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PhD Opportunity – Generating a Leeds geodemographic classification: applications in policy, commerce and health


Geodemographic classifications are a useful tool to profile a population by segmentation based on demographic and household characteristics tied to small area geographic units, such as neighbourhoods. They are widely used to target and evaluate interventions, policy and service provision at a local level, be-it for commercial, health or societal gain. This project seeks to combine big data from academic, commercial and local authority sources in order to generate a city specific geodemographic segmentation system. Such a system would enable users in the academic, commercial and policy sectors to target research, services and interventions in response to city specific geodemographic characteristics and potentially pave the way for a new generation of localised city or sector specific geographic segmentations.

Background and Rationale

Geodemographic classifications are commonly generated using national level data, predominantly from the decadal UK census, or generalised from national sample surveys. A number of open source, commercial and targeted small area geodemographic classifications are available, for example the Output Area Classification (OAC) (Office for National Statistics), Mosaic (Experian), and ‘Green and Ethical Segmentation’ (Callcredit) respectively. The former captures predominantly demographic characteristics and is built purely from census data whilst the latter contain a richer set of behavioural and attitudinal insights from survey data (e.g. ONS Living Costs and Food Survey [LCF]), and commercial sources (e.g.Callcredit’s ‘Define’ consumer database).

Given that regions and cities differ from one another in structure, governance, social norms and behaviours there are societal, policy and commercial needs for city specific geodemographics. The bespoke ‘London OAC’ (LOAC) built for the Greater London Authority recognises that Greater London has a unique demographic composition not fully captured by national geodemographic classifications. Whilst demonstrating the needs and potential utility of city specific geodemographic classifications, the LOAC is derived purely from area based census variables. To date there are no examples of commercial geodemographic classifications, accounting for attitudinal and transactional behaviours, built at a sub-national level.

In an age of ‘the big data revolution’ increasing volumes of real time data (consumer transaction data, smart ticketing and social media feeds) are emerging which could transform city-specific geodemographic segmentation, providing more granular classifications based on local demographic, compositional, behavioural and attitudinal insights available from local level data sources. We seek to link academic, commercial (marketing, transactional and credit data) and local authority (behavioural and transactional) datasets related to the city of Leeds in order to demonstrate the potential benefits of city specific geodemographics.

Through the Leeds Institute for Data Analytics (LIDA) and the Consumer Data Research Centre (CDRC) strong partnerships exist with Leeds City Council and with Callcredit Information Group. As a major city leading in data analytics, these partnerships facilitate access to wealth of local government and commercial data specific to the Leeds population, in addition to publically available neighbourhood statistics.

Aims and Approach

Aim: Create and apply a custom built geodemographic classification for Leeds at a household and small area level.

Objectives and approach:

  1. Generate a data driven Leeds specific geodemographic classification. This will involve integration of data sources from Leeds City Council, LIDA, Callcredit and open source data (Appendix 1 outlines the data sources that will be made available from Callcredit and Leeds City Council). This will utilise expertise from Callcredit and LIDA to determine the most appropriate and forward thinking methods to generate a robust segmentation for wider application.
  2. Apply this classification to a number of case study applications of relevance to the project partners in order to assess potential benefits and uplift relative to generic geodemographic systems. Application depth will vary to meet the project partner needs, be them, academic, societal or commercial. Case studies may be applied to a range of topical research areas for example; health, fuel poverty, employment opportunities, direct marketing and retail demand.

While this project is for application in Leeds the approach will be reproducible and applicable elsewhere at a variety of spatial scales, where equivalent data and infrastructure exists.

Utilising the Integrated Research Campus at the University of Leeds this project will benefit from secure infrastructure for data storage, handling and processing enabling the aims of this project to be met without compromised data security.

Impact of Research

The research will deliver applied evidence that city specific geodemographic segmentation, fuelled by data sharing between academic, commercial and local authority sources, can provide considerable benefits relative to existing geodemographic systems. This could in turn pave the way for a new generation of commercially exploitable city specific geodemographics.

This project is thus well positioned to generate impact at a variety of levels:

  • Academic – New approach for geodemographic classification which is both timely and spatially specific, and which combines novel and traditional big data sources.
  • Societal – The application provides opportunity to change lives in Leeds through improved targeting and evaluation of services, policy and interventions.
  • Commercial – Generating commercial insight for Callcredit in city specific geodemographics and in working with new data from local government and retail partners (through CDRC).

The output will be a new tool generated for use by researchers and also the intelligence group at Leeds City Council. The tool will be used to support specific case studies which themselves will generate specific impact within their application areas and with reference to Leeds with potential future wider application.

Strengthening links between the University, Leeds City Council and Callcredit information group will contribute to Leeds’ role as a hub for Data Analytics and be a model for application elsewhere.


The student will benefit from world leading research training and support available through CDRC (one of four ESRC Business and Local Government data centres) and LIDA, which offers state of the art facilities for – and expertise in – data analytics, and the opportunity to work collaboratively with interdisciplinary specialists from computer sciences, mathematics and medical research fields.

The student will also be part of the Centre for Spatial Analysis and Policy (CSAP) in the School of Geography, an internationally recognised research group which undertakes applied research and methodological developments in geodemographics and segmentation, alongside application areas including health, crime, retail and population dynamics.

Partners and Collaborators

Leeds City Council (Malachi Rangecroft, Intelligence Manager)

Leeds City Council are major users of geodemographic systems as a tool to profile Leeds citizens and households in order to target and evaluate local policy, interventions and service delivery. They have a strategic remit to provide services which are responsive to the needs of the local community and rely on accurate household and area based geodemographics to support these functions. With increasing volumes of big data collected in relation to Leeds residents, Leeds City Council are keen to work with LIDA and Callcredit to develop a novel geodemographic classification to support household level targeted interventions.

Callcredit information group (Andy Peloe, Concept manager and Libby Plowman, Data Products Manager)

Callcredit are one of the leading providers of commercial geodemographic systems including the market leading CAMEO classification which is available for 40 countries worldwide. In the UK, CAMEO combines census, lifestyle, survey and transactional datasets and is available at the individual, household and postcode level. At the forefront of developing targeted and bespoke classifications, Leeds based Callcredit are keen to partner with the University and Leeds City Council to develop the first city specific classification by combining data, expertise and insight from all partners.

Appendix 1

Data provided by CallCredit:

Individual level



Estimated age

Household composition and size

All CAMEO area-level products (e.g. CAMEO UK, Income, Financial, Investor, Property, Unemployment)


Social class and estimated household income

Household expenditure estimates

Head of household /

decision maker

Property types, sizes and values

All CAMEO area-level products (e.g. CAMEO UK, Income, Financial, Investor, Property, Unemployment)

Multiple behavioural models (technology, financial, ethical attitudes)

CAMEO Household level products (e.g. CAMEO UK, Lifestyle)

CAMEO Individual level products (e.g. CAMEO UK)

Data provided by Leeds City Council:

I. The full range of 2011 Census data at Output Area geography (and aggregations thereof) ;

II. Contact centre data down to household (where a member of the public has

contacted the council to raise a complaint or ask for help etc.); III. Benefits data down to household (details of benefit type);

IV. Council tax data at postcode level;

V. lndices of Deprivation (2015) at Lower Super Output Area (LSOA); VI. Crime data derived from data.police.uk at street level;

VII. Health data such as mortality, obesity, health conditions at Middle Super

Output Area (MSOA) level;

VIII. Wide range of education including attendance, non-attendance, attainment, IX. school population, special needs – at various geographies (usually LSOA); X. Housing data relating to social housing at household level;

XI. Breeze data (children’s scheme) at postcode level; XII. Library attendance data at postcode level;

XIII. Environmental data (such as Energy Performance Certificates at household level);

XIV. Multiple GIS layers (such as boundary information and map layers);