Spatio-temporal areal unit modelling in R with conditional autoregressive priors using the CARBayesST package

Lee, D. , Rushworth, A. and Napier, G. (2018) Spatio-temporal areal unit modelling in R with conditional autoregressive priors using the CARBayesST package. Journal of Statistical Software, 84(9), (doi:10.18637/jss.v084.i09)

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Abstract

Spatial data relating to non-overlapping areal units are prevalent in fields such as economics, environmental science, epidemiology and social science, and a large suite of modelling tools have been developed for analysing these data. Many utilise conditional autoregressive (CAR) priors to capture the spatial autocorrelation inherent in these data, and software such as CARBayes and R-INLA have been developed to make these models easily accessible to others. Such spatial data are typically available for multiple time periods, and the development of methodology for capturing temporally changing spatial dynamics is the focus of much current research. A sizeable proportion of this literature has focused on extending CAR priors to the spatio-temporal domain, and this article presents the R package CARBayesST, which is the first dedicated software for spatio-temporal areal unit modelling with conditional autoregressive priors. The software can fit a range of models focused on dfferent aspects of space-time modelling, including estimation of overall space and time trends, and the identification of clusters of areal units that exhibit elevated values. This paper outlines the class of models that the software can implement, before applying them to simulated and two real examples, the latter in the fields of epidemiology and housing market analysis.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan and Napier, Dr Gary
Authors: Lee, D., Rushworth, A., and Napier, G.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of Statistical Software
Publisher:Foundation for Open Access Statistics
ISSN:1548-7660
Published Online:20 April 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Journal of Statistical Software 84(9):
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
588351A rigorous statistical framework for estimating the long-term health effects of air pollution.Duncan LeeEngineering and Physical Sciences Research Council (EPSRC)EP/J017442/1M&S - STATISTICS
647701A flexible class of Bayesian spatio-temporal models for cluster detection, trend estimation and forecasting of disease risk.Duncan LeeMedical Research Council (MRC)MR/L022184/1M&S - STATISTICS