Generating a Leeds geodemographic classification: applications in policy, commerce and health
Supervisors: Dr Michelle Morris, Dr Andy Newing, Professor Mark Birkin http://www.geog.leeds.ac.uk/study/phd/centre-for-spatial-analysis-and-policy/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.
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
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.
Minimum of 2.1 Honours Degree or MSc in Geography or a related quantitative discipline.
Must meet the English language requirements detailed here:
This project is a fully-funded ESRC Collaborative studentship covering full UK fees and a tax-free maintenance stipend of approximately £14,296 per year for 3 years as well as some additional research expenses.
Deadline for applications extended to 8 April 2016