Research Challenges for Real Time Decision Makingin Public Sector Service Delivery:

Alex Singleton pontificates about geodemographics for about an hour...

Introduction Why Classification? An Evolving Research Themes

Commercial○ Extension Strategies – Rebranding, Diversification

Academic○ Consumption○ Critique & Production○ Geodemographics 2.0

Key Challenges Geodemographics 2.0

Relevance in a Depreciating Data EconomyComplexity – Time, Space and ScaleGeocomputation Challenges

Why Classification? The world is complex Simplify Reality Improve our Understanding

Help make decisionsHelp us to navigate the world

Evolving Research Themes

Expertise

Critique / KT ?Production Consumption

Evolving Research Themes Missing Knowledge Transfer? 2 Sides to a coin - Different Drivers

Better Explanation Increased Rigour Open / Reproducible Cumulative

Knowledge Usability

Market Share Sector Region

Turnover & Profit R&D Usability Better Explanation

What are the....

An Evolving Research Agenda Commercial Innovations

New Markets & Diversification

New IndustriesAutomotiveFinancialGroceryPublic SectorHealth

New SegmentsDaytimeNamesIncomeStreet ValuesShareholder ActivityUnemploymentIndividuals (Scale)

RegionGlobalLondonScotlandNorthern Ireland

An Evolving Research Agenda Commercial Innovations – Extension Strategies

Rebranding

Time

MarketShare

Ori

gina

l

Ext

ensi

on

What are the....

An Evolving Research Agenda Academic – 10+ Years Critique & Production

Time

Ground TruthGoss (1995a, b)

Diversity of DiversityVoas and Williamson (2001)

Critical TheoryConsumption ReaderGoss (2003)

Methodology &Application

Fuzzy GeodemographicsFeng and Flowerdew (1998)

Fuzzy GeodemographicsSee and Openshaw (2001)

Production

Bespoke GeodemographicsLongley et al (2008) Singleton and Longley (2009a)

Public ConsultationLongley and Singleton (2008)

Live Classification UpdatesSingleton and Longley (2009b)

Description V ModellingHarris et al (2007)

2001 CensusGBProfiles

(Openshaw)

1991 CensusOAC

(Vickers)LSOAC(ONS)

London OACHealth OAC

Education OACE-Society

Critical Theory Arguments & Response / Lessons

Monitor, Model and Control○ Consumers are marketed identities○ Assumes consumers are not rational

Strategy Metaphor – “Simplify and Mystify” technology – purpose gaining market share○ Relates to products essentially using the same

data – just a different badge. ○ True, there is little to choose between the products○ Argument demonstrates further contempt for

consumers!Cluster Analysis – Aggregation Errors

○ True, however doesn’t entirely mitigate the valueAssumes Social identity perfectly correlates with

consumption○ Good point – bespoke classifications!

Methodology and Application Fuzzy - Discrete V Continuous Representation

Good idea – communication a problem Sensitivity to local conditions

True, compress multiple space time dimensions into core components

Modelling versus Description Yes!

Application Specific (Bespoke) Yes, but how do these become flexible and embedded for

general use. Consumer Feedback – Challenge Assignmets

Sounds good! Building Feedback into Classification

How are these verified?

What are the....

Everything 2.0

Critique / KT ?

Consumption

Everything 2.0

= Accumulation of Significant External Changes

e.g.•Geoweb 2.0•Web 2.0•Wikisation of GIS•Social Networks•Crowd Sourcing•Neogeography

Challenges for Everything 2.0 Changing relationships with space & time (new channels)Rising Importance of Virtual Places (Social media, social

networks)○ Do individuals interact in real places in the same way as virtual

places? How do we reach them? Changing responses to information

Consumption (Encarta) V Production (e.g. Wiki)Static V Fluid

Clever ConsumersInformation savvy - B2B & Public

Data Economy ParadoxData Economy has never been stronger!Data Economy has never been more restricted?Lead datasets (electoral roll) increasingly marginalised

What is my response....

Academic Role in Geodemographics 2.0 Commercial providers will operate business as

usual until something better comes along. Why rock the boat?E.g. Legacy of K-Means - RIP

Little engagement with any critiqueSome evidence @ CACI (e.g. Health Acorn)

IMPORTANTPARTNERSHIP & KNOWLEDGE TRANSFER!

Geodemographics 2.0

Bespoke Requests

Reality RealtimeMeasurement

Geocomputation Challenges Data Variable Relationships & Verification Spatio-Temporal Sensitivity Processing & Analysis Dissemination

DataIndividual Level Data

○ Confidentiality & Disclosure Control○ Multiple Sources – Data Protection

Area Level Data○ Managing different scales safely

Disparate Data○ Standards – XML○ Secure - Encryption

Speed of Transfers

Relationships & Verification

Assuring Integrity Validity○ Ontological and Meta Information

E.g. How old is the data?Which data is behavioural?Which data is attitudinal?What is the coverage?

- Time, Place

Interpreting user requests○ Which variables?○ Which weight?

Spatial WeightsTemporal Weights

Spatio-Temporal Sensitivity Accounting for local variation

Spatial Interaction Models? Geographically Weighted Cluster Analysis Temporally Weighted Cluster Analysis

Normalisation which is sensitive, without detrimentally effected by outliers

Processing Which clustering algorithms?

Not K-Means Not stable, can’t efficiently optimise – why do we use

them? Maybe Partitioning Around Medoids (PAM) Maybe Intelligent Genetic Algorithms Maybe something entirely different

Building for a single purpose Multidimensional Scaling Principle Components

Processing which doesn’t collapse the geographic dimension of the data

Processing which is FAST! Safe to use – Testing!

Dissemination Live Classifications require...

Live Pen portraits○ Build automated descriptions for clusters

based on appended dataLive Maps

○ Automated Google Map GenerationOvercome tile processing limitations

Live Images○ Automated production of appropriate pictures

and collage imagery

Geodemographics 2.0

Geodemographics 2.0

ReferencesFeng Z and Flowerdew R, 1998, Fuzzy geodemographics: a contribution from fuzzy clustering method. In Carver S (Ed.),

Innovations in GIS 5. London: Taylor and Francis, pp.119—127

Goss, J. 1995a: Marketing the new marketing: the strategic discourse of geodemographic information systems. In Pickles, J., editor, Ground truth, New York: Guilford Press , 130-170.

Goss, J. 1995b: ‘We know where you are and we know where you live’: the instrumental rationality of geodemo-graphic information systems . Economic Geography 71, 171-198

Goss, J. (2003) ‘We know who you are and where you live’ The Instrumental Rationality of Geodemographic Information Systems. In David B. Clarke, Marcus A. Doel, Kate M. L. Housiaux The Consumption Reader. Routledge.

Harris R, Johnston R, Burgess S, 2007, Neighborhoods, ethnicity and school choice: developing a statistical framework for geodemographic analysis, Population Research and Policy Review, 26, 553-579.

Longley, P.A., Webber, R., Li, C. (2008) The UK geography of the E-Society: a national classification. Environment and Planning A. In Press.

See, L. and Openshaw, S. 2001. Fuzzy geodemographic targeting. In: Regional Science in Business, Clarke, G. and Madden, M. (eds). Springer, Berlin. pp.269-282.

Singleton, A., Longley, P.A. (2009a) Creating Open Source Geodemographics - Refining a National Classification of Census Output Areas for Applications in Higher Education. In Press Papers in Regional Science.

Singleton, A., Longley, P.A. (2009b) As yet untitled Area paper – Relates to updating classification.

Longley, P., Singleton, A.D. (2008) Classification through Consultation: public views of the geography of the e-Society. International Journal of Geographical Information Science. In Press.

Voas, D. and Williamson, P. (2001), The diversity of diversity: a critique of geodemographic classification, Area, 33 (1), 63-76.