Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy
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 neg...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/8/11/488 |
| _version_ | 1828518921492234240 |
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| author | Hongqiang Liu Xinyan Zhu Dongying Zhang Zhen Liu |
| author_facet | Hongqiang Liu Xinyan Zhu Dongying Zhang Zhen Liu |
| author_sort | Hongqiang Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T19:04:19Z |
| format | Article |
| id | doaj.art-3ef6c0741eef43c093d1503e204099d6 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-11T19:04:19Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-3ef6c0741eef43c093d1503e204099d62022-12-22T00:53:57ZengMDPI AGISPRS International Journal of Geo-Information2220-99642019-10-0181148810.3390/ijgi8110488ijgi8110488Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling StrategyHongqiang Liu0Xinyan Zhu1Dongying Zhang2Zhen Liu3School of Tourism and Geography Science, Qingdao University, Qingdao 266071, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450016, ChinaA 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.https://www.mdpi.com/2220-9964/8/11/488crime riskcontextual effectsbayesian spatial modelingpoisson regression |
| spellingShingle | Hongqiang Liu Xinyan Zhu Dongying Zhang Zhen Liu Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy ISPRS International Journal of Geo-Information crime risk contextual effects bayesian spatial modeling poisson regression |
| title | Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy |
| title_full | Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy |
| title_fullStr | Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy |
| title_full_unstemmed | Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy |
| title_short | Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy |
| title_sort | investigating contextual effects on burglary risks a contextual effects model built based on bayesian spatial modeling strategy |
| topic | crime risk contextual effects bayesian spatial modeling poisson regression |
| url | https://www.mdpi.com/2220-9964/8/11/488 |
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