Publication type
Journal Article
Authors
Publication date
August 31, 2017
Summary:
Geodemographics is a spatially explicit classification of socio-economic data, which can be used to describe and analyse individuals by where they live. Geodemographic information is used by the public sector for planning and resource allocation but it also has considerable use within commercial sector applications. Early geodemographic systems, such as the UK’s ACORN (A Classification of Residential Neighbourhoods), used only area-based census data, but more recent systems have added supplementary layers of information, e.g. credit details and survey data, to provide better discrimination between classes. Although much more data has now become available, geodemographic systems are still fundamentally built from area-based census information. This is partly because privacy laws require release of census data at an aggregate level but mostly because much of the research remains proprietary. Household level classifications do exist but they are often based on regressions between area and household data sets. This paper presents a different approach for creating a geodemographic classification at the individual level using only census data. A generic framework is presented, which classifies data from the UK Census Small Area Microdata and then allocates the resulting clusters to a synthetic population created via microsimulation. The framework is then applied to the creation of an individual-based system for the city of Leeds, demonstrated using data from the 2001 census, and is further validated using individual and household survey data from the British Household Panel Survey.
Published in
Applied Spatial Analysis and Policy
DOI
https://doi.org/10.1007/s12061-017-9233-7
ISSN
1874463
Subjects
Notes
Online Early
Open Access
© The Author(s) 2017
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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