Bayesian inference for the dissimilarity index in the presence of spatial autocorrelation

Lee, D. , Minton, J. and Pryce, G. (2015) Bayesian inference for the dissimilarity index in the presence of spatial autocorrelation. Spatial Statistics, 11, pp. 81-95. (doi: 10.1016/j.spasta.2014.12.001)

Abstract

The degree of segregation between two or more sub-populations has been studied since the 1950s, and examples include segregation along racial and religious lines. The Dissimilarity index is a commonly used measure to numerically quantify segregation, using population level data for a set of areal units that comprise a city or country. However, the construction of this index usually ignores the spatial autocorrelation present in the data, and it is also typically presented without a measure of uncertainty. Therefore we propose a Bayesian hierarchical modelling approach for estimating the Dissimilarity index and quantifying its uncertainty, which utilises a conditional autoregressive model to account for the spatial autocorrelation in the data. This modelling approach is motivated by a study of religious segregation in Northern Ireland, and allows us to quantify whether the dissimilarity index has exhibited a substantial change between 2001 and 2011.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Pryce, Professor Gwilym and Minton, Dr Jonathan and Lee, Professor Duncan
• Authors: Lee, D., Minton, J., and Pryce, G.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics; College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Spatial Statistics
• Publisher: Elsevier B.V.
• ISSN: 2211-6753
• ISSN (Online): 2211-6753
• Copyright Holders: Copyright © 2015 The Authors
• First Published: First published in Spatial Statistics 11:81-95
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
50076	2	Advanced Quantitative Methods Network (AQMeN) in Scotland	Gwilym Pryce	Economic & Social Research Council (ESRC)	RES-043-25-0011	SPS - URBAN STUDIES