Development of a Spatial Data Infrastructure

for the Water Sector of Benin

J. van der Kwast, MSc, PhD Senior Lecturer in Ecohydrological Modelling UNESCO-IHE Institute for Water Education j.vanderkwast@unesco-ihe.org

Introduction: Benin • Capital: Porto-Novo • Largest city: Cotonou • Population (2012):

9,598,787 • Density: 87.1/km2

• Area: 112,662 km2

• Language: French • Government: Multi-

party presidential republic

• President: Yayi Boni

Introduction

• Benin has adequate water resources for food production, drinking water and nature conservation

• Nevertheless, unsustainable agricultural and other land-use practices and water use, combined with the anticipated effects of climate change and population growth threaten the water resources

Introduction

• Benin is already confronted with large environmental problems such as seasonal water shortages, (urban) pollution, salinization and increasing damage due to flooding

Driver: Sea level Rise

Driver: Population Growth

Driver: Economic Growth

1. Stratégie et gouvernance - 2 Driving Forces and Vulnerability

Strategy and Governance

GIS and IWRM

IWRM

Health

Drinking water

Sanitation

Environment

Economy

Agriculture

Climate Change

Population Growth

Institut National de l’Eau (INE)

• Nuffic NICHE-BEN-167 project: Establishment of a Water Institute to deal

more effectively with water and sanitation problems

Context of the feasibility study • Sharing of data is necessary for Integrated

Water Resources Management • Need for a system for sharing data

– The current systems are autonomous, with short term objectives, no interoperability among the systems

– Absence of a culture that enables communication and sharing of data

– No tool has been established to enable exchange of data

ASECNA

BDI HAB

BDI

AKVO

Base de données

Plan Delta IGN

Context of the feasibility study

SNIEAU

• Système National d’Information sur l’Eau (SNIEAU, National Water Information System)

• http://benin.snieau.bj • Objective:

Establish a national system for sharing reliable information on water facilitating decision making for integrated water resources management and sustainable development

Agriculture

Entrepreneurs/ researchers

Added value, available for the water sector Development of apps and services

Decision Support Systems

SNIEAU

Health

Drinking water

Sanitation

Economy

…

Vision of SNIEAU

By 2016 Benin has a unifying, reliable and accessible system for sharing information and data for improved management of water and related resources through an exchange network between the institutions in order to improve intersectoral communication for a synergy of actions towards a harmonious and sustainable development

Organisation • SNIEAU Working Group

– Stakeholders: ministries and other institutions that produce and/or use data for the water sector

– 2 representatives per organisation – Involvement in the design and monitoring of the

development of SNIEAU • Secretariat

– DGEau • Technological responsibility and coordination

– INE • Technical assistance and education

– PPEAII – NICHE

Milestones in the realisation of SNIEAU

February 6 2014: Presentation of the project

June 11 2014: Start of the Working Group

October 10 2014: Finalisation of the inception phase

January 27 2015: Finalisation of the feasibility study

February 4 2015: Presentation of the feasibility study

Justification of choice of technology

Where is the data?

0

20

40

60

80

100

Papier DVD/CD Disques durs externes Serveur

Nom

bre

d'in

stitu

tions

(en

%)

Where is it archived?

0

20

40

60

80

100

Papier DVD/CD Disques durs externes Serveur

Nom

bre

d'in

stitu

tions

(en

%)

How is data shared?

• Paper: 82%

• Digitally (USB stick, DVD/CD, e-mail…): 94%

Main challenges

• Instability of the power supply • Instability of the internet connection • Most data are still shared on paper

Technical solutions for SNIEAU

• Centralised architecture • External hosting • Open source software

Centralised architecture

Institute A

Institute B

Institute C

SNIEAU

Institut A

Institut B

Institut C

SNIEAU

Distributed architecture

Institut A

Institut B

Institut C

SNIEAU

Hybrid architecture

Architecture Type Avantages Désavantages

Centralised

Less investments in hardware and software Less cost for securing power and internet supply Secure backups

Requires great confidence and a clear legal framework Difficulty of defining a common policy

Distributed

True networking, where everyone is fully in control of his data Policy developed in each institute

Large investment cost for each institution

Hybrid

Combines the advantages of the other two solutions Creates redundancy

Combines the disadvantages of the other two solutions Large investment cost

Advantages and disadvantages of different architectures

Hosting type Advantages Disadvantages

Internal

Confidentiallity better preserved Large investment in hardware, software, human resources, skills Need to secure the power supply and internet connection

External Ensuring large up-time of the service (e.g. >95%) Minimized costs

The privacy policy is not always clear

Choice for hosting

Hosting

• Installation and maintenance: Upande Ltd., Nairobi, Kenya (http://www.upande.com)

• Amazone cloud • 5 years maintenance contract

Choice for open source software

• Open source software often provide better interoperability between internal and external components

• Open source software often use international standards • Proprietary tools impose limits to the user; it is difficult to

make improvements, complicated to change supplier (lock in) • Open source software is improved continuously thanks to the

participation of the user community • Quick implementation of new developments (at the

forefront of technology) • Opportunity for innovation for Benin

GeoNode

Source: http://geonode.readthedocs.org/en/latest/reference/architecture.html

Which data?

Fundamental data: Fundamental data is a dataset for which

several government agencies, regional groups and/or industry groups require a comparable national coverage in order to achieve their corporate objectives and responsibilities.

Structure responsable

Spécificité thème (couches, types, sources de données)

Géométrie de la couche

Intitulé du thème ou

application

Demande utilisateur ou

total coef.

Application matrix

Application matrix • Analysis of the actual situation based on an inventory of

products and services • Evaluation of the use of frequency layers • Identify priorities for the update • Priorities for quality improvement

• Add the desired situation

• Set priorities for the acquisition of new data

• Impacts improvements on the underlying information

systems

• Gives arguments for financial support

Why is sharing so difficult? Individuals, institution and governments often see risks in

sharing data (UNECA, The SDI Handbook for Africa):

– a priori suspicion of the quality of third party data is common.

– a priori presumption that the institutions’ own data

(generally deemed of high quality by the latter) may be “wrongly” used if shared with a third party, or even that ownership thereof may be lost.

– fear that other users discover the poor quality of their data by sharing them.

Metadata

Metadata should clarify terms of use of data The user should be able to determine if the data is suitable for his/her purpose

SDI Data Policy

• A sound data policy should look carefully at ways to remove the potential risks so that the data producers are happy and confident in sharing their data

• Commitment of all contributing stakeholders is needed

Need for a policy

• What data types/themes will be stored in the system?

• Who will upload the (meta)data? • When will the (meta)data be uploaded? • Who is responsible for the (meta)data? • Who will check new data added to the SDI? • Who can be addressed for issues with the

SDI?

Business models

“Value of information is the amount a decision maker would be willing to pay for information prior to making a decision”

Business models

• Publicly funded data are a public good, produced in the public interest and thus should be freely available to the maximum extent possible.

Costs of selling data Organisations can overestimate the return on

investment of selling the data they own (including making it available through a freemium business model) if they underestimate the costs that can be involved, such as: – legal costs of creating and enforcing restrictive

licences – development costs of restricting access and use

of data – administrative costs of issuing licences – sales and marketing costs to promote the data

Strategic risks of selling data

• Low willingness to pay (like with music, movies, etc.). People are becoming less prepared to pay for digital products that can easily be copied and shared with others

• Competitors or communities might also be able to undermine your data business by releasing their data as open data.

Organisation of SNIEAU • 2 administrators:

– User management – Management of the platform – Communication of anomalies

• 1 steering committee – 1 representative of each organisation – Quarterly meetings – Tasks: decisions, sustainability, initiative to keep SNIEAU

active – Yearly reporting with conclusions and recommendations

• Team for technical assistance

Actions for the near future

The infrastructure has been put in place For you to ride now!

SNIEAU: an infrastructure

Actions for the near future

At the level of each organisation: • Participate in the SNIEAU network • Establish an organisational framework for

data management • Submit your data • Improve your information systems • Discuss your needs

Actions for the near future

For SNIEAU • Establish the organisational structure • Take actions needed for further

development of SNIEAU • Plan and organise trainings • Become the source of information for large

projects, e.g. Plan Delta Ouémé