- Email: email@example.com
- Thesis title: Generating a Leeds Geodemographic Classification: Applications in Policy, Commerce and Health
- Supervisor: Dr Michelle Morris, Dr Andy Newing, Professor Mark Birkin
Amanda is a PhD student with a keen interest in data science and its application for better informing decision making processes, particularly in social and spatial contexts.
Geodemographic Classification, Multivariate Analysis, Urban Analysis
Geodemographic classifications in the UK are commonly generated using national level data, predominantly from the decadal census, or generalised from national sample surveys. 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. Moreover, in an age of ‘the big data revolution’ increasing volumes of real time data 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.
To date, there are no examples of geodemographic classifications accounting for attitudinal and transactional behaviours built at a sub-national level. This project seeks to link academic, commercial and local authority datasets related to the city of Leeds to demonstrate the potential benefits of city specific geodemographics.
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 and the application of the most appropriate and forward-thinking methods to generate a robust segmentation for wider application.
(2) Apply this classification to a range of case study applications to assess potential benefits and uplift relative to generic geodemographic systems. Case studies to be applied to a range of topical research areas for example; health, fuel poverty, employment opportunities, direct marketing and retail demand.
Leeds City Council, Trans Union (Formerly Call Credit)
Cluster & research affiliations
Awarded best poster presentation at the 26th Annual GIScience Research UK (GISRUK) conference, University of Leicester, 17th-20th April 2018
- MSc Geographical Information Systems, University of Leeds
- BSc Mathematics (Ind), University of Leeds