

For example, during the run up to an election, electoral boundaries and polling stations

could be mapped. A mapping party can also be a good motivator, especially as

contributors can see the effect of a large number of updates in a short space of time.

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There is little tradition of volunteering for free in developing countries, so

crowdsourcing initiatives often have to remunerate contributors, even if the stipends are

small. Finding the right level of remuneration is crucial. If one organisation pays local

workers over-generously, it can be more difficult for other response organisations to

recruit people. (6.1.4; 6.2.2; 6.2.9; 6.4.3)

It is useful to gain the cooperation of a local NGO to help engage the local community.

When working with several local organisations, each will have different agendas,

cultures and ways of working. There may even be power struggles between these

organisations. Eria (2012: p270-271) identified a number of potential problems when

working in developing countries: “(1) a culture of corruption and impunity, (2) old-

fashioned attitudes towards power relations within the organizational hierarchy, (3)

poor attitudes towards work and service delivery, (4) heavy reliance on donor funding,

and (5) poor attitudes towards sharing of public data.” (2.3.2; 6.2.2)

Predicting what information is politically acceptable is difficult. For example,

government organisations responsible for environmental management might not

welcome an initiative that exposes shortcomings and vulnerabilities. Gap analysis

might reveal severe under provision by central government. Recording the poor state of

a road might highlight that funding for road improvements had been misappropriated.

(6.2.3)

8.7. Perceived and actual facilitating conditions

Although positive perceptions about facilitating conditions encourage people to

consider using mapping technologies, any good intentions will soon disappear if actual

conditions do not support them.

8.7.1. Technologies In general, cheap but reliable hardware can be more useful than the latest high-

specification hardware. Unreliable utility supplies can be a major problem in

developing countries. Laptop computers cope better during a power cut than desktop

computers. Map editors that can continue to work even through Internet outages are 269


more reliable than those that require constant connectivity; offline editors such as

JOSM can upload the edited map after communications have been restored. Consider

creating a kit of equipment (GPS, laptop PC, software) configured to work together. It

is helpful to all users if equipment, software and data conform to widely-used

standards, because proprietary systems – especially proprietary data standards – hinder

interoperability. (3.4.1; 3.4.2; 6.2.4)

Some consumer-grade GPSs can record lines (track-logs) as they move about, and

points of interest (POIs or favourites) when the user presses an appropriate button.

Ideally the track-logs and POIs should be annotated with text to indicate what they are

(road, path, health centre, hazard). Accuracy is typically within a few metres, though

accuracy is lower initially after being turned on, when near tall buildings, under tree

canopy, and in steep-sided valleys. Accuracy can be improved by recording a location

several times on different days and averaging the reported locations. (3.2.2)

Most GPS data loggers are even cheaper than consumer GPSs as they have no screen.

In addition to a description of a POI, the contributor must record on paper the date and

time a POI was visited, and later match that to the date and time the POI button was

pressed. Some contributors prefer to photograph features and match the date/time

stamp on the photograph with the track-log to identify the location. The GPS will

record times in Coordinated Universal Time (UTC) not local time. Accuracy is similar

to that of a consumer-grade GPS, i.e. typically within a few metres. (3.3.2; 6.2.6)

If a contributor owns an appropriate smartphone, it is possible to use its built-in GPS

and an app to record track-logs and POIs. In other respects, operation is similar to a

consumer-grade GPS, though accuracy is often not quite as good. Apps such as

OSMTracker and Traveler can combine track-logs with photographs of features taken

on the smartphone. A geotagged photograph can record the name of a clinic, school and

so on. (3.3.2; 6.2.6)

Financial support for maintenance of equipment is important. Equipment in developing

countries is seldom maintained in the way one is accustomed to in industrialised

countries (Gowa, 2009). One factor that can result in lack of maintenance is that funds

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may be misappropriated, so funding for mapping initiatives should be arranged with

this in mind. (3.4)

In addition to hardware and software, users also need basic geographic data about the

target area. Developing a map from scratch is time consuming. OSM and Google Map

Maker have at least basic GI for most of the world. Licences for GI usually restrict

what you do with the information. Aerial imagery is useful, but the resolution in OSM

and Google Map Maker can vary a great deal from one area to another (especially in

OSM). As the cost of UAVs (drones) comes down and features improve, it may be

feasible for a project to produce its own low-altitude aerial images. Such images need

to be orthographically rectified (to remove distortions) and georeferenced (to locate

geographically). (3.3.6; 6.2.4)

The repository for your map/GI needs to be secure and resilient in the event of a

disaster. Repositories held solely in the disaster area may not survive a catastrophic

event. That repository needs to be ‘discoverable’ in the event of a disaster. OSM,

Google Map Maker and others use multiple servers in secure locations, and they have

become well known to disaster response organisations (Limbu, 2012). (2.5.2)

8.7.2. Skills, training, supportAlthough skills training (capacity building) is important to a degree, it is not an end in

itself. See the caution above about how training-led projects may not be effective in

encouraging people to contribute to a mapping exercise. Opinions differ about the level

of skills typically found in developing countries. Regardless of skills levels, relatively

simple consumer-grade technologies will be more accessible, understandable and

maintainable than expensive professional-grade technologies. (3.3.2; 6.1.3)

It is quite normal to recruit volunteers with specific skills and backgrounds (Hacklay et

al., 2014). Organisations that actually need and will use GI for their work are possible

sources of people to train in the use of mapping technologies. Organisations in

developing countries have a relatively high turnover of staff. Several mapping

initiatives have recruited university students, who join for altruistic reasons but can also

be incentivised by scholarships or competitions with prizes. Because students tend to

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move between home, university and graduate work, they do not provide a stable pool of

skills in a given location. Some successful initiatives have included people from other

backgrounds such as farmers and doctors (Narvaez, 2012). (2.4.1; 6.1.3; 6.2.9)

Contributors will need operating guidance on how to use equipment, how to survey,

how to record GI and so on. Generic guidelines already exist, such as the MapAction

Field Guide to Humanitarian Mapping:

http://www.mapaction.org/resources/fieldguide.html

Obtaining local government support can be helpful. Consider whether government

officers can help with the project or provide resources. They can be pivotal in

encouraging support from village committees, citizens and local NGOs. The timing of a

mapping initiative can be important. Support for disaster preparedness is likely to be

higher immediately after a disaster, while the effects are fresh in people’s minds and

they are concerned to avoid a recurrence. (2.2.2)

8.7.3. LegalObtaining appropriate licences, for GI in particular, is important. Licence fees are often

high. Licences usually restrict users from merging GI from different sources. In

particular, proprietary GI cannot be copied into crowdsourced system such as OSM

unless the licence specifically permits it. (6.2.4)

Check if it is necessary to obtain a permit before surveying an area. Government, law

enforcement and military organisations have particular objections to mapping features

that they consider to be sensitive.

8.8. MotivationIdentifying what motivates contributors to take part in an initiative is important. It is

particularly important in long term initiatives to keep contributors motivated after the

initial enthusiasm has waned. In industrialised countries, commonly mentioned

motivations for crowdsourcing are (Coleman et al., 2009: pp342-343):

altruism;

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personal or professional interest;

intellectual stimulation;

protection or enhancement of a personal investment;

social reward;

enhanced personal reputation;

an outlet for creative and independent self-expression;

pride of place. (4.5.4)

Motivators in developing countries might be rather different. Maslow (1943) indicated

that there was little point appealing to the higher-level needs (such as intellectual

stimulation and reputation) if people’s lower-level needs were unmet. Applied to

crowdsourced mapping, while contributors from wealthier societies might produce GI

out of altruism or pride, contributors from poorer societies are likely to be concerned

about physiological needs for food, clothing and housing, and safety. Put simply, many

successful initiatives pay local contributors a stipend for their participation. (4.5.1;

6.1.6; 6.2.9) Nevertheless, it is worth also considering a broad range of motivators,

including those that are intrinsic and others that are extrinsic (Kaufmann et al., 2011:

p2).

Motivation from the task itself (intrinsic):

o enjoyment derived from doing the work;

o a sense that the work is helping the community.

Motivation derived from results of doing the task (extrinsic):

o immediate payoffs, such as remuneration;

o delayed payoffs, such as disasters averted or mitigated;

o social motivation, including respect from peers, peer pressure. (4.5.2;

4.5.5)

Fear-based appeals need to be used with care. Concern about a potential disaster

motivates communities to contribute to disaster preparedness activities, but only if

those people feel that their actions will be effective (Witte, 1992). It can help if

contributors understand how their GI will be used and how their contributions helped to

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improve the aggregate performance of the team. (Hacklay et al., 2014: p29;

Ramchurn et al., 2013: p4) (4.2.3)

Crano (1983) identified that contributors may find disaster preparedness mapping

projects demotivating if they have broad geographic scales and long timescales. He

suggests that more immediate motivators, local and short timescale, must be identified.

In this context, it is important to demonstrate the value and usefulness of volunteered

GI to those who live in the localities. Printed copies of maps are useful for this,

especially where Internet access is slow or unavailable. It will be useful to suggest to

local organisations a range of alternative possible uses for GI. (4.2.2; 6.1.5; 6.1.6)

Technical difficulties can be overcome if there is a drive and a motivation to succeed;

conversely projects that have relatively few technical difficulties can still fail if team

members don’t have that drive or motivation. (3.1; 6.2.9) A project’s organisers need to

consider other issues raised by the participation of local communities, such as

(Chambers, 2006: pp6-7):


undone);

exposing contributors to danger (e.g. during insurrection);

raising expectations even though only outsiders benefit. (2.4.2)

It is important to consider the personal safety of contributors. Where their personal

safety is at risk, the ICRC (2013) provides professional standards and guidelines at:

http://www.icrc.org/eng/resources/documents/publication/p0999.htm (2.4.2)

8.9. HabitMapping technologies are more likely to be used in the long term if people are in the

habit of using them. Where contributors are members of the general public, they are

probably already in the habit of using OSM or Google Map Maker. Where an initiative

trains a team of local contributors, it will be important to encourage them to get into the

habit of using the technologies frequently. Limayem et al. (2007: p714-715)

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determined that habit requires three things: frequent repetition; satisfaction with the

outcomes of the behaviour; and relatively stable contexts. (4.6.2; 4.6.3; 6.2.7)

Frequent useIf team members use the technologies frequently the steps become easier to remember.

There is a case for a smaller team where every member uses mapping technologies

every day, than a bigger team where each member uses the technologies only

occasionally. Transferring data from GPS to PC and mapping features accurately and

consistently can be difficult for inexperienced users; people who use a system

infrequently tend to make mistakes (Limayem et al., 2007: p729). (2.3.2; 6.2.4; 6.2.7)

SatisfactionHabitual use is determined by the users’ satisfaction with the technology. User

satisfaction is influenced, in turn, by whether technology use to date confirms their

original expectations. If the original expectations were unreasonably high,

disappointment may soon follow. (4.6.2)

Stable contextTeam members may change job quite frequently, especially government employees.

Furthermore, people trained for a project often use their new skills to find new jobs.

Sustained and frequent use of mapping technologies is impossible where there is a high

turnover of staff. (2.3.2; 6.2.7)

Although calling on students to map a locality has been successful in several projects, it

is worth considering if they provide a stable skills base for longer-term mapping

initiatives. Students tend to move frequently between home, university and graduate

employment. Also university work pressures vary greatly during an academic year,

leaving them with little time for altruistic activities. (6.1.3; 6.2.1; 6.2.9; 6.4.3)

8.10. Chapter summaryThis chapter has looked in detail at each structure in TASUT, and provided guidance on

how it could be applied to a mapping initiative in developing countries. The emphasis

has been on how to encourage communities to use mapping technologies both in the

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short and long term. The next chapter documents how the draft TASUT model and

these guidelines were evaluated.

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9. Evaluation

9.1. MethodBetween October and December 2014, twenty experts were identified to provide

feedback about a draft of the TASUT model and a draft of the guidelines. Some proved

uncontactable, but fifteen of them were asked to provide feedback. The evaluators

worked in various NGOs and universities around the world. Most evaluators had been

interviewees as documented in Chapter 6. Each was provided with a copy of the

TASUT model and definitions (section 7.5), the guidelines “Applying TASUT to

mapping developing countries” (Chapter 8), and a form on which to record their

evaluations and feedback. After a few questions about the evaluator, the form asked for

opinions about the model and guidelines. Responses were received from the five

evaluators listed in Table 9.20; their completed forms are in Appendix B.

Code Organisation Role Date Evaluation type

{NGO8x}

GroundTruth Initiative LLC

Director 8 Dec 2014 Form

“Developing the Map Kibera project, the Map Kibera Trust and several other related projects worldwide using OpenStreetMap.”

{NGO12x}


Executive Director 15 Dec 2014

Form

“Have worked to provide geographic data to disaster managers for the past 13 years. Have been working with crowdsourcing projects for the past 5 years.”

{NGO17x}

PONT PONT Advisor 30 Dec 2014

Int & form

“I have been involved in the PONT project in the Mbale Region of Uganda for 10 years. During that time I have been involved in a wide range of community development activities such as primary healthcare in villages. This has included being trained as a Ugandan primary healthcare worker and encouraging local villagers to change behaviours to improve levels of health within communities. I was involved in helping to deal with the after effects of a landslide in Mbale in 2010 which killed a number of people and displaced a large number of families. I have direct experience of the effects of landslides and rockslides in rural areas of Uganda. I have direct experience in projects to plant trees in Mbale working with local farmers and communities. I spent 6 months as a project manager working on the United Nations TACC project that looked at the physical effects of climate change on rural

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Code Organisation Role Date Evaluation type

communities in Eastern Uganda which included potential environmental disasters such as landslides resulting from climate changes.”

{UNI3x} ITC, University of Twente

Associate Professor (Remote sensing for disaster risk management)

5 Dec 2014 Form

“About 20 years work and research experience in geoinformatics for [Disaster Risk Management], PhD in volcano remote sensing; past ca. 12 years focus on use of geoinformatics for post-disaster response, in particular damage mapping. Includes use of UAV imagery, geometric data processing. Work in VGI for past 2 or 3 years; working group leader of COST Action Mapping and the Citizen Sensor (focus on understanding and influencing volunteers); Work at ITC has largely focused on [developing countries], and I have previously worked in Costa Rica, Nicaragua and the Philippines.”

{UNI4x} Northumbria University

Senior Researcher 17 Nov 2014

Form

“I have used GIS for the past 25 years and seen the approaches for participation in mapping from communities evolve. I have been involved in a number of participatory mapping projects with communities – the most pertinent being one on flooding issues and options for changes in management in a UK watershed. The p-mapping collected community identified options for management changes to ameliorate the flood risk. I have also undertaken participatory scenario mapping to identify community responses to proposed plans in terms of their likely behavioural response e.g. how would they act under the changed conditions?”

Table 9.20: Summary of experience of the evaluators

9.2. Responses about TASUT model and supporting text

9.2.1. TASUT diagram{NGO8x}, {NGO17x} and {UNI4x} were all content with the TASUT diagram.

{NGO8x} added that the relationships seemed complete. {NGO17x} thought the model

helpful in bringing together diverse factors that need to be considered. {UNI4x} added

that the diagram is useful and clear. {NGO12x} found the diagram “a bit difficult to

understand” but added that “the complexity is okay”. Only {UNI3x} thought the

diagram wasn’t immediately accessible. He suggested that grouping some boxes

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Figure 9.1: Extract showing suggested additional effects of Social influence in bold.


Social influence

Use behavior


together might help, and also that readers needed some help in knowing where to start

reading it.

{UNI4x} wondered if reinforcement or encouragement for use could be included in the

diagram, giving the example of getting ‘likes’ or points to encourage sustained habitual

use. One could argue that these concepts are already included under Motivation, such as

Maslow’s (1943) ‘esteem’ and Kaufmann et al.’s (2011) ‘social motivation’. {UNI4x}

thought that Social influence should feed into both initial Use behavior and Sustained

use behavior as indicated in Error: Reference source not found.

However, it does raise the question about whether Social influence needs to be shown

separately at all; it could be part of Motivation. Kaufmann et al. (2011) listed

“community-based motivation” as an intrinsic motivator, and “social motivation” as an

extrinsic motivator. Merging the Social influence construct with Motivation – rather

than adding those dependencies – would be more accurate and would help simplify the

TASUT diagram a little.

{UNI3x} was of the opinion that Motivation should be linked with Satisfaction and

Habit. He also wondered if there might be reciprocal determinants, for example, if Use

behaviour produces a positive outcome, it improves Motivation. In a sense, a positive

outcome is already shown in the model as Satisfaction, but there is a case for showing

that Satisfaction can influence Motivation as indicated in Error: Reference source not

found.

279

Performance expectancy/ Benefits


Figure 9.2: Extract showing Motivation affected by Satisfaction, and Social influence combined with Motivation.

Motivation

Satisfaction



{NGO17x} suggested that the Performance expectancy construct should be renamed

Benefits. This would indicate more clearly that the construct was about the beneficial

outcomes of using the technology, not expectations about the performance of

participants in a mapping exercise. The name “Benefits” would also go well with the

Costs construct.

{UNI3x}, {NGO12x} and {NGO17x} commented that TASUT doesn’t show the

feedback from the value returned from using the technology increasing Satisfaction and

so reinforcing Motivation. Although the model implies this through Satisfaction, it

could be shown overtly using a feedback loop.

Based on her experience of sustained mapping in various projects, {NGO8x} thought

that Habit was not a major factor in sustainability, and went on to say, “In general, long

term mapping will happen when there are reasons to keep mapping – all the same

conditions that led to initial mapping, only ideally it will happen faster and better.”

{NGO12x} suggested that Performance expectancy was related to Use behavior and

Sustained use behavior, explaining, “often during the introduction to technology people

still do not completely understand its capabilities and it requires sustained use.”

Otherwise she thought it was complete.

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9.2.2. Definitions{UNI4x}, {NGO12x} and {NGO17x} thought that the definition of each construct was

clear and helpful. {UNI3x} thought that the definition of Motivation should clarify

whether it was the motivation of the contributor or the organiser. {UNI3x} also pointed

out that the definition of Performance expectancy (or Benefits) could be phrased in a

way more relevant to an altruist, that is, less in terms of what the contributor would get

out and more about achieving the desired outcome.

9.3. Responses about the guidelines for applying TASUT

9.3.1. Understandability and sequence of material{NGO8x}, {NGO12x} and {NGO17x} thought that the guidelines were clear and

understandable, and both were content with the length of the guidelines. {UNI4x} said,

“I think the guidelines are comprehensive and well written” but perhaps too long for

managers. {UNI3x} also thought the guidelines were too wordy, and he would have

preferred less use of the word ‘may’.

There was some divergence of opinion about the writing style and use of references.

{NGO8x} found the references distracting. {UNI4x} pointed out that if the guide were

to be issued to managers not academics, then the references etc. could be removed and

a ‘further reading’ section added for anyone interested enough to pursue them.

Conversely, {UNI3x} wanted more references and a more academic writing style. A

numbering system for citations (see Wright-Jones, 2014, for an example) might have

been less intrusive and distracting than the Harvard system.

{UNI4x} thought the introduction should explain how they can benefit from using

them; this would “sell it better”. He thought the guidelines were rather long and that

managers would invest the time to read the guidelines only if the benefits were clearer;

he suggested opening with a short Executive Summary that sets out the benefits of the

guidelines, and highlights a few key recommendations. He also suggested creating a

flow chart of best practice, though he didn’t clarify what this might contain.

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{NGO12x} added that she thought the introduction should stress the importance of the

use of GI, because too many mapping initiatives focussed solely on data collection.

{UNI3x} and {NGO12x} both thought the sequence of material was correct. {UNI4x}

said, “I think the structure could be re-organised to ensure their use”, but didn’t

elaborate. It is likely that this refers to the need for an Executive Summary.

{NGO17x} highlighted the importance of recruiting the right people as contributors,

and he endorsed the recommendation of building on whatever organisations are already

working in the area, though he cautioned that mapping needs to be done as part of their

everyday activities saying, “tasks that add to employees’ workload will not we

welcomed.”

In relation to motivation, {NGO17x} recounted his experiences of appointing over 600

volunteer healthcare workers in the Mbale region. In contrast to the two case studies

(sections 6.4.5 and 6.4.6), where volunteers received stipends, these volunteers were

unpaid; their main motivators were altruism and enhanced status in the community.

This enhanced status could be reinforced if volunteers felt that they had access to

people in powerful positions, such as politicians and local government officials.

{NGO17x} emphasised that enhanced status would be especially important to women

in a society where women are generally undervalued. For women who are married, care

must to be taken to cultivate husbands’ support though, to ensure he supports rather

than obstructs the work. {NGO17x} also raised another example of Motivation, that of

religion. For some religious people, being a good ‘witness’ (example to others) was a

positive motivator in humanitarian work. For other religious people, disasters may be

viewed fatalistically as inevitable or a divine punishment, an ‘Act of God’ as it were.

{NGO8x} suggested that among the examples of Motivation’s “delayed payoffs” could

be added “skills building”, since most of the volunteers her team had trained wanted to

learn a potentially marketable skill for improved employability.

9.3.2. Usefulness, effectiveness and applicability{UNI3x} didn’t consider the guidelines very practical, but conceded that he thought

that about all frameworks and models. {UNI3x} noted that there is a risk that no one

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would pick up and implement the model, and asked how the guidelines would be

disseminated to managers of mapping initiatives.

{NGO12x} said she would potentially use the guidelines, but noted that her

organisation already had other guidelines. {NGO17x} would definitely recommend the

guidelines to anyone conducting a mapping or capacity-building initiative for disaster

preparedness. He thought that elements of the guidance could be used anywhere

provided they were adapted for other cultures. {NGO8x} similarly considered that the

guidelines were sufficiently generic to be usable around the world. She particularly

liked the opening few paragraphs saying, “This is probably the most useful section.

Especially about how just training won’t do much, and why. I don’t think this has been

said much before and it’s true.”

9.4. Resultant changes to TASUT and guidelines

9.4.1. Final TASUT modelBased on the feedback identified in this chapter, some changes have been made to the

working version of the TASUT model (Error: Reference source not found on page

Error: Reference source not found) to produce the final version of the TASUT model

that follows (Error: Reference source not found).

Four constructs are renamed:

Performance expectancy becomes Benefits;

Behavioral intention becomes Intention to use;

Use behavior becomes Initial use; and

Sustained use behavior becomes Sustained use.

There are several reasons for these changes. First, the new names make clearer the

intention of the model. With the aid of supporting text, the new names should help the

reader to know where to start reading the diagram; this addresses {UNI3x}’s comment

as noted in section 9.2.1. Second, the use of a technology is a less abstract concept than

behaviour. Third, the sequence Intention to use, Initial use and Sustained use is made 283


clearer. Fourth, the spelling of ‘behavior’ in the working version was distracting for

some readers, especially when the supporting text used UK spelling.

The construct Social influence has been removed because another construct,

Motivation, already encompasses it. For example, Kaufmann et al. (2011: p2) list

“community-based motivation” as one type of intrinsic motivator, and “social

motivation” as a type of extrinsic motivation. Maslow (1943) includes

“Love/belonging” and “Esteem” in the Hierarchy of Needs.

{UNI3x} suggested that Satisfaction is a factor that leads to Motivation (see section

9.2.1) and so a new determinant arrow appears in the final model to reflect this. This is

supported by Kaufmann et al. (2011: p2) who define “immediate payoffs” and “delayed

payoffs” as types of extrinsic motivators. In the context of disaster preparedness,

Coleman et al. (2009: pp342-343) list “Protection or enhancement of a personal

investment” as a motivator. In the marketing world, satisfaction is widely considered

key to retaining people’s loyalty in the long term (Bhattacherjee, 2001).

Three other comments documented in section 9.2.1 have not been acted upon. First,

{NGO8x} thought that Habit was not a major factor in sustainability; however, it was

found to be significant in the literature (sections 4.4.9 and 4.6.3), interviews (6.2.7),

and one of the case studies (6.4.4). Thus Habit remains in the final version of the

model, though the definition has been expanded to clarify the breadth of the concept.

Second, {NGO12x} suggested that Performance expectancy (Benefits) was related to

Use behavior and Sustained use behavior; however, this construct concerns the

expectation about a technology that the user has just experienced for the first time.

Users’ initial and sustained use, however, is dependent on actual performance and this

is represented by Satisfaction; this construct is shown as indirectly influencing both

initial and sustained use in the final model. Finally, a few respondents commented that

the diagram ought to be cyclic, that is, show feedback from the benefits of actual use

improving Satisfaction and thus Motivation. All of the technology acceptance models

documented in section 4.4 are ‘acyclic directed’ graphs, that is, they do not support

feedback. Making such a change would represent a major departure from the family of

models and is considered further in section 10.4.5.

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Stable context

Benefits

Intention to use

Figure 9.3: Theory of Acceptance and Sustained Use of Technology: final version.

Sustained use


Motivation

Costs

Habit

Initial use


Frequent use

Satisfaction


Relevant changes are incorporated in the final version of TASUT in Error: Reference

source not found.

Four changes have been made (below) to the definitions of the terms. The entry for

Performance expectancy is renamed Benefits, and the definition is rephrased to

encompass someone acting altruistically. The definition of Social influence is removed

because the construct has been removed. Consequently, the definition of Motivation is

expanded to encompass explicitly social influence and the other factors. In response to

{UNI3x}’s comment noted in 9.2.2, this definition is also rephrased to be clearly about

the contributor to, as distinct from the organiser of, a mapping initiative. The definition

of Habit has been expanded to clarify the breadth of the concept.

DefinitionsIntention to use is the degree to which a person has formulated conscious plans to

perform or not perform some specified future behaviour. (Based on Venkatesh et al.,

2003).

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Initial use is the observable act of a person using a technology immediately after it has

been introduced. (Based on Fishbein and Ajzen, 1975: p13.)

Sustained use is the observable act of a person continuing to use a technology after

outside initiatives and support have ended.

Benefits represent the degree to which using a technology is expected to provide

benefits to themselves or others in performing certain activities. (Based on Venkatesh

et al., 2012.)

Perceived facilitating conditions refer to users’ perceptions of the resources and

support available to perform a behaviour. (Based on Venkatesh et al., 2012.)

Actual facilitating conditions refer to the resources and support actually available to

perform a behaviour. (Based on Venkatesh et al., 2012.)

Costs include all the costs of using a technology, whether monetary or in terms of

personal effort.

Motivation encompasses all the factors that drive people to use a technology including

intrinsic motivators, such enjoyment and a sense of community, and extrinsic

motivators such as remuneration, skills-building and social influence (Kauffman et al.,

2011).

Habit is the extent to which people tend to perform behaviours automatically because

of learning (Limayem et al., 2007: p709). It derives from satisfaction, frequent use and

a stable context.

Satisfaction is the degree to which the user’s positive expectations about a technology

are confirmed. (Based on Limayem et al., 2007: p708.)

Frequent use means the technology is used often enough in a given period to help the

user to remember how to use it.

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Stable context refers to situational cues and relevant goals of the individual that are

similar (or the same) across consecutive situations. (Based on Limayem et al., 2007:

p715.)

9.4.2. Changes to guidelines for applying TASUT model

In sections 9.3.1 and 9.3.2, a few proposals were made about the guidelines. Reducing

the number of occurrences of the word ‘may’ and recasting citations using numbers,

rather than Harvard format, are relatively mechanistic exercises. Since the Social

influence construct is now incorporated into Motivation, the text under the heading

“Social influence” should be incorporated into the section headed “Motivation”. The

examples in section 8.8 of the range of motivators, taken from Kaufmann et al. (2011:

p2), could be expanded to include “enhanced status”, “religion” {NGO17x}, and “skills

building” {NGO8x}. Some section titles would need to be renamed to match the four

renamed constructs in the final version of TASUT.

In response to suggestions from {NGO12x} and {UNI4x}, an executive summary

could usefully be included in the guidelines, as suggested in Box 9.2.

Executive SummaryA successful mapping initiative is one that records relevant and accurate geographic

information (GI), both in the short and long term. A mapping initiative is about far

more than just the initial training of contributors; people must actually go out and

survey relevant GI, and continue to update the GI in the long term to reflect changing

circumstances. Also, that GI must be put to good use. This guide aims to help

organisers of crowdsourced mapping initiatives to get local communities involved in a

sustainable way. This guide is particularly suited to initiatives related to disaster

preparedness in developing countries.

Maps and GI are useful for a variety of purposes, including gap analysis, enabling

NGOs to make informed decisions, identifying natural resources, and providing

information for finance and audit. Specifically in relation to disaster preparedness, there

are two main uses for GI: to support disaster mitigation and prevention activities, and to

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provide information for disaster response organisations should a disaster occur.

This guide explains the Theory of Acceptance and Sustained Use of Technology

(TASUT) model and how to use it. By addressing each component of the model, the

crowdsourced mapping initiative is more likely to be successful in gaining support

from local communities in the short and long term.

Box 9.2: Proposed Executive Summary for the guidelines

9.5. Validation of model by exampleIn order to validate how the TASUT model and guidelines could strengthen a project

proposal, it has been applied retrospectively to the broad-based capacity building

training course case study (section 6.4.1) to demonstrate how it can be used to help

ensure successful outcomes. In this case study, the district government and local NGOs

needed to map the locations of their various climate-related projects, and the author

was asked to train 14 delegates in January 2011 under the auspices of Partnerships

Overseas Networking Trust (PONT) and the United Nations’ Territorial Approach to

Climate Change (TACC) projects (UNDP, 2013). The 14 delegates were trained in two

sessions each lasting two days. The case study highlighted a number of problems; these

are explained below in chronological order of the problems occurring. For each

problem identified, one or two safeguards are proposed and the expected improved

outcomes indicated. Problems, safeguards and expected outcomes are summarised in

Table 9.21.

Identifying appropriate trainees The author suspected that some trainees in the case study had little real intention of

recording GI in the long-term. Conversely, it also transpired that at least one junior

employee at a local government office really who needed the training hadn’t been

allowed to attend (see section 6.4.2). Were a similar event to be held again, the trainer

would liaise with PONT’s Coordinator in Mbale to identify those who really need the

training. By focussing training on those who need GI for their daily work it is expected

that Satisfaction and Motivation will be higher, use of the system more frequent; the

aim would be that regular use will become a Habit. Consequently we would see

improvements in Intention to use, Initial use and Sustained use.

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Competing prioritiesIn discussion with those trainees who were still contactable a year later, it became

apparent that they had not used the system because of competing priorities. A stipend

or other remuneration might have raised the profile of mapping activities. The value of

the stipend might be small in comparison with the overall cost of the TACC project but

could energise the mapping effort by giving an immediate Benefit and raising

Motivation with the result that Intention to use, Initial and Sustained use would be

enhanced. Were a similar event to be held again, the visiting trainer would liaise in

advance with UNTACC’s Project Manager and with PONT’s Coordinator in Mbale to

identify a person from each district (Mbale, Manafwa and Bududa) who might

reasonably be appointed as District Mapping Coordinator. The District Mapping

Coordinators would inter alia be responsible for encouraging local mapping activities

such as mapping parties. Mapping parties have been found to help Motivation (Open

Cities, 2013; Mapping Day, 2014; also see section 6.4.3) and should help to encourage

Sustained use should initial enthusiasm wane.

A post-training action plan can help trainees contextualise the training in their daily

work, and provide a focus for subsequent activities to ensure they create and update

local GI on a regular basis (Phaiju et al., 2010). Such an action plan might include key

skills and insights from the training event, specific actions the trainee will take in a

given period to try out some of these skills and insights, obstacles that might keep

him/her from actually doing this, and strategies to get around those obstacles (Johnson,

2011).

Travel effortIt was time consuming for contributors to obtain a GPS to assist with ground truthing.

The only GPS in the region that was capable of recording track-logs was held in the

PONT office in Mbale; for many trainees, travelling from home to Mbale, to a point of

interest, back to Mbale, and then home could take an entire day. By providing GPS

devices in each district, a contributor could obtain one at short notice, begin surveying

straight away and retain the device for a day or two in order to read track-logs into an

editor or upload to an on-line mapping system. The interactivity of a consumer-grade

GPS might be intrinsically motivating and encourage use; conversely the simplicity and

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economy of a data logger GPS might be more practical depending on budget available

(see 3.3.2). The provision of locally-available GPS devices should reduce the personal

Cost of contributors (travel time and cost), enhance both Perceived and Actual

facilitating conditions and so improve Intention to use, Initial use and Sustained use.

Difficulty in using equipmentIn the NGO expatriate volunteers case study (6.4.4) it became obvious the extent to

which contributors need help when using crowdsourced mapping technologies, even

though a contributor might have relevant training and education. The only training

courses held in Mbale were held in January 2011 and no-one locally was shown how to

train more contributors on an on-going basis. The Map Kibera case study (6.4.6)

showed that giving local people responsibility for on-going mapping improved local

‘ownership’ of the project. If District Mapping Coordinators were appointed and shown

how to train contributors, they would be able to support people in a more sustainable

way. That would reduce the contributors’ effort (Cost) and would enhance both

Perceived and Actual facilitating conditions and so improve Intention to use, Initial use

and Sustained use.

Another lesson learned from the Red Cross (6.4.5) and Map Kibera (6.4.6) case studies

is that managers found it useful if local contributors specialised. For example, someone

who surveys on a regular basis learns how to do well at it; similarly someone who edits

the map regularly gains familiarity and skill at editing. Having a small number of

regular users, rather than the relatively ‘scatter gun’ approach taken in the broad-based

training case study, should lead to more Frequent use by each contributor, which in

turn will develop Habit and so enhance Sustained use.

Contributors might not appreciate results and so experience disillusionmentBy providing feedback to contributors, they will be aware of the Benefits that the GI

brings, that should improve Satisfaction and so improve Motivation. Such feedback

might include an appreciation of the amount of coverage of their locality, uses their GI

has been put to, and examples of how other contributors’ GI has been used. Ultimately

this would encourage Sustained use.

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Staff turnover, skills atrophy and lack of local ‘ownership’These three problems have been grouped together because the proposed safeguards are

the same for all three. Between the field studies in January 2011 (section 6.4.1) and

April 2012 (6.4.2) there had been a high turnover in staff so that half of the original

trainees were no longer in their same post the following year. Those who had been

trained had not used the technologies so their skills had atrophied. No local staff had

the skills, materials or authority to act as local trainer or coordinator, so new training

had to be sourced from outside in 2012. Ultimately there was no local ownership of

mapping initiatives; mapping seemed to be considered as a requirement imposed from

outside.

The role of a District Mapping Coordinator has been mentioned above. In addition to

organising mapping parties, the Coordinators would support and reinforce existing

contributors, and train additional contributors when the need arises. The District

Mapping Coordinators would report – for the purposes of mapping – to the UNTACC

Project Manager. The Project Manager would be responsible for developing local

ownership of mapping initiatives.

An outline plan for a two-week capacity building deployment might be, for example:

Monday and Tuesday: Make final preparations for training event. Gather local

track-logs to use in practical exercises.

Wednesday, Thursday and Friday: Train the District Mapping Coordinators in

(a) how to use the mapping technologies, and crucially (b) how to train others.

Monday and Tuesday: District Mapping Coordinators train other contributors in

how to use the mapping technologies (under the supervision of the visiting

trainer).

Wednesday, Thursday and Friday: Review trainees’ action plans with their line

managers. Support trainees’ initial mapping activities.

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Summary of example case studyImproved outcomes expected

Problems identified Safeguards proposed

Benefits

Costs



Motivation

Satisfaction

Frequent use

Stable context

Habit

Intention to use

Initial use

Sustained use

Identifying appropriate trainees.

Focus on those who need GI for daily work use. Y Y Y Y Y Y Y

Competing priorities.Stipend for contributors or at least for Coordinators. Y Y Y Y YMapping parties. Y Y YAction plan. Y Y Y Y Y

Travel effort. More GPSs/dataloggers/ smartphones. Y Y Y Y Y Y

Difficulty in using equipment.

District Mapping Coordinators. Y Y Y Y Y YFew regular users; specialise in surveying or mapping.

Y Y Y

Contributors might not appreciate results and so experience disillusionment

Feedback. Y Y Y Y

Staff turnover.Skills atrophy.Lack of local ‘ownership’.

Train the District Mapping Coordinators; Coordinators train the trainees.

Y Y Y Y Y Y Y Y

Local leader e.g. UNTACC PM. Y Y Y Y Y Y

Table 9.21 Example use of TASUT to develop safeguards

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Summarising the above safeguards in logical order – as opposed to chronological order

– the UNTACC Project Manager would be made the leader of mapping activities with

the aid of a team of three District Mapping Coordinators. The District Mapping

Coordinators would train the other trainees. The UNTACC Project Manager would

ensure that trainees were people who need GI for daily work use, because it is better to

have few regular users than many occasional users. Users could specialise as those who

(mostly) survey localities with GPS etc., and those who (mostly) record GI in a

crowdsourced mapping system. As far as possible, all trained contributors would

receive a stipend for activities they take part in; at the very least, there would be a

stipend for District Mapping Coordinators.

During the capacity building deployment, the District Mapping Coordinators would be

trained on how to train others, and they would train the wider group of trainees in the

latter part of the deployment. Immediately after the training event, each trainee would

develop an action plan for mapping activities in forthcoming months.

At least one GPS or datalogger would be given to each District Mapping Coordinator

that would be made easily available to contributors. Each District Mapping Coordinator

would monitor local activity, provide feedback to contributors and organise mapping

parties to enhance satisfaction, maintain motivation and help ensure sustained use. The

Coordinators would also be responsible for training more contributors as the need

arises.

9.6. Chapter summaryIn general, evaluators were supportive of the TASUT model and the associated

guidelines. Some changes have been made to the model as a result of their comments; a

major potential redesign to use iteration was also suggested and this is discussed in

section 10.4.5. The guidelines have not required many changes; the most significant is

the addition of an executive summary to better promote the adoption of the model.

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10.Conclusion

10.1. Reflections on project objectivesAll of the project’s objectives have been met. The first, to establish disaster

management organisations’ requirements for digital geographic information both for

disaster management and other every-day uses, was achieved both by the literature

review in Chapter 2, and by an analysis of interviews with employees of relevant

organisations in Chapter 6.

The second objective was to review techniques and technologies for producing digital

maps in developing countries. The literature review in Chapter 3 sets out the various

alternative technologies for storing and retrieving GI, recording (surveying) GI and the

necessary supporting ICTs. The chapter evaluates their suitability for crowdsourced

mapping in developing countries. In addition, the evaluation draws on the author’s

experience of mapping Uganda, as described in Chapter 6.

During the empirical field work described in Chapter 6, it was confirmed that it was

important to identify contributors’ motivations and barriers to crowdsourced mapping

in the short and long term. Identifying them was the third objective and it was met

through a comprehensive review of various behavioural models in Chapter 4, and

through interviews with relevant stakeholders in Chapter 6.

Lessons learned from the literature review and the empirical work were used in Chapter

7 to propose an improved model of technology acceptance, which was the fourth

objective. Various key points from the literature review and empirical work were drawn

together and used to derive a prototype of the Theory of Acceptance and Sustained Use

of Technology (TASUT) model.

To help ensure the TASUT model is used in practice, key points from the literature

review and the empirical work helped inform the contents of the guidelines on how to

apply the TASUT model to crowdsourced mapping initiatives in developing countries,

which was the fifth objective. The guidelines in Chapter 8 provide practical guidance to

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managers of crowdsourced mapping initiatives and are intended to facilitate actual use

of the TASUT model.

In order to validate the work done, the final objective was to evaluate the model and

guidelines, and make further improvements. The evaluation process and results are

documented in Chapter 9. In general, the feedback from the experts confirmed the

design of the model and usefulness of the guidelines. A few changes were made to the

prototype TASUT model to produce the final version. Also an Executive Summary was

produced for the guidelines in order to clarify their purpose and benefits.

10.2. Key achievements10.2.1. The TASUT modelThe fieldwork identified the crucial need for sustained motivation for crowdsourced

mapping initiatives. Whereas many studies of technology acceptance models conducted

in industrialised nations identified a strong relationship between stated intention and

actual use, during the fieldwork in East Africa the opposite was found to be true:

following through from stated intention to actual use proved difficult. The various

TAMs studies had not addressed longer-term motivations for, and barriers to, the use of

modern technologies. In Chapter 7 various improvements to UTAUT2 are described

that make the new TASUT model more applicable to crowdsourced mapping for

disaster management in East Africa, especially in relation to longer-term sustainability.

The TASUT model is further refined in Chapter 9 as a result of feedback from external

evaluators.

10.2.2. Guidelines for applying the TASUT model to mapping in developing countries

Using best practice from the literature review, various case studies and interviews with

experts, Chapter 8 provides guidance on how to apply the model to crowdsourced

mapping of developing countries for humanitarian purpose including disaster

preparedness. The focus of the guidelines is to ensure people actually use the mapping

technologies, both in the short and long term. Practical advice identified in previous

chapters is brought together under the various TASUT headings. As with the TASUT

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model, the guidelines are further refined in Chapter 9 as a result of feedback from

external evaluators.

10.2.3. Review of behavioural modelsChapter 4 provides a very comprehensive review of various models, including:

three disaster preparedness models;

two innovation models;

three decision-making models;

six technology acceptance models (plus a few secondary derivations);

one combined decision-making and technology acceptance model;

five motivation models; and

four sustained use models.

The key features of each model were described, and key points that were especially

relevant for crowdsourced mapping in developing countries were highlighted. This

review provided a firm foundation for the development of the TASUT model.

10.2.4. Crowdsource mapping for disaster preparedness concept

In September 2010 the author first published the concept of using crowdsourced

mapping for disaster preparedness, and the related issue of governments using open

data for building resilience to natural hazards and the impact of climate change

(Farthing and Ware, 2010). In that paper it was proposed that if organisations could be

encouraged to collect GI for day-to-day economic development tasks, for example,

much of that information would also be useful in the event of a disaster. At that time,

no-one else had published this concept. According to personal correspondence, this

paper was forwarded in 2011 to a consultant at the World Bank’s Global Facility for

Disaster Reduction and Recovery (personal e-mail correspondence, R Battenberg). The

value of this approach is confirmed in that, subsequently, the concept was adopted by

the Humanitarian OpenStreetMap Team (HOT, 2011), World Bank (2012; Haklay et

al., 2014), and the Graduate Institute of International and Development Studies in

Geneva (Narvaez, 2012).

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Published work on government organisations using crowdsourced GI as part of

initiatives to build resilience to natural hazards and the impact of climate change is also

sparse. The World Bank report Crowdsourced Geographic Information use in

Government (Haklay et al., 2014: p12) said that it is, “the first study and report of its

kind to deal directly with [volunteered GI] use in government.”

The current research project commenced years before the above-mentioned documents

were published, and several of them cite the conference paper Farthing and Ware

(2010).

10.3. Critique of research method adopted

10.3.1. Abductive strategyThe aim of this project is to provide guidance to an organisation that is planning a

mapping initiative by modelling the factors that enable and encourage crowdsource

contributors to adopt and use mapping technologies. As explained in section 5.4.2, to

achieve this, an abductive strategy has been adopted for analysing qualitative data to

derive a new technology acceptance model and to develop associated guidelines. This

is a somewhat unusual approach among technology acceptance models. Among the

decision-making and technology acceptance models documented in section 4.4, all but

one of them followed deductive strategies by first hypothesising a model and then using

quantitative data analysis to identify which determinants were strongest. The exception

in section 4.4 was Lindsay et al. (2011), who adapted TAM3 for mobile police

technologies abductively using qualitative data. A relatively small number of other

technology acceptance studies have used qualitative data. For example, Williams et al.

(2011) systematically reviewed 450 articles that cited the original UTAUT paper by

Venkatesh et al. (2003) and found that only nine articles were based on qualitative data.

The abductive strategy has worked well in this PhD project as it has allowed the

TASUT model to be based on actual experience, rather than hypothesised in advance.

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10.3.2. Grounded and action researchThe TASUT model and associated guidelines are aimed at helping to solve very real

problems in developing countries. A strength of this project, in comparison with those

that produced the previous technology acceptance models, is that it has identified

several determinants of sustained usage from an analysis of real-world case studies and

the experiences of experts who have worked in the field. Many of the previously

existing models considered only a narrow set of determinants, which put them at risk of

hypothesis blindness: only determinants that featured in the hypothesis were measured.

The conclusion of the Dynamic TAM study (Davis and Venkatesh, 2004),

which aimed to identify factors in sustained and long-term usage, might be

paraphrased as ‘if someone says they will do something, then they will do it and

they will continue to do it' (see section 4.6.1); this conclusion was not borne out

by the empirical work in Chapter 6.

Several other models showed Behavioral intention as the sole determinant of

Use behavior; Venkatesh et al. (2012) subsequently found that this relationship

applies only among inexperienced users.

Legris et al. (2003: p202) highlighted concerns about unrepresentative samples

in their meta-analysis of 22 articles about technology acceptance research. (1)

Nine of the studies involved students, and they thought the research would be

better if performed in a business environment. (2) Most studies examined the

introduction of office automation software, and they thought the research would

benefit from examining other types of application. (3) Most of the studies

analysed self-reported use, not actual use.

In this PhD project, interviews with experts used open-ended questions to elicit a wide

range of possible determinants of sustained use, and actual mapping initiatives were

examined to identify what factors might lead to sustained use. By identifying the key

points from the empirical work, a much richer model has been developed that considers

a wide variety of possible determinants.

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Work on developing the TASUT model will produce little benefit for disaster

management unless it is actually adopted in practice. In this PhD project, the guidelines

in Chapter 8 are specifically designed to help people use the model.

Action research is bound up in a particular situation (Kurt Lewin, 1946), so the project

and its conclusions are limited to crowdsourced mapping for disaster preparedness in

East Africa. Nevertheless it is hoped that the model will be useful in other contexts in

and other areas of the world.

10.3.3. Qualitative data In section 5.4.2 the rationale for using qualitative data rather than quantitative data is

explained. The ultimate experiment for the TASUT model would be to compare the

outcomes of disasters in areas that had been mapped (in advance) by crowdsourcing

with those in areas that hadn’t been previously mapped. However, each disaster has so

many variables it would be impossible to control them all to gain statistically reliable

insights. Very large scale disasters are, fortunately for mankind, relatively rare events,

so a post-disaster validation of the model would necessarily be anecdotal rather than

statistical.

Obtaining a statistically reliable sample of disaster experts who could reasonably

evaluate the new model would be difficult because the population of such experts is

small. By way of comparison, the World Bank sponsored a study of the utility of

crowdsourced GI for government (Haklay, 2014). Despite the Bank’s high profile, and

incentives for people to take part, the online survey received only five responses (out of

3500 page views); the study team had to use their contacts to find a further 24

responses. In Chapter 6, qualitative responses provided a rich source of information,

and respondents were able to propose factors based on their actual experiences rather

than those constrained by closed-ended questions. In Chapter 9, the evaluators included

employees of NGOs who have extensive experience of crowdsourced mapping in

developing countries, an NGO worker who has created a successful volunteer network

in the Mbale region of Uganda, and academics with experience of geoinformatics for

disaster management. Their evaluations drew on a broad range of relevant experience.

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The research methods and tools adopted in this project have proved appropriate for

generating concepts for inclusion in the TASUT model and guidelines. Nevertheless,

this research work could be expanded in future in many ways, as suggested in the next

section.

10.4. Possible future directions and limitations of the research

10.4.1. Further validation and development of TASUTThe model has been developed in an abductive way from the various interviews and

case studies. By drawing key themes from this information and developing a theory

that fits, the approach uses ideas from grounded theory (Glaser and Strauss, 2009). The

proposed model and guidelines have been validated by drawing support from the

literature review, the interviews, the case studies and the final evaluators. The proposed

model and guidelines have not been validated statistically because of the small number

of participants in the various studies. It may be possible to conduct a quantitative

survey if relevant experts were gathered together, for example, at a conference or

workshop. However, experience from the evaluation exercise documented in Chapter 9

suggests that each of the experts required an hour or more to read and understand the

draft model and guidelines, and to write their feedback. A larger scale survey would

have to be very different in nature and would, in itself, be a major undertaking. The

outputs would be the opinions of experts not a post-disaster validation.

10.4.2. Broader application of the modelThis project has taken an action research approach in East Africa. Action research is

bound up in a particular situation, so it would be interesting to study whether the results

are of use in other types of activities whether crowdsourced or not, elsewhere in the

world, and in conjunction with other technologies. The TASUT model has been

designed to be widely applicable in that it deliberately avoids using words like

‘crowdsourcing’, ‘mapping’, ‘disaster preparedness’ and ‘developing countries’.

Studies in other situations may open up the possibility of validating the model

quantitatively. It is possible that new constructs, or new examples within a construct,

might be identified from a larger sample. If the sample size were large enough, it may

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even be possible to identify moderating variables such as age, gender and so on, as

discussed in section 7.6.2.

10.4.3. Psychological influences There may be a range of deeper psychological influences that are not modelled in

TASUT. Ajzen (2012: pp368-369) highlights the importance of psychology dimensions

such as personality traits and accessibility of dispositions. That is, individuals are

inclined to have certain beliefs, attitudes etc. that come easily to mind, whereas other

dispositions require some cognitive effort to be activated. An activated disposition

influences perceptions and judgements. Dispositions that are frequently activated

become more easily accessible. There may be scope for psychologists to evaluate

TASUT and propose improvements.

10.4.4. Self-mobilised behaviourAs discussed in section 7.6.1, self-mobilised behaviour could be important in some

situations. Although no examples were found of self-mobilised crowdsourced mapping

for disaster management in developing countries, one example from the USA in

response to wildfires in the Santa Cruz Mountains, California is documented in

section 2.6.1; there may be others. An analysis of factors that enabled a self-mobilised

response might suggest whether and how the TASUT model can be further enhanced.

10.4.5. Redesigning the TASUT model using iterationAll of the technology acceptance models documented in section 4.4 are acyclic directed

graphs, that is, iteration is not overtly supported. Among the decision-making models,

only TRA2 (Fishbein and Ajzen, 2009) is a cyclic directed graph in that it shows how

outcomes of actual behaviour can affect (reinforce or contradict) previously held

beliefs. TASUT conforms to previous technology acceptance models in being acyclic;

however, {UNI3x}, {NGO12x} and {NGO17x} queried whether the model should be

cyclic in order to show feedback overtly using a feedback loop. There appears to be

value in investigating whether sustained use is better modelled by overtly showing

feedback from the outcomes of actual use. Error: Reference source not found is offered

as a hypothetical cyclic technology acceptance model, though it has not been evaluated

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Expected future benefits

Intention to use

Figure 10.1: Possible use of cyclic determinants to represent reinforcement.

Expected facilitating conditionsExpected future costs

Habit

Actual use


Satisfaction

Actual outcomes

Feedback may result in increased or

decreased satisfaction.

Motivation

Dashed lines represent

feedback into the second and

subsequent iterations.


or validated. Justifying, designing and evaluating an iterative model could be a

significant project.

10.4.6. Sustained mapping using other techniquesOne of the project objectives is to identify factors for crowdsourced mapping in the

short and long term. There are alternative ways to create and update GI in the long

term. For example, some research has been conducted into using a remote sensing

technique called ‘Object-Based Image Analysis’ (Leidig et al., 2013) to identify

hazardous terrain and vulnerable features. Further research is needed to evaluate ways

in which GI from remote-sensing can be used as a ‘first draft’ map that can then be

refined with the aid of local knowledge and ground-truthing. Various potential

problems with GI gathered from remote sensing are identified in 3.3.5, but as analysis

techniques improve it may be possible to reduce requirements for human intervention

even further.

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A related avenue of research would be into using remote sensing to update existing

crowdsourced maps as developments on the ground evolve. For example, slum areas

can grow quickly and wooded areas can be cleared through illegal logging. Chima and

Trodd (2010) documented experiments in Abuja, Nigeria to sense changes in

boundaries of features remotely. It may now be possible to compare remotely-sensed

boundaries of disaster-relevant features with those recorded in a system like OSM and

update the map.

In the short-term, a good way to encourage sustained mapping is to identify

contributors who need up-to-date GI for their daily responsibilities (Farthing and Ware,

2010; see also section 2.8.3). If GI about utility services, key facilities, land cover and

so on were recorded in an open-access source, it would be available for disaster

management purposes.

10.5. SummaryA detailed digital map of the whole world would have seemed nearly impossible 20

years ago, but is a tantalising possibility in perhaps the next 10 or 20 years thanks to

crowdsourced mapping and related technologies. Such online maps are already of great

use to disaster response organisations, especially where mapping initiatives are

conducted in advance of a disaster rather than afterwards. Map coverage of developing

countries remains a challenge, but a surmountable one if local communities take action.

Existing models of technology acceptance may be sufficient for the domains in which

they were developed, but they omit factors that are relevant to sustained crowdsourced

mapping in developing countries. The TASUT model is unique in bringing together

these factors into one technology acceptance model. The associated guidelines have

been evaluated as useful to managers of crowdsourced mapping initiatives, especially

to encourage them to consider a broad range of issues that are relevant to gaining the

cooperation of local communities. It is hoped that use of the model and guidelines will

result in communities being more prepared for disasters and so reduce loss and

suffering in developing countries.

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Appendix A

Philosophical contextThis appendix sets out the background philosophies that form people’s ontological and

epistemological assumptions.

Ontological assumptionsOntological assumptions relate to the ways we answer the question, “What is the nature

of… reality?” (Blaikie, 2007: p6). Ontology has been described as “the science or study

of being” and is said to include assumptions about “what exists, what it looks like, what

units make it up, and how these units interact with each other.”

The main schools of thought are realism, relativism and nominalism. Traditional

realism holds that the world is concrete and external, and that science progresses by

making observations of phenomena (Easterby-Smith et al., 2012). Relativism is based

on the acceptance that scientific theories are hotly debated; often the strength of a

scientific argument is based on the scientist’s status, the politics of business or the

resources available. Thus the ‘truth’ of a scientific theory is reached through discussion

and agreement. The current debate about global climate change and its causes is a good

example of this. Within social sciences, nominalism says that social concepts (e.g.

social class and racial discrimination) are defined and experienced differently; what

counts as ‘truth’ will vary from person to person, place to place, and over time. Thus

social reality might be no more than the creation of people through language and

discourse.

One might say that ontology helps us uncover our assumptions about whether

phenomena such as culture, power and control really exist or are created in our minds.

If these underlying assumptions are not identified and considered, the researcher

may be blinded to certain aspects of the inquiry or certain phenomena, since

they are implicitly assumed, taken for granted and therefore not opened to

question, consideration or discussion. (Flowers, 2009: p1)

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Epistemological assumptionsEpistemological assumptions relate to the ways people answer the question, “How can

reality be known?” Epistemology is about the nature and scope of human knowledge,

the kinds of knowledge that are possible, and the criteria for judging the adequacy of

knowledge (Blaikie, 2007). Among the many schools of thought are positivism and

constructivism.

In general, a ‘positivist’ paradigm confines itself to the data of experience and excludes

a priori speculation. Positivism holds that knowledge exists independently of the

researcher; there is an absolute truth that can be proved. Positivism is attributed

originally to the French philosopher Auguste Comte (1798–1857) and is closely

associated with the realist ontology. There are specific forms of positivism, such as

rationalism, which hold that certain types of truth can be grasped intellectually without

physical evidence and truth can be deduced rationally (Walliman, 2011). Another form,

empiricism, states that knowledge comes primarily from sensory experience and

physical evidence; one arrives at truth through inductive reasoning.

Scientists have come to realise that almost every theory or law will one day be

superseded. Creswell (2009) suggests that few now hold a strongly positivist world

view, that is, that we can be absolutely positive about truth. However, a new positivism

lives on in what he calls ‘post-positivism’. This paradigm accepts that in reality we can

believe only in a probability that something is true. Post-positivists are unlikely to say

they have proved a hypothesis true, but that they haven’t found a reason to reject it.

Easterby et al. ([1991] 2012) set out the ‘constructivist’ (or ‘social constructionist’)

paradigm. Constructivism holds that reality is not directly knowable, and can only be

inferred or assigned by consensus; meaning is generated by individuals and groups.

This paradigm holds that the researcher should not gather facts and measure patterns,

but instead should appreciate the different constructions and meanings that people put

upon their circumstances; see Table A.1. It is closely associated with the relativist and

nominalist ontologies.

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Positivism ConstructivismThe observer… must be independent is part of what is being

observedHuman interests… should be irrelevant are the main drivers of

the scienceExplanations… must demonstrate

causalityaim to increase general understanding of the situation

Research progresses through…

hypotheses and deductions

gathering rich data from which ideas are induced

Concepts… need to be defined so that they can be measured

should incorporate stakeholder perspectives

Units of analysis… should be reduced to the simplest terms

may include the complexity of ‘whole’ situations

Generalisation through…

statistical probability theoretical abstraction

Sampling requires…

large numbers selected randomly

small numbers of cases chosen for specific reasons

Table A.1: Contrasting implications of positivism and constructivism(Easterby-Smith et al., 2012)

People can hold very strong views that only one of these paradigms is valid, perhaps

labelling the other paradigm naïve or unscientific. In the author’s view, both paradigms

hold some truth. There are domains, such as basic mathematics, where reality or

correctness is provable; they nevertheless require mental models to infer and

understand them. In other domains, such as informatics and sociology, phenomena are

often difficult to prove unequivocally and ‘proof’ relies heavily on the context,

inference and mental constructs. The situation might be represented as in Figure A.1.

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← Reality →Domain Provable Inferred (Positivist) (Constructivist)

Basic mathematics

Algorithms

Simple science

Advanced physics

Medicine

Informatics

Psychology

Sociology

Figure A.1: Representation of positivism and constructivism in various domains.


Consequently researchers who work exclusively in certain domains may feel strongly

that positivism is the only valid paradigm, and researchers in other domains may feel

the equally strongly about constructivism. The adoption of a positivist or a

constructivist paradigm will influence the ways in which the researcher approaches a

study, as shown in Table A.2.

Positivist Constructivist 1. Researcher is independent Researcher is involved

(e.g. action research)2. Samples typically large

(often quantitative analysis)Samples may be small (often qualitative analysis)

3. Testing theories(e.g. deductive)

Generating theories(e.g. inductive, retroductive)

4. Experimental design Fieldwork5. Universal theory Local knowledge6. Verification/ Falsification Comparison/ triangulation

Table A.2: Key choices of research design(Adapted from Easterby-Smith et al., 2012)

Easterby-Smith et al. (2012) compare how these ontologies and epistemologies might

interact.

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Ontology Realism Relativism NominalismEpistemologyMethodology:

Positivism Constructivism Strong constructivism

Aims Discovery Convergence InventionStarting points

Hypotheses Questions Critique

Designs Experiment Cases and surveys Engagement and reflexivity

Data types Numbers and facts

Words and numbers

Discourses and experiences

Analysis/ interpretation

Verification/ falsification

Triangulation and comparison

Sense-making and understanding

Outcomes Confirmation of theories

Theory generation New insights and actions

Table A.3: Methodological implications of different epistemologies(Adapted from Easterby-Smith et al., 2012)

Cupchick (2001) proposes ‘constructivist realism’ as a paradigm that accommodates

both positivist and constructivist views and methods. He argues that the positivist and

constructivist philosophies aren’t so different in reality: positivists accept that

measuring a phenomenon alters it and that ‘proven’ theories may be superseded from

time to time; constructivists accept certain facts as real provided the context is clear.

For some types of research, among the alternatives to positivist and constructivist

paradigms is the ‘advocacy’ (or ‘participatory’) paradigm. Creswell (2009) explains

how various authors developed this paradigm during the 1980s and 1990s in relation to

research about marginalised individuals and social justice. In advocacy projects, there

is an agenda for reform to benefit the lives of participants, institutions and societies.

Specific issues are addressed, such as empowerment, inequality, oppression,

domination, suppression and alienation. Typically the researcher works collaboratively

with participants so as not to marginalise them. Advocacy research provides an outlet

for the participants’ concerns, and may provide an agenda that they can use to press for

reform and change.

Kemmis and Wilkinson (1998: p280-283) list seven key features of the advocacy

paradigm:

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1) Participatory action research is a social process; it deliberately explores the

relationship between the individual and the social context.

2) It is participatory; it engages people in examining their knowledge and

interpretation.

3) It is practical and collaborative; participatory action research examines the

social practices that link people with others.

4) It is emancipatory. “Participatory action research aims to help people recover,

and release themselves from, the constraints of irrational, unproductive, unjust

and unsatisfying social structures”

5) It is critical; it aims to release people from the constraints embedded in the

social mechanisms they use.

6) Participatory action research is reflexive (recursive); it helps people investigate

reality in order to change it.

7) Participatory action research is transformative; it does not regard theory or

practice as preeminent, but aims to articulate and develop both.

In ‘cultural relativism’ the researcher should suspend his/her own cultural biases when

attempting to understand beliefs and behaviours in their local contexts. William

Graham Sumner (1840–1910), one of the great advocates of cultural relativism, warned

against a “view of things in which one’s own group is the center of everything, and all

others are scaled and rated with reference to it” (Sumner, [1906] 1992: p13). Avoiding

this ethnocentrism is important, though difficult in practice.

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Appendix B

Evaluation forms

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Theory of Acceptance and Sustained Use of Technology (TASUT)Feedback and evaluation

I’d be grateful for you help please. As the final part of my PhD project I need some feedback from relevant people on TASUT and the accompanying guidelines. Your responses will be anonymised except where specifically stated below.Dave W FarthingUniversity of South Wales

1. About you1.1 For my records, please confirm your name:

{Name removed}

Please note that in the report I shall refer to you as {NGO8x} rather than use your name.

1.2 Please enter your role at this organisation (this will be published):

Director

(If you are the only person with that role (e.g. Head of...) then bear in mind that a reader might be able to deduce who you are. If you don’t want this, enter something less specific like ‘A manager’.)

1.3 Please tell me a little about your experience in disaster management, mapping/GIS, crowdsourcing/VGIS, developing countries, or behaviour modelling.

Developing the Map Kibera project, the Map Kibera Trust and several other related projects worldwide, using OpenStreetMap.

1.4 Where an evaluator writes something that is particularly pertinent, I would like to quote your exact words. Are you content for me to quote your exact words in my dissertation?Yes

2. Diagrams, explanation and definition (‘Annex B’)The following will be published only in summarised form.

2.1 Did you find the simplified TASUT diagram (Figure 13.1) understandable? And did you find the full TASUT diagram (Figure 13.2) understandable?

Yes

2.2 Concentrating on the full TASUT diagram (Figure 13.2), do you think it is too simple or too complex? Is the full TASUT diagram complete (is something missing)? Does it contain factors

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that you consider to be unnecessary (what should be removed)? If so, please explain why.

Well, without reading the whole PhD it doesn't necessarily explain itself. I mean, I understand the terms but whether or not I agree that this is the right model would require a lot more critical thinking, ie, to understand how you arrived at it. I have skimmed through several parts of the dissertation so I understand somewhat.

2.3 The factors are joined by arrows to indicate which of them affect others. Do you think they are complete, or are some missing? If so, please explain why.

Seem complete, but again I'd have to think it through

2.4 Before the full diagram is some introductory text. Is it understandable? How helpful is it? Was there anything that it didn’t explain well?

Click here to enter text.

2.5 After the diagram are some definitions. Are they understandable? How helpful are they? Was there anything that they didn’t explain well?


3. Guidelines for applying TASUT to mapping for disaster preparedness (‘Annex C’)

The following will be published only in summarised form.

3.1 Did you find the guidelines understandable? Is the writing style clear?

Yes, although I'm distracted by all the references.

3.2 Are the guidelines too short or too long? Are the guidelines complete (are any factors missing)? Do they contain material that you consider to be unnecessary (what should be removed)? If so, please explain why.

Fine length.

As for motivations: you're missing skill building/education. Most we've trained want to learn a potentially marketable skill or general computer skills for employability.

Honestly, I'm not sure about the Habit part. I've not really seen re-mapping happen just out of habit. Or perhaps I'm considering the part about long term use more broadly. In general, long term mapping will happen when there are reasons to keep mapping – all the same conditions that led to initial mapping, only ideally it will happen faster and better. In other words, there has to be funding for it, and incorporation of outcomes into broader processes and institutions, and ideally an increasing acceptance and awareness by officials, powerful people, the local community, etc.

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3.3 Are the topics in the right sequence?


3.4 If you knew someone about to conduct a mapping or capacity-building initiative for disaster preparedness, would you recommend these guidelines? Are the guidelines effective and practical? Do you think they will encourage mapping initiatives in both the short- and long-term? Do you think the guidance would be suitable only for East Africa or elsewhere too?

It seems not very tailored to East Africa but more generic, could be used elsewhere. In fact, the biggest guideline I would give to those coming to map East Africa is to talk to those of us you interviewed first – not to duplicate, or reinvent the wheel. This is quite common.

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{Name removed}



Executive Director



Have worked to provide geographic data to disaster managers for the past 13 years. Have been working with crowdsourcing projects for the past 5 years.

1.4 Where an evaluator writes something that is particularly pertinent, I would like to quote your exact words. Are you content for me to quote your exact words in my dissertation?Please enter Yes or NoYES



I find both diagrams a bit difficult to understand. I think they both leave out the value

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returned from the technology as a motivation.

2.2 Concentrating on the full TASUT diagram (Figure 13.2), do you think it is too simple or too complex? Is the full TASUT diagram complete (is something missing)? Does it contain factors that you consider to be unnecessary (what should be removed)? If so, please explain why.

I think the complexity is okay ,but I would expect Performance Expectancy to fall into the Use Behavior and the Sustained Use Behavior often during the introduction to technology people still do not completely understand its capabilities and it requires sustained use.


Other than the Performance Expectancy falling into each stage I think it is complete.


This sentence isn't very good in the annex: "It models factors that enable and encourage local contributors to adopt and use mapping technologies such as GPS and OpenStreetMap." The reason being that OpenStreetMap using GPS, so it reads a bit strange.


yes they are fine.




The guidelines are understandable.


The guidelines are good, though I think use of the data needs to be stressed. Meaning often the focus is so much on collecting the data that then using it is forgotten.

3.3 Are the topics in the right sequence?339


Yes


I would potentially share the guidelines, though there are OpenStreetMap specific ones I’d be more likely to use.

340





{Name removed}



PONT Advisor



I have been involved in the PONT project in the Mbale Region of Uganda for 10 years. During that time I have been involved in a wide range of community development activities such as primary healthcare in villages. This has included being trained as a Ugandan primary healthcare worker and encouraging local villagers to change behaviours to improve levels of health within communities. I was involved in helping to deal with the after effects of a landslide in Mbale in 2010 which killed a number of people and displaced a large number of families. I have direct experience of the effects of landslides and rockslides in rural areas of Uganda. I have direct experience in projects to plant trees in Mbale working with local farmers and communities. I spent 6 months as a project manager working on the United Nations TACC project that looked at the physical effects of climate change on rural communities in Eastern Uganda which included potential environmental disasters such as landslides resulting from climate changes.

1.4 Where an evaluator writes something that is particularly pertinent, I would like to quote your exact words. Are you content for me to quote your exact words in my dissertation? Yes

2. Diagrams, explanation and definition (‘Annex B’)341




Yes they were both understandable


I do not think it is too simple or too complex. The model is helpful in bringing together a number of diverse factors and informative in identifying those factors that need to be considered. The purpose of the ‘Performance expectancy’ construct might be clearer if it were renamed ‘Benefits’. This name would indicate more clearly that it’s about the benefits of using the technology, not expectations about the performance of the participants. The name ‘Benefits’ would also go well with another of the primary constructs, ‘Costs’


All the technology acceptance models are acyclic directed graphs, i.e. there is no iteration. In real life motivation and behaviour are reinforced by beneficial outcomes from actual behaviour. This reinforcement is certainly implied by ‘Satisfaction’, but it might be worth considering wether such reinforcement of behaviour would be represented more clearly if modelled using iteration.


Yes it was understandable and I found it very clear and helpful in explaining the elements of the diagram. I didn’t feel there was anything it didn’t explain well


Yes they are understandable and they were very useful. I didn’t feel there was anything they didn’t explain well.




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Yes, they were understandable and the writing style was very clear


I think the guidelines are the right length and I don’t feel that anything is unnecessary but as a result of my experiences in Uganda there are a few areas that you may want to consider. It is vitally important for a mapping initiative to identify suitable people. This can be done through working closely with local NGO’s and building on the work they are already undertaking in the area, but the mapping needs to be done as part of their everyday activities; tasks that add to peoples’ workload will not be welcomed. As part of the PONT primary healthcare project we have trained over100 people to be Primary healthcare workers and over 500 community health promoters in the Mbale region. They are all unpaid volunteers but because of the benefit of the work they do, they are held in high regard by the community, which may also be the case for those people involved in disaster preparedness. I have found that this enhancing of a person’s status within the community is a powerful motivator. This could be reinforced if the volunteers felt that they had access to people in powerful positions such as political and community leaders and local government officials and if possible central government officials. In relation to status being a motivator, I have found that women often appreciated improved status in a society where women are generally undervalued. If the volunteer is married it is important to cultivate the husbands’ support. If the husband sees this work as beneficial to his own status, then he will support his wife; if he sees it as a threat then he may confiscate equipment or otherwise obstruct the work. Another motivator could be a persons religious beliefs. For some religious people, being a ‘good witness’ (example to others) was a positive motivator in humanitarian work. For other religious people, disasters may be viewed fatalistically as inevitable or a divine punishment, an ‘Act of God’ as it were. In an ideal world, initiatives would be self-mobilised, but at the moment humanitarian initiatives often need to be driven by someone in authority. It’s a sad fact that, in Uganda, someone from outside Uganda is often seen as a stronger figure of authority than a local person. Similarly, practical support and funding (‘Actual facilitating conditions’) are likely to be more highly regarded if they come from abroad.


Yes


Yes I would certainly recommend these guidelines as they are both effective and practical and I think they provide a framework that will be very useful to anyone conducting a mapping or capacity-building initiative for disasater preparedness. Yes I think they will encourage mapping initiatives. Although it will initially be very useful in East Africa there are elements in the guidance that can be used anywhere and it is possible to culturally adapt the guidance so that all of it can be used in any country.

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344





{Name removed}

Please note that in the report I shall refer to you as {UNI3x} rather than use your name.


Associate Professor (Remote sensing for disaster risk management)



About 20 years work and research experience in geoinformatics for DRM, PhD in volcano remote sensing; past ca. 12 years focus on use of geoinformatics for post-disaster response, in particular damage mapping. Includes use of UAV imagery, geometric data processing. Work in VGI for past 2 or 3 years; working group leader of COST Action Mapping and the Citizen Sensor (focus on understanding and influencing volunteers); Work at ITC has largely focused on LDCs, and I have previously worked in Costa Rica, Nicaragua and the Philippines

1.4 Where an evaluator writes something that is particularly pertinent, I would like to quote your exact words. Are you content for me to quote your exact words in my dissertation?Yes


2.1 Did you find the simplified TASUT diagram (Figure 13.1) understandable? And did you find

345


the full TASUT diagram (Figure 13.2) understandable?

I’m not sure both are needed. It takes time to understand them individually, and gain time to relate the simple to the complex one. Not alays is the perspective clear (e.g., motivation refers to the motivation of the volunteers, but there are also pertinent aspects related to how the organizer can motivate someone. Here I was lsot at times. It’s also a bit unclear what you exactly want to use the model for. You say the purpose is “is to help managers of crowdsource mapping initiatives in developing countries” – can such an abstract conceptualization help, or will it be used? How can you influence that, work towards that? There is a risk that it remains an academic exercise.


The model is not immediately accessible. Maybe grouping some boxes, making it clear what they belong together will help. Alos the starting point of how to read this is not clear. There are also more connections that exist but are nto indicated; for example, motivation is linked with habit, satisfaction, and of couse directly with expectation, social influences, etc. Not clear why motivation is shown to exist in isolation. Also it’s not so clear how exactly this can help at a practical level to implement a new project.


see comment above. I also wonder if there are reciprocal relationsips. E.g. if you have a certain use behaviour, and, say, a positive experience, that will also affect your motivation.


I find the motivation (“to help managers of crowdsource mapping initiatives in developing countries.”) too vague and unverifiable. How can you test if the use of your model as guidance really makes a difference. There is a risk that you propose something that no one will bother to pick up and implement. After all, many people have pretty firm ideas and are difficult to convince to ask differently. Thinking about how actually to implement the model, and to measure its effect (and tehn perhaps to refine it) would eb good


as I said before, some terms have differnet meanings depending on the contect (e.g, motivation relating to the volunteer, but that motivation also being driven/steered by the organizer. It’s not always clear

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I’m not sure why you put this in an annex. TO me it seems to be core of the work to look at how a model can be implemented and its effect tested. The text overall us quite woedy and vague (see my comment on the many “may”s. More references are needed to back up claims.


How do you plan to package this/ present this to your target managers. Let’s be honest, the thesis itself will not be widely read, and if you publish this ina journal such articles, too, are not so likely to be read by practitioners. So what to do?>


seems ok


I don’t find them very practical, though that is the nature of frameworks and models such as this one. It would be good to have a clear case where you have a theme addressed by differene efforts, where you could use one to test the model and ideally have something of a control groups. Not sure if that’s feasible though.

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{Name removed}

Please note that in the report I shall refer to you as {UNI4x} rather than use your name.


Senior Researcher



I have used GIS for the past 25 years and seen the approaches for participation in mapping from communities evolve. I have been involved in a number of participatory mapping projects with communities – the most pertinent being one on flooding issues and options for changes in management in a UK watershed. The p-mapping collected community identified options for management changes to ameliorate the flood risk. I have also undertaken participatory scenario mapping to identify community responses to proposed plans in terms of their likely behavioural response – how would they act under the changed conditions?

1.4 Where an evaluator writes something that is particularly pertinent, I would like to quote your exact words. Are you content for me to quote your exact words in my dissertation?YES


348



Yes


I think the diagram is useful and clear. I wonder if re-enfoercement or encouragement for use is included. For example, getting likes or points to encourage sustained habitual use is included.


I think the influence of others should feed into both initial and sustained.


I think it explained the concepts clearly.


They are clear and helpful




Guidelines are clear.


For managers – probably too long. I think putting up front why the guide is important for them – and how they can benefit from implementing the model would sell it better. At the moment I am not sure many managers would invest the time without a clearer benefit for them.

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I think a shorter executive summary needs to be upfront – with the benefits, usage or need for the guide more clearly articulated to managers from page 1. I think some key recommedations to promote success at the front – or even a flow chart of best practice would be useful for managers.


I think the guidelines are comprehensive and well written. However, I think the structure could be re-organised to ensure their use. Also if this is a guide – as opposed to academically focussed – then the references etc. could be removed and a key further reading section added for anyone interested enough to pursue them.

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Appendix C

Published papers

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Farthing, D. W., and Ware, J. M. (2010) When it comes to mapping developing

countries, disaster preparedness is better than disaster response. Proceedings of AGI

Geocommunity '10, Stratford Upon Avon, Sept 2010.

When it comes to mapping developing countries, disaster preparedness is better than disaster response

Dave W Farthing, Principal Lecturer, University of GlamorganDr J Mark Ware, Reader, University of Glamorgan

AbstractA study is being undertaken into how indigenous organisations in developing countries can develop digital maps for disaster preparedness. There have been recent high-profile examples of the mapping community updating OpenStreetMap and Google Map Maker in response to disasters in developing countries. Although laudable, our contention is that it would be better still if disaster-prone areas were mapped in advance so that relevant geographic information (GI) would be available from day one. The technology exists; the challenge is to identify a motivation and means for organisations in vulnerable areas to collect and maintain GI for themselves. Possible ways of meeting this challenge are explored and discussed.

BackgroundIn the event of a rapid-onset disaster, relief agencies need information about the locality before a disaster and information about the effect of a disaster. (Woof, 2010; Mafabi, 2009; Mawejje, 2009; Karatunga, 2005; Montoya, 2003; Cova, 1999)

Information about the locality before a disaster may include: vulnerabilities, such as population demographics, extent of flood plains, seismic

activity; roads/routes for emergency access and evacuation; bridges in case any get washed away; land marks, towns names and street names for navigation and context; health centres and hospitals for casualties; schools, sports fields and stadia as possible locations for relief agencies, field

hospitals, feeding stations and safe zones for the local population; water sources and sanitation for disease avoidance/reduction; emergency services and local authority buildings so relief workers know where to go

for help or to attend meetings; land use, vegetation etc. to predict fires, famine and flash flooding; topology:

o to identify the physical constraints of the disaster area; and o to assist with modelling potential floods, lahars, landslides.

Information about the effect of a disaster may include: the extent and severity of the disaster’s impact; estimates of casualties; the spread of hazards such as spills, infestation, infection or epidemic; damage to infrastructure, such as roads, bridges, water supplies and sewers; damage to disaster-relevant resources, such as hospitals; locations of disaster relief agencies and the areas they are serving, often referred to

as Who-What-Where, to create a common operational picture;

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areas that have been searched in order to minimise searching the same areas twice; public relations information.

Unfortunately developing countries often have rather sparse digital geographic information (GI). Some countries have never had a concerted topographical exercise; some haven’t since colonial days (Steklis et al, 2005); some were mapped by the US and USSR military in cold-war days; others have only ever been mapped at 1:1,000,000 scale as part of the ISCGM Global Map project. In yet other countries, non-digital mapping techniques still prevail, for example, Nepal where government bodies prefer compass and chain surveys (Shrestha, 2006).

So, the GI of a disaster area may be low-resolution, error-ridden and/or out-of-date (Karikari et al, 2005). Rarely does it show up-to-date locations of resources that are of interest to disaster relief agencies.

Disaster response solutionsBecause rescue work is most effective in the first 48 hours of a sudden-onset disaster, GI is needed very quickly. This means that disaster relief agencies– like MapAction ††† – often have to rely on freely available GI from sources like VMAP and OpenStreetMap. These sources may lack necessary detail about developing countries. Furthermore, restricted band-width in the disaster zone may mean that response teams have only the GI that they take with them from the airport.

Sometimes the affected nation has a national mapping agency. Their willingness to share GI varies; some choose not to share their digital maps with the disaster relief agencies. Those that do may have good quality digital GI, they may not. The agency may have been badly affected by the disaster itself. For example, in the 2010 Haiti earthquake disaster the offices of the Centre National d’Information Geomatique et Spatiale (national mapping agency) were destroyed, eight personnel were killed and almost all of their data and maps were destroyed.

Pre- and post-event satellite photogrammetry is made available under the International Charter Space & Major Disasters (International Charter, 2000). Images may become available a day or two after the event. This can sometimes be useful in identifying the extent of the disaster zone, but it says little about the human geography or human needs. Any information gleaned from such photogrammetry has to be ground-truthed, and this takes time. It also requires bandwidth to get it to the disaster zone and there may not be spare bandwidth on the communication satellites.

Recent developments in mapping for disaster responseThere has been a recent trend for open source and crowd-source maps to be developed immediately post-disaster. For example, after an earthquake hit Haiti on 12 January 2010 contributors from around the world helped to improve the amount of geographic detail on the OpenStreetMap and Google Map Maker web sites (Zook et al, 2010). The volunteers mostly relied on aerial images and traced features from them; few volunteers knew Haiti or had actually been there and there was little time or resource for ground-truthing the GI. OpenStreetMap and Google used Haitian ex-patriots (“diaspora”) to verify the crowd-sourced GI and provide details such as street names.

While this is laudable work, the GI is not available for disaster planning or disaster preparedness, it’s developed under enormous time pressures, it becomes available days or weeks after a disaster, and may be riddled with errors due to misunderstanding the aerial images (Zook et al, 2010).

Smaller disasters don’t attract such media attention and hence little or no crowd-sourcing activities. For example, on 1 March 2010 a landslide and flooding in the Bududa District of Uganda killed an estimated 400 people and displaced some 300,000 (UN, 2010). In response the organisations like the Red Cross and Red Crescent societies flew in relief supplies (IFRCRCS, 2010). The location of the affected villages and the local infrastructure were not added to either OpenStreetMap or Google Map Maker and there remains little usable GI of the affected area.

††† An organisation that supports humanitarian relief agencies in the field by providing geographic and other information. See www.mapaction.org

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Figure 1: Paucity of OpenStreetMap coverage of the location of a major landslide. (Source: Screen shot.)

None of this detracts from the amazing achievements of crowd-sourcing efforts after recent disasters. However we mustn’t become complacent. While open source and crowd-sourced maps may be part of the solution, it is apparent that it’s not the whole solution. The challenge is to devise a method whereby GI is recorded in advance of a disaster, is accurate and verified, is maintained up-to-date in the long term, is resilient to disasters itself, and is available from the moment the relief operation first swings into action.

Current work in mapping for disaster preparednessThe University of Glamorgan is working on a project to encourage indigenous government and non-government organisations to develop digital maps of their localities. A key challenge is to identify a motivation for these organisations to maintain the GI.

In an ideal world all countries would have sufficient resources to prepare for disasters, and that there would be intrinsic motivation to produce digital maps and keep them up-to-date. In developing nations, where resources are already over-stretched, preparing for events that might or might not happen some time in the future receive lower priority (Mitchell, 2006). Mitchell concludes that it’s unlikely that a GIS will be successful if used solely for one purpose, e.g. disaster preparedness; there will be economies of scale if the cost of systems, data, training, maintenance and so on can be shared for various purposes such as municipal planning, civil engineering and so on. From work in the field we can confirm that indigenous organisations have a day-to-day need for GI but simply don’t know how to access and/or create it. This project proposes that GI collected would be useful both on a day-to-day basis and also in the event of a disaster.

The proposal is to equip relevant organisations with simple, inexpensive GPS devices. Staff would be trained in the use of the GPS devices and relevant GIS and/or open-source mapping systems like OpenStreetMap. The organisations would record and use the GI to help with their everyday development work; as projects are completed – such as building hospitals, health centres, schools and so on – details would be recorded on the system. Much of this information could happily serve dual purposes: to help with day-to-day decision-making and to be useful in the event of a disaster. Examples of the latter include sending casualties to appropriate hospitals and health centres, locating internally displaced persons in schools away from the disaster area, and establishing bases for relief organisations or heavy lifting equipment in suitable playing fields.

By moving the focus from disaster response to disaster preparedness, we expect to realise specific benefits:

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Approx.

location

of


The GI is recorded in advance of a disaster, is more accurate and verified, is maintained up-to-date in the long term, is resilient to disasters itself, and is available from the moment the relief operation first swings into action.

The approach is scalable, because indigenous organisations from around the world can map their own areas.

The approach is sustainable, because GI is maintained for everyday use. The approach is empowering, because it is run by indigenous organisations not

imposed by outsiders.

Case studyThe University of Glamorgan has links with the District of Mbale, Uganda through the Partnerships Overseas Networking Trust (PONT)‡‡‡. Indigenous non-governmental organisations (NGOs) are encouraged to network with each other and with local government in order to work towards agreed priorities. The initial projects were training of primary care workers, distribution of mosquito nets, providing goats for orphans, and improving water supplies. Other work that is ongoing is improving access roads to schools so children can reach them in the rainy season, training local police and fire fighters and assisting with local IT needs.

Uganda UKPopulation 33,398,682 61,284,806Population growth rate 3.56% (Second highest) 0.28% (170th)Labour force 15,010,000 (2009 est.) 31,370,000Birth rate 47.55 births/1,000 pop 10.67 births/1,000 popDeath rate 11.9 deaths/1,000 pop 10 deaths/1,000 popLife expectancy 53 years 79 yearsGDP per capita (USD) $1,300 $35,200Area 241,038 km2 243,610 km2 Roads 70,746 km (2003) 398,366 km - paved 16,272 km 398,366 km - unpaved 54,474 km Public byways not included

Table 1: Information about Uganda, with UK data for comparison. (Source: CIA World Fact Book, 2010)

Population growth causes additional demand for arable land which – too often – results in deforestation. In mountainous areas destabilisation results in landslides (Knapen et al, 2006) like the one in March 2010 mentioned above (UN, 2010).

During 2009 several members of staff of Ugandan government and non-government organisations were interviewed to identify their needs for GI, both for everyday development work and in the event of a disaster. It became clear that most organisations had a need for GI but no means to access or use it. The only organisation in the Mbale/Bududa districts with a comprehensive GIS was the National Park Authority; this was created by the head office in Kampala but park rangers record events and sightings every day using hand-held GPS devices.

Central government had tasked district government authorities to create digital maps of their districts to assist the Peace Recovery and Development Programme. Many interviewees felt they didn’t have the skills to collect GI and assumed the equipment was prohibitively expensive. All were encouraged when they saw what could be achieved in a few days using relatively cheap equipment and software.

‡‡‡ PONT is part of the twinning relationship between Rhondda Cynon Taff and the town of Mbale, Uganda. See www.pont-mbale.org.uk

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Figure 2: Sample GPS data collected along the road to Bududa, Uganda. (Source: Project data.)

Future workThere is much work still to be done. Equipping and training staff at relevant organisations would be an important task. Experience at MapAction supports the view that users will need on-going support. Enabling mutual support will be important too so, in due course, the users can help each other. Clearly, identifying financial sources for the hardware, software and training will be a challenge.

The University is considering how it can best work with MapAction for disaster preparedness and capacity building.

In order to reduce transcription errors, experiments will be made with GPS data loggers to geo-tag photographs of points of interest. Smart-phones are not common in developing countries, but in due course there may be benefits in using smart-phones with cameras and built-in GPS receivers.

A major consideration will be to identify a means to store GI in the long-term. The storage mechanism needs to be easy-to-use, inexpensive and secure. The host needs to be stable and reliable. The GI needs to be freely available to contributing organisations and disaster relief agencies, so open-source and creative commons licences seem appropriate.

Having an approach that is successful in Uganda is no guarantee that it is applicable world-wide. Identifying and addressing local differences will be an on-going challenge.

References Cova, T.J. (1999) GIS in emergency management. Chapter in Geographical

Information Systems: Principles, Techniques, Applications, and Management. (P. A. Longley, M. F. Goodchild, D. J. Maguire, D. W. Rhind editors). John Wiley and Sons: New York. 845 – 858.

IFRCRCS (2010) Uganda: Floods and Landslides in Eastern Uganda. Operational report by the International Federation of Red Cross and Red Crescent Societies, 4 March 2010.

International Charter (2000) Charter On Cooperation To Achieve The Coordinated Use Of Space Facilities In The Event Of Natural Or Technological Disasters Rev.3 (25/4/2000). http://www.disasterscharter.org/web/charter/charter (Accessed 26 Aug 2010.)

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Karatunga, Ali M. (2005) Role of Geographic Information Systems (GIS) in disaster management - the Ugandan case. Proceedings of Workshop University Network for Disaster Risk Reduction in Africa, Makerere University, Kampala, Uganda, 12-13 September 2005.

Knapen, A., Kitutu, M.G., Poesen, J., Breugelmans, W., Deckers, J., and Muwanga, A. (2006) Landslides in a densely populated county at the foot of Mount Elgon (Uganda): Characteristics and causal factors. Geomorphology, 73 (2006) pp 149 – 165.

Mafabi, R (2009) Interview with Robert Mafabi, Mbale Coalition Against Poverty, on 10 March 2009.

Mawejje, A (2009) Interview with Andrew Mawejje, Chief Administrative Officer at Mbale District Local Government, on 27 April 2009.

Mitchell, T.C. (2006) Building a disaster resilient future: lessons from participatory research in St Kitts and Montserrat. Ph.D. thesis. London, University College, 57-332

Montoya, L. (2003) Geo-data acquisition through mobile GIS and digital video: an urban disaster management perspective. Environmental Modelling & Software Vol 18 Issue 10 December 2003, pp 869-876. ISSN: 1364-8152

Shrestha, H. L. (2006) Using Global Positioning Systems and Geographic Information Systems in Participatory Mapping of Community Forest in Nepal. The Electronic Journal on Information Systems in Developing Countries (25, 5).

Steklis, H. D., Madry, S., Steklis, N. G., and Faust, N. (2005) GIS Applications for Gorilla Behavior and Habitat Analyses. ArcNews (27,2) ESRI.

UN (2010) UN continues to help Ugandan landslide victims. Press release from the Un News Service, 18 March 2010. http://www.un.org/apps/news/story.asp?NewsID=34127 (Accessed 27 Aug 2010.)

Woof, N (2010) Interview with Nigel Wood, Operations Director with MapAction, on 16 July 2010.

Zook, M; Graham, M; Shelton, T; and Gorman, S (2010) Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake. World Medical & Health Policy: Vol. 2 Issue 2.

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Farthing, D. W., and Ware, J. M. (2011) Mapping Disaster Areas: Past and Future.

Dealing With Disasters Conference, Pontypridd, Nov 2011.

Mapping Disaster Areas: Past and Future

Dave W Farthing, University of Glamorgan, UK

J Mark Ware, University of Glamorgan, UK

Abstract

In developed nations we assume that valuable geographic information (GI) is available for disaster management, but when a disaster strikes a developing nation little GI may be available. In some countries the national mapping agency does not have a comprehensive map; in others it is not up-to-date (Steklis et al, 2005; Karikari et al, 2005). If there is an up-to-date map it may not be accessible, as was the case in 2010 when the offices of the Haitian mapping agency, CNIGS, were destroyed by the earthquake (Richardson, 2010). In the event of a disaster, response agencies will not have the time or manpower to negotiate with the copyright owners of maps. What disaster response agencies need is an up-to-date digital map that is securely stored, has a permissive license and is freely available over the Internet. Ideally it should be possible to download the data in industry-standard format so it can be used in the field in GIS software and on GPS devices (OSM, 2010).

Maintaining a map that covers all developing countries in the world is too big a task for outsiders. Realistically GI has to be recorded by local people, meaning it must not require expensive hardware, software or training.

A system that comes close to meeting these requirements is Google Map Maker (www.google.com/mapmaker): a web-based system that holds a world map and high-quality aerial photogrammetry. Anyone can sign up to edit it; edits are moderated to assure quality. The Map Maker map can be viewed in a regular Web browser, but cannot be downloaded for use in GIS software or a GPS device. Google owns the copyright of the GI. In practice Google has been happy for disaster response agencies to use their map, and has used this for publicity (Parsons, 2011).

Fortunately there is another system that meets more of the requirements criteria: OpenStreetMap (www.openstreetmap.org) is another web-based system that holds a world map, but the GI is licensed under a Creative Commons licence (OSM, 2011a). This means the GI is freely available and can be used in a web browser or downloaded into GIS software and GPS devices (OSM, 2011b). Unfortunately the aerial photogrammetry isn’t consistently in as high resolution as Google’s so contributors need to use a GPS device to record features. This is time-consuming and the cost may be prohibitive for many.

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After Haiti and other recent disasters, both Google and OpenStreetMap data were updated extensively by volunteers around the world (Zook et al, 2010). Although this is laudable, it is our contention that it would be even better if this GI were available in advance of a disaster. The key obstacle is how to persuade people to maintain a digital map of their area for disaster preparedness.

For example, the University of Glamorgan has links with organisations in Mbale, Uganda. In January 2011 the author trained delegates from these organisations in the use of GPS and OpenStreetMap, intending that they would develop the map further. However, those trainees have not made significant use of them since. Conversely Google has been quite successful in persuading local University students to update Map Maker. The next stage in the project will be to identify whether this is due to differences in the participants, the availability of usable aerial photogrammetry or other factors.

Keywords

Disaster preparedness, developing nations, geographic information.

References

Karikari, I., Stillwell, J., Carver, S. (2005) The application of GIS in the lands sector of a developing country: Challenges facing land administrators in Ghana. International Journal of Geographical Information Science, vol 19 iss 3, pp 343 – 362. ISSN 1365-8816.

OSM (2010) OSM Fairfax County Urban Search & Rescue Team Using Garmin Downloads. Wiki article by a search and rescue team member, 21 Jan 2010. Available at: http://wiki.openstreetmap.org/wiki/Talk:WikiProject_Haiti (accessed 2 September 2011)

OSM (2011a) Copyright and licence. Available at http://www.openstreetmap.org/copyright (accessed 1 September 2011).

OSM (2011b) Converting map data between formats. Available at http://wiki.openstreetmap.org/wiki/Converting_map_data_between_formats (accessed 1 September 2011)

Parsons, E. (2011) Google's citizen cartographers map out the world. Google/AFP press release. Availabe at http://www.google.com/hostednews/afp/article/ (accessed 2 September 2011).

Richardson, D. (2010) Rebuilding Geography and GIS Capacity in Haiti. ArcNews (32, 4) ESRI. ISSN 1064-6108

Steklis, H. D., Madry, S., Steklis, N. G., and Faust, N. (2005) GIS Applications for Gorilla Behavior and Habitat Analyses. ArcNews (27,2) ESRI. ISSN 1064-6108

Zook, M., Graham, M., Shelton, T., and Gorman, S. (2010) Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake. World Medical & Health Policy: Vol. 2 Issue 2.

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Farthing, D. W., and Ware, J. M. (2013) Overcoming Obstacles to Mapping for

Disaster Preparedness in East Africa. 4th Conference of the International Society for

Integrated Disaster Risk Management (IDRiM 2013), Newcastle, Sep 2013.

Overcoming Obstacles to Mapping for Disaster Preparedness in East Africa

Dave W Farthing, University of South Wales, UK

J Mark Ware, University of South Wales, UK

Dave W FarthingUniversity of South Wales

Pontypridd, CF37 1DLUK

mailto: [email protected]: 01443 482722

Abstract

This paper reports on a project that is considering ways of mapping East Africa, with special focus on the Mbale region of Uganda. Mainly as a result of climate change and population growth, Mbale is prone to floods and landslides (Knapen et al, 2006; Kitutu et al, 2009; Mugaga, 2012). At the beginning of the study the region was poorly mapped; improving the region’s mapping infrastructure is a key component of disaster preparedness.

A number of case studies have been examined to identify best practice and how to overcome obstacles to mapping. Analogies have been drawn too from psychological decision-making models such as Theory of Planned Behavior (Ajzen, 1991) to enhance the outcomes.

Background

This project is evaluating various case studies for mapping areas in developing countries like Uganda. These approaches include:

Broad-based training course. One-to-one training and field work. NGO capacity building. Local NGO mapping parties.

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NGO ex-pat volunteer. Community mapping (e.g. Map Kibera). Twinned area field work. World Bank collaboration with MapMaker.

Through practical field work and interviews, this project has built on that body of knowledge and tailored it to East Africa. The project will produce a best practice guide that will cover issues to be considered in mapping projects, such as:

Situation: Identifying the specific needs of the target area, the disaster risks, the organisations that work there, and what use they have for geographic information.

Technology: Working with low-grade technology, an unreliable power supply, intermittent Internet access, and unavailability of GPS devices.

Culture: Since there is little tradition of volunteering in East Africa, crowd-sourcing initiatives may have to remunerate participants.

Politics: Predicting what is politically acceptable is difficult. Those responsible for managing hazards might not welcome an initiative that exposes shortfalls.

Resources: There is a high turn-over of staff in organisations; many trainees move on to other jobs within 12 months. Equipment is seldom maintained in the way one is accustomed to in the West.

Although this best practice is tailored for East Africa, in the future it will be analysed to see if it can be generalised for use in other developing countries.

Technology acceptance

There have been numerous studies into the acceptance and diffusion of geographic technologies in the developed world (for example, Campbell & Masser, 1995; Harvey & Tulloch, 2006; Caiaffa et al, 2008). Encouraging local people to map the Mbale region has proved problematic. In some of the case studies, the participants have stated their intention to use the mapping technologies, but subsequently failed to do so.

This project has been looking at various models for technology adoption and diffusion, and considering which is most appropriate for modelling the problems that need to be overcome in order to encourage mapping in developing countries, with a special focus on East Africa. Among the models under consideration is the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al (2003).

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Use behavior

Effort expectancy

Social influence


Figure 1: Unified Theory of Acceptance and Use of Technology, based on Venkatesh et al (2003)

Beliefs and evaluations


Figure 2: Theory of Reasoned Action, based on Fishbein & Ajzen (1975)

Actual behavior

Attitude toward behavior


Subjective norm


As identified above, participants usually stated their intention to use the mapping technologies, but in some case studies they failed actually to do so. Clearly there are obstacles between moving from “Behavioral intention” and “Use behaviour”. Venkatesh et al don’t say much about those obstacles or how to overcome them.

The UTAUT model is based loosely on some psychological decision-making models. For example, the Theory of Reasoned Action (Fishbein & Ajzen, 1975) and the subsequent Theory of Planned Behavior (Ajzen, 1991) both have a number of factors that lead to intention and then actual behaviour.

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Behavioral beliefs

Normative beliefs

Control beliefs


Subjective norm


Intention

Behavior


Fortunately, there have been studies into why there may be a difference between a person’s intention and actual behaviour in these models. For example, Fishbein & Ajzen (2009) developed some useful insights such as:

sequential hurdles, whereby the greater the number of steps to be completed before being able to perform the task, the lower the correlation with intention;

volitional control, if the participants lack the skills or resources to undertake the action;

hypothetical versus real situations and pseudo-inconsistency, o how real-life can alter intention hence changing the outcome (sometimes

positively, sometimes negatively).

It’s interesting to note that the latter model identifies an important feature not seen in the other models: “Perceived behavioral control” can directly influence “Behavior”.

Enhanced model

The project is looking at whether the above models can be enhanced in order to be more generally applicable outside Western culture. For example, in cultures where it is very important to “save face”, there may be a distinction between stated intention to use a technology and actual intention. That is, at the end of a capacity-building exercise, the trainees may feel under pressure to say that a technology is useful and easy-to-use even if that’s not what they are really thinking. Furthermore, it may be advantageous to learn from the Ajzen model (1991) how “Perceived behavioral control” may positively reinforce actual “Behavior”.

Conclusion

By examining both successful and unsuccessful case studies, the project is identifying good practice but especially obstacles and how to overcome them. Using analogies between technology acceptance models and psychological decision-making models has helped uncover further potential obstacles and solutions.

Keywords

Disaster preparedness, developing nations, geographic information, crowd-sourcing, technology acceptance model.

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Figure 3: Theory of Planned Behavior, based on Ajzen (1991)


References

Ajzen, I. (1991) Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, vol 50, 179-211. ISSN: 0749-5978

Caiaffa, E., Cardinali, S., Screpanti, A., & Valpreda, E. (2008) Geographic Information Science: a Step Toward Geo-governance Solutions. 3rd International Conference on Information and Communication Technologies: From Theory to Applications, 2008, (pp. 1 – 4). IEEE. ISBN: 978-1-4244-1751-3

Campbell, H. & Masser, I. (1995) GIS and Organizations. Taylor and Francis, London.Fishbein, M., & Ajzen, I. (1975) Belief, attitude, intention, and behavior: An

introduction to theory and research. Longman. ISBN13: 978-0201020892Fishbein, M., & Ajzen, 1. (2009) Predicting and Changing Behavior: The Reasoned

Action Approach. Taylor & Francis Inc. ISBN-13: 978-0805859249Harvey, F., & Tulloch, D. (2006) Local‐government data sharing: Evaluating the

foundations of spatial data infrastructures. International Journal of Geographical Information Science, vol 20 iss 7, pp 743 – 768. DOI 10.1080/13658810600661607

Kitutu, M.G., Muwanga, A., Poesen, A.M.J., & Deckers, J.A. (2009) Influence of soil properties on landslide occurrences in Bududa district, Eastern Uganda. African Journal of Agricultural Research, vol 4, iss 7, pp 611 – 620. ISSN: 1991-637X

Knapen, A., Kitutu, M.G., Poesen, J., Breugelmans, W., Deckers, J., & Muwanga, A. (2006) Landslides in a densely populated county at the footslopes of Mount Elgon Uganda, Characteristics and causal factors. Geomorphology vol 73 iss 1-2, pp 149 – 165. ISSN: 0169-555X

Mugagga, F., Kakembo, V., & Buyinza, M. (2012). Land use changes on the slopes of Mount Elgon and the implications for the occurrence of landslides. Catena, vol 90, pp 39 – 46. DOI: 10.1016/j.catena.2011.11.004

Venkatesh, V., Morris, M., Davis, G. & Davis, F. (2003) User acceptance of information technology: toward a unified view. MIS Quarterly, Vol. 27. No. 3, September, pp. 425–478. ISSN: 0276-7783

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