A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain

Guoqiang Wu; Alison Heppenstall; Petra Meier; Robin Purshouse; Nik Lomax

doi:10.1038/s41597-022-01124-9

Scientific Data (Jan 2022)

A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain

Guoqiang Wu,
Alison Heppenstall,
Petra Meier,
Robin Purshouse,
Nik Lomax

Affiliations

Guoqiang Wu: Leeds Institute for Data Analytics and School of Geography, University of Leeds
Alison Heppenstall: Leeds Institute for Data Analytics and School of Geography, University of Leeds
Petra Meier: MRC/CSO Social and Public Health Sciences Unit, University of Glasgow
Robin Purshouse: Department of Automatic Control and Systems Engineering, University of Sheffield
Nik Lomax: Leeds Institute for Data Analytics and School of Geography, University of Leeds

DOI: https://doi.org/10.1038/s41597-022-01124-9
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 11

Abstract

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Measurement(s) Health Disparity Populations • socio-economic outcomes Technology Type(s) computational modeling technique • digital curation Factor Type(s) geographical area Sample Characteristic - Location Great Britain

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

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