Harnessing mobility data to capture changing work from home behaviours between censuses

Datos Bibliográficos

ID5286141
AutoresHubert Gibbs (0000-0003-4413-453X, Department of Geography University College London London UK, autor de correspondencia), Peri Ballantyne (0000-0001-8980-2912, Department of Geography and Planning University of Liverpool Liverpool UK), James Cheshire (0000-0003-4552-5989, Department of Geography University College London London UK), Alex Singleton (0000-0002-2338-2334, Department of Geography and Planning University of Liverpool Liverpool UK), Mark Green (0000-0002-0942-6628, Department of Geography and Planning University of Liverpool Liverpool UK)
Año2024
Volumen190
Número2
Fecha de publicación2024-06-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaGeographical Journal (JOURNAL)
Identificadores de la revistaISSN: 0016-7398 • E-ISSN: 1475-4959
EditorialWiley (PUBLISHER • GB)
DOI10.1111/geoj.12555
OpenAlexW4388133928
IdiomaEN
Citas recibidas6
Referencias citadas27

This paper provides an analysis of working from home patterns in England using data from the 2021 Census to understand (1) how patterns of working from home (WFH) in England have shifted since the COVID-19 pandemic and (2) whether human mobility indicators, specifically Google Community Mobility Reports, provide a reliable proxy for WFH patterns recorded by the 2021 Census, providing a formal evaluation of the reliability of such datasets, whose applications have grown exponentially over the COVID-19 pandemic. We find that WFH patterns recorded by the 2021 Census were unique compared with previous UK censuses, reflecting an unprecedented increase likely caused by persistent changes to employment during the COVID-19 pandemic, with a clear social gradient emerging across the country. We also find that Google mobility in 'Residential' and 'Workplace' settings provides a reliable measurement of the distribution of WFH populations across Local Authorities, with varying uncertainties for mobility indicators collected in different settings. These findings provide insights into the utility of such datasets to support population research in intercensal periods, where shifts may be occurring, but can be difficult to quantify empirically

American Community Survey · Census · Coronavirus disease 2019 (COVID-19 · Demographic economics · Economics · Geography · Pandemic · Population · Proxy (statistics · Sociology · Work (physics · Computer Science · Demography · Human Mobility and Location-Based Analysis · Medicine · Urban Transport and Accessibility · Urban, Neighborhood, and Segregation Studies