An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources
Abstract To address the scarcity of small-area datasets focused on economic inclusion, we created a harmonised dataset describing the extent and enablers of economic inclusion in Great Britain. The result, the SIPHER (Systems Science in Public Health and Health Economics Research) Inclusive Economy...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05502-x |
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| author | Hugh P. Rice Andreas Höhn Petra Meier Alison Heppenstall Nik Lomax |
| author_facet | Hugh P. Rice Andreas Höhn Petra Meier Alison Heppenstall Nik Lomax |
| author_sort | Hugh P. Rice |
| collection | DOAJ |
| description | Abstract To address the scarcity of small-area datasets focused on economic inclusion, we created a harmonised dataset describing the extent and enablers of economic inclusion in Great Britain. The result, the SIPHER (Systems Science in Public Health and Health Economics Research) Inclusive Economy (Ward Level) dataset, consists of 13 indicators describing economic inclusion at electoral ward level (N = 7,973 of 8,020 wards, 2022 boundaries), for 2019–2021. The dataset was curated based on administrative statistics (mostly open-source) and the SIPHER Synthetic Population, a validated, survey-based, full-scale synthetic population dataset derived from the UK Household Longitudinal Study (UKHLS): Understanding Society, and aggregate-level population statistics. The dataset also includes summary measures of population health – age-standardised Short Form Health Survey (SF-12) mental and physical health component scores – and supplementary demographic indicators describing the population structure. For validation, a range of comparisons against deprivation indices and other data provide strong evidence of the dataset’s added value and utility for applications in research and policy requiring high-quality estimates at a granular spatial resolution. |
| format | Article |
| id | doaj-art-b5fd47b2fb00420f9ba9038b3b597fa7 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-b5fd47b2fb00420f9ba9038b3b597fa72025-08-20T03:42:44ZengNature PortfolioScientific Data2052-44632025-07-0112111810.1038/s41597-025-05502-xAn inclusive economy dataset for wards in Great Britain using administrative and synthetic data sourcesHugh P. Rice0Andreas Höhn1Petra Meier2Alison Heppenstall3Nik Lomax4Institute for Spatial Data Science, School of Geography, University of LeedsSchool of Health and Wellbeing, University of GlasgowSchool of Health and Wellbeing, University of GlasgowSchool of Health and Wellbeing, University of GlasgowInstitute for Spatial Data Science, School of Geography, University of LeedsAbstract To address the scarcity of small-area datasets focused on economic inclusion, we created a harmonised dataset describing the extent and enablers of economic inclusion in Great Britain. The result, the SIPHER (Systems Science in Public Health and Health Economics Research) Inclusive Economy (Ward Level) dataset, consists of 13 indicators describing economic inclusion at electoral ward level (N = 7,973 of 8,020 wards, 2022 boundaries), for 2019–2021. The dataset was curated based on administrative statistics (mostly open-source) and the SIPHER Synthetic Population, a validated, survey-based, full-scale synthetic population dataset derived from the UK Household Longitudinal Study (UKHLS): Understanding Society, and aggregate-level population statistics. The dataset also includes summary measures of population health – age-standardised Short Form Health Survey (SF-12) mental and physical health component scores – and supplementary demographic indicators describing the population structure. For validation, a range of comparisons against deprivation indices and other data provide strong evidence of the dataset’s added value and utility for applications in research and policy requiring high-quality estimates at a granular spatial resolution.https://doi.org/10.1038/s41597-025-05502-x |
| spellingShingle | Hugh P. Rice Andreas Höhn Petra Meier Alison Heppenstall Nik Lomax An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources Scientific Data |
| title | An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources |
| title_full | An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources |
| title_fullStr | An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources |
| title_full_unstemmed | An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources |
| title_short | An inclusive economy dataset for wards in Great Britain using administrative and synthetic data sources |
| title_sort | inclusive economy dataset for wards in great britain using administrative and synthetic data sources |
| url | https://doi.org/10.1038/s41597-025-05502-x |
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