Otley, A, Morris, M orcid.org/0000-0002-9325-619X, Newing, A orcid.org/0000-0002-3222-6640 et al. (1 more author) (2021) Local and Application-Specific Geodemographics for Data-Led Urban Decision Making. Sustainability, 13 (9). 4873. ISSN 2071-1050
Abstract
This work seeks to introduce improvements to the traditional variable selection procedures employed in the development of geodemographic classifications. It presents a proposal for shifting from a traditional approach for generating general-purpose one-size-fits-all geodemographic classifications to application-specific classifications. This proposal addresses the recent scepticism towards the utility of general-purpose applications by employing supervised machine learning techniques in order to identify contextually relevant input variables from which to develop geodemographic classifications with increased discriminatory power. A framework introducing such techniques in the variable selection phase of geodemographic classification development is presented via a practical use-case that is focused on generating a geodemographic classification with an increased capacity for discriminating the propensity for Library use in the UK city of Leeds. Two local classifications are generated for the city, one a general-purpose classification, and the other, an application-specific classification incorporating supervised Feature Selection methods in the selection of input variables. The discriminatory power of each classification is evaluated and compared, with the result successfully demonstrating the capacity for the application-specific approach to generate a more contextually relevant result, and thus underpins increasingly targeted public policy decision making, particularly in the context of urban planning.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | geodemographic classifications; feature selection; recursive feature elimination; urban planning; libraries |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 May 2021 10:34 |
Last Modified: | 11 May 2021 10:34 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/su13094873 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173796 |