Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy
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School of Tourism and Geography Science, Qingdao University, Qingdao 266071, China
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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
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Key Laboratory of Aerospace Information Security and Trusted Computing of the Ministry of Education, Wuhan University, Wuhan 430079, China
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School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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College of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450016, China
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 488; https://doi.org/10.3390/ijgi8110488
Received: 22 September 2019 / Revised: 22 October 2019 / Accepted: 28 October 2019 / Published: 30 October 2019
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are often neglected in Bayesian spatial crime analysis. The contextual effects model accounts for the effects of independent variables, overdispersion, spatial autocorrelation, and contextual effects. Both the contextual effects model and the conventional Bayesian spatial model were fitted to our data. Results showed the two models had almost the same deviance information criterion (DIC). Furthermore, they identified the same set of significant independent variables and gave very similar estimates for burglary risks. Nonetheless, the contextual effects model was preferred in the sense that it provides insights into contextual effects on crime risks. Based on the contextual effects model and the map decomposition technique, we identified, worked out, and mapped the relative contribution of the neighborhood characteristics and contextual effects on the overall burglary risks. The research contributes to the increasing literature on modeling crime data by Bayesian spatial approaches.
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MDPI and ACS Style
Liu, H.; Zhu, X.; Zhang, D.; Liu, Z. Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy. ISPRS Int. J. Geo-Inf. 2019, 8, 488.
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