Extending Geodemographics Using Data Primitives

Geodemographics, a method of grouping areas based on similar characteristics, has been a pivotal tool in various sectors, from transport and marketing to health and higher education. However, with the urban environment constantly evolving, there's a pressing need to refine how we classify and understand neighbourhoods, particularly when exploring them over time. A 2021 paper by Gray and others introduces a novel approach using data primitives to capture the dynamic nature of neighbourhoods.

Despite their utility in a range of sectors, geodemographic classifications have their drawbacks. They are often temporally static, making it challenging to analyse neighbourhood dynamics. Moreover, the hard allocation of areas to a single cluster may overlook subtle yet significant changes in neighbourhood composition when exploring temporal changes.

The promise of data primitives:

To address these limitations, the researchers introduce a "data primitives" approach, enhanced by change vector analysis. Data primitives are fundamental measurements that capture processes of interest. This approach offers a more nuanced perspective, allowing for the analysis of emergent social processes that might be overlooked in traditional classifications.?

Change vectors represent the direction and magnitude of change for each data primitive over multiple time periods. By comparing the states of neighbourhoods over different times, researchers can discern social dynamics and predict future neighbourhood states.

This approach enhances our understanding of complex neighbourhood processes, ensuring a comprehensive and adaptable approach to geodemographic research.

Case study - Nottingham:

A case study illustrates the data primitive approach at the LSOA level for Nottingham. Data primitives covering population density, ethnicity, housing affordability, average house price, disability benefits recipients, neighbourhood churn, and population movements were analysed. The key findings include:

• Population density: Areas with increasing population density correlated with rising housing prices and a decrease in housing affordability, indicative of gentrification.
• Ethnicity distribution: Shifts in ethnicity distribution highlighted areas experiencing significant demographic changes, potentially due to migration or socio-economic factors.
• Housing affordability and prices: Analysis revealed that neighbourhoods with lower housing affordability were also experiencing higher rates of population churn, suggesting a link between economic pressures and resident mobility.
• Disability benefits recipients: This metric provided insights into the socio-economic health of neighbourhoods, with higher concentrations indicating areas that might require targeted policy interventions.
• Neighbourhood churn: High rates of population movement were found in certain areas, correlating with significant changes in the neighbourhood composition over time.

This comprehensive approach allowed for a detailed understanding of the social dynamics at play in Nottingham's neighbourhoods, offering a clearer picture of how various factors interact over time.

Significance of extending geodemographics using data primitives:

Understanding the intricate dynamics of neighbourhoods is crucial for various sectors. For businesses, it can guide marketing strategies, ensuring they target the right audience. For policymakers, it can shed light on areas needing intervention, from healthcare to education. Moreover, with urban environments rapidly changing, having a dynamic tool that captures these shifts, and capturing processes like gentrification, can be invaluable for economic planning and societal well-being.

Publication: Gray, J., Buckner, L., & Comber, A. (2021). Extending geodemographics using data primitives: A review and a methodological proposal.?ISPRS International Journal of Geo-Information,?10(6), 386. DOI: https://doi.org/10.3390/ijgi10060386

CDRC Data:?

Modelled Ethnicity Proportions - https://data.cdrc.ac.uk/dataset/cdrc-modelled-ethnicity-proportions-lsoa-geography?

Residential Mobility Index - https://data.cdrc.ac.uk/dataset/cdrc-residential-mobility-index?

People:

https://www.dhirubhai.net/in/jennie-gray-phd-039b0b88/?

https://www.dhirubhai.net/in/alexis-lex-comber-3283106/?