Mapping the geodemographics of digital inequality in Great Britain: An integration of machine learning into small area estimation

Singleton, Alex, Alexiou, Alexandros ORCID: 0000-0003-3533-3238 and Savani, Rahul ORCID: 0000-0003-1262-7831 (2020) Mapping the geodemographics of digital inequality in Great Britain: An integration of machine learning into small area estimation Computers, Environment and Urban Systems, 82. p. 101486. ISSN 0198-9715, 1873-7587

Access the full-text of this item by clicking on the Open Access link.

Abstract

Geographic variation in digital inequality manifests as a result of a range of demographic, attitudinal, behavioural and locational factors. To better understand this multidimensional geography, our paper develops a new geodemographic classification for the spatial extent of Great Britain. In this model, we integrate a range of new small area measures that are drawn from multiple new forms of data including consumer purchasing data, survey and open data sources. Our analytical approach innovatively provides an integration of machine learning into a small-area estimation technique to obtain Lower Super Output Area / Data Zone estimates of Internet use, alongside a range of online engagement and consumption measures. Following the collation of a range of input measures, we implemented a more standard geodemographic framework that utilises the unsupervised clustering algorithm k-means to produce a map of the multidimensional characteristics of digital inequality for Great Britain; creating the Internet User Classification (IUC). Our outputs provide a new and nuanced understanding of the contemporary salient characteristics of digital inequality in Great Britain, which we evaluate both internally and externally within the context of preparations for the 2021 UK Census of the Population, exploring the geodemographic patterns of Census test response rates and the prevalence to complete the survey online. Our innovative work illustrates the strength of a geodemographic approach in mapping spatial patterns of digital inequality, and through the presented application concerning Census response rates and characteristics we demonstrate how the IUC can be operationalised within such settings for local intervention or benchmarking.

Item Type:	Article
Uncontrolled Keywords:	33 Built Environment and Design, 40 Engineering, 46 Information and Computing Sciences, 4601 Applied Computing, 4013 Geomatic Engineering, 3304 Urban and Regional Planning, Machine Learning and Artificial Intelligence, 1.2 Psychological and socioeconomic processes, Generic health relevance, 10 Reduced Inequalities
Depositing User:	Symplectic Admin
Date Deposited:	29 Apr 2020 10:22
Last Modified:	01 Mar 2026 11:43
DOI:	10.1016/j.compenvurbsys.2020.101486
Open Access URL:	https://doi.org/10.1016/j.compenvurbsys.2020.10148...
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3083740
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.