Research Article
Bayesian analysis of the multivariate geographical distribution of the socio-economic environment in England
Article first published online: 22 AUG 2007
DOI: 10.1002/env.872
Copyright © 2007 John Wiley & Sons, Ltd.
Issue
Environmetrics
Special Issue: Special Issue on METMA3 Workshop: Spatial and Spatio-temporal Modelling
Volume 18, Issue 7, pages 745–758, November 2007
Additional Information
How to Cite
Abellan, J. J., Fecht, D., Best, N., Richardson, S. and Briggs, D. J. (2007), Bayesian analysis of the multivariate geographical distribution of the socio-economic environment in England. Environmetrics, 18: 745–758. doi: 10.1002/env.872
Publication History
- Issue published online: 26 SEP 2007
- Article first published online: 22 AUG 2007
- Manuscript Accepted: 22 JUN 2007
- Manuscript Received: 10 NOV 2006
Funded by
- Department of Health
- Department for Environment
- Food and Rural Affairs
- Environment Agency
- Scottish Executive
- Welsh Assembly Government and Northern Ireland Department of Health, Social Services and Public Safety
- Spanish Ministry of Education and Science. Grant Number: MTM2004-03290
- AFSSE. Grant Number: RD2004004
- INSERM-ATC. Grant Number: A03150LS
- Abstract
- References
- Cited By
Keywords:
- socio-economic status;
- indices of deprivation;
- social inequalities;
- geographical distribution;
- spatial factor analysis;
- Bayesian statistics
Abstract
In this paper we analyse the geographical distribution of the Indices of Deprivation 2004, a set of seven area-based indices measuring multiple dimensions of socio-economic status (SES) related to income, employment, education, health, access to services and housing, crime, and living environment in England at district level. We first study all the seven SES ‘domains’ separately using spatial hierarchical models to assess the level of underlying geographical structure in each of them. We then explore their joint relationships using a spatial factor model that extracts the information common and specific to the income, employment and education domains and regress the other domains on the extracted components. We use Bayesian inference throughout. Results show that all seven domains present strong spatial structure. The income, employment, education and health domains largely share the same information, which moderately overlaps with the crime and environment domains. The domain on access to services and housing is quite different from the others. The geographical patterns found in the latent common and specific factors are however difficult to interpret without further complementary information. Further investigation is required to identify plausible causal processes related to current economic and regional policies, and also to historic factors and their long-lasting socio-economic legacy. Copyright © 2007 John Wiley & Sons, Ltd.