Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China
AbstractThe relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the
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Chen, J.; Liu, L.; Zhou, S.; Xiao, L.; Song, G.; Ren, F. Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China. ISPRS Int. J. Geo-Inf. 2017, 6, 138.
Chen J, Liu L, Zhou S, Xiao L, Song G, Ren F. Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China. ISPRS International Journal of Geo-Information. 2017; 6(5):138.Chicago/Turabian Style
Chen, Jianguo; Liu, Lin; Zhou, Suhong; Xiao, Luzi; Song, Guangwen; Ren, Fang. 2017. "Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China." ISPRS Int. J. Geo-Inf. 6, no. 5: 138.
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