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Open AccessArticle

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

1,2,* and 1,2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, 129 Luoyu Road, Wuhan University, Wuhan 430079, China
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editors: Marco Helbich and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(1), 16;
Received: 20 October 2016 / Revised: 18 December 2016 / Accepted: 9 January 2017 / Published: 13 January 2017
PDF [1412 KB, uploaded 13 January 2017]


A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section. View Full-Text
Keywords: multivariate spatial model; crime risk; Bayesian spatial modeling; spatial Poisson regression; Markov chain; Monte Carlo multivariate spatial model; crime risk; Bayesian spatial modeling; spatial Poisson regression; Markov chain; Monte Carlo

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Liu, H.; Zhu, X. Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach. ISPRS Int. J. Geo-Inf. 2017, 6, 16.

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