Mapping the geodemographics of digital inequality in Great Britain: An integration of machine learning into small area estimation. (July 2020)

• Record Type: Journal Article
• Authors: Singleton, Alex; Alexiou, Alexandros; Savani, Rahul
• Is Part Of: Computers, environment and urban systems. Volume 82(2020)
• Publication Date: 2020-07
• Subjects: City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101486
• Languages: English
• ISSNs: 0198-9715

Abstract

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. Highlights: Great Britain can be differentiated into ten groups of salient digital inclusion / exclusion. Supervised machine learning can provide methodological enhancement to small area estimation. Census response rates have a relationship to patterns of digital inclusion / exclusion.