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)

[img] Text
102199.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

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

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
500762Advanced Quantitative Methods Network (AQMeN) in ScotlandGwilym PryceEconomic & Social Research Council (ESRC)RES-043-25-0011SPS - URBAN STUDIES