Predictive Geodemographics
Lead Research Organisation:
University of Leeds
Department Name: Sch of Geography
Abstract
One of the many criticisms of geodemographics is that they are static - typically build using census data, they are out of date as soon as they are released, a snap shot of what the population was like 3 years previously (this is, on average, the time taken for census agencies to release census data). The next step is to make geodemographics dynamic - what do people truly look like now, and what changes in their characteristics or circumstances will move them into another segment? This is important for timely communication, in for example, marketing activities. However if long-term customer value is considered, it is what the consumer will look like three or five years hence that becomes important. The ability to identify the association of individuals with a defined social trajectory, a socio-economic path through life, is a powerful concept. Using new forms of data, is it possible to build a geodemographic system for the UK, that provides a predictive path through a CAMEO-like hierarchy, to enable our customers to evaluate consumers' lifetime value, and also serve up the right mix of timely information for consumers to make better decisions?
People |
ORCID iD |
Alexis Comber (Primary Supervisor) | |
Jennie Gray (Student) |
Publications
Gray J
(2019)
Exploring social dynamics: predictive geodemographics
Gray, J
(2019)
Exploring social dynamics: predictive geodemographics.
Gray J
(2021)
Extending Geodemographics Using Data Primitives: A Review and a Methodological Proposal
in ISPRS International Journal of Geo-Information
Gray J
(2023)
Identifying Neighbourhood Change Using a Data Primitive Approach: the Example of Gentrification
in Applied Spatial Analysis and Policy
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ES/P000401/1 | 30/09/2017 | 29/09/2024 | |||
1944002 | Studentship | ES/P000401/1 | 30/09/2017 | 30/03/2022 | Jennie Gray |
Description | This research has developed a new method for identifying dynamic neighbourhood changes over a ten year period via neighbourhood processes, namely through the lens of gentrification. The method is able to capture cycles of gentrification, identifying the year in which the changes started, the year associated with the greatest amount of change (the peak), and the year in which these changes ended. This research has analysed how these cycles manifest throughout space, and has implemented predictive models to predict the spatial and temporal extent of gentrification for England. This approach can be used for other investigating other neighbourhood processes, like urban decline, and suburbanisation. |
Exploitation Route | The data primitive approach can be explored in greater depth, particularly with change vector analysis. They have great potential for uncovering the intricate relationships between neighbourhood processes, alongside the complex periodicities of specific neighbourhood processes. This will help to uncover insights into the neighbourhood processes under investigation, how they operate through space and time, which in turn could be greatly beneficial for public policy and planners. |
Sectors | Communities and Social Services/Policy |
Description | AGILE 2019 Grant |
Amount | £560 (GBP) |
Organisation | Environmental Systems Research Institute |
Sector | Private |
Country | United States |
Start | 03/2019 |
End | 06/2019 |
Description | GSIRUK 2019 Early Career Scholarship |
Amount | £150 (GBP) |
Organisation | GISRUK |
Sector | Charity/Non Profit |
Start | 03/2019 |
End | 04/2019 |
Description | Data Supplier |
Organisation | EDGE Analytics Limited |
Country | United Kingdom |
Sector | Private |
PI Contribution | Acknowledgement of Edge Analytics (EA) and their data in any of my outputs from the data supplied from EA. |
Collaborator Contribution | Three requested datasets, and access to a piece of commercial software and its methodology. |
Impact | None yet. |
Start Year | 2019 |
Description | Official Data Supplier |
Organisation | Callcredit Limited |
Country | United Kingdom |
Sector | Private |
PI Contribution | I conducted what should have been a two week internship at callcredit, but it ended up being around 4-6 weeks. This was for the internship module in the first year, but I did so for a project proposed by callcredit. |
Collaborator Contribution | None. They were my official PhD partner, but have made no contributions to my project, they have supplied zero data, leading me to seek elsewhere (EA). |
Impact | Officially still active, but zero communication, zero data, thus zero outcomes from the partnership. |
Start Year | 2017 |