Lomax, N orcid.org/0000-0001-9504-7570 (2021) Estimating household mobility using novel Big Data. In: Birkin, M, Clarke, G, Corcoran, J and Stimson, R, (eds.) Big Data Applications in Geography and Planning. Edward Elgar , Cheltenham, UK , pp. 25-42. ISBN 9781789909784
Abstract
Household mobility is a process which delivers substantial change in population size and composition at a local level. The 2011 UK Census shows that 6.8 million people made an internal move in the year to March 27, 2011, ten times more than the number who migrated from overseas. We need timely data which provides good coverage to understand mobility patterns because they impact on every part of the planning system, from housing to transport and health care provision. Internal mobility patterns are an essential component in sub-national population projections which inform forward looking policy. Currently, data used in official estimates are incomplete, relying on individuals informing their doctor of a change of address, and these data are adjusted to take in to account under- and over-counts, for example of young men and students who do not re-register in a timely manner. This chapter introduces a novel Big Dataset which captures the mobility patterns of households who use the Royal Mail postal forwarding service and combines these with property information from an online search portal. Mobility patterns are estimated and assessed by property type and area characteristics. The data are compared to official estimates and to Census data to provide an overview of data quality. The chapter concludes with a discussion about the future of assessing mobility patterns using Big Data.
Metadata
Item Type: | Book Section |
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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) |
Funding Information: | Funder Grant number ESRC (Economic and Social Research Council) ES/L011891/1 ESRC (Economic and Social Research Council) ES/S007164/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Oct 2021 08:11 |
Last Modified: | 04 Oct 2021 08:11 |
Published Version: | https://www.elgaronline.com/view/edcoll/9781789909... |
Status: | Published |
Publisher: | Edward Elgar |
Identification Number: | 10.4337/9781789909791.00008 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178567 |