Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Abstract

Abstract:The 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 R 2 d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R 2 d . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model

“…There are several local parameters varying from negative to positive in the local models, which is not in conformity with our common sense. For example, the floating population has been reported to have a significantly positive impact on residential burglaries in previous studies [25,47,48], which means that PSMAs with fewer floating populations were safer. Nevertheless, the coefficients of the floating population in some PSMAs are negative in this research.…”

“…The coefficients of the three local models were positive except for a few PSMAs. Renters have been reported as an important risk factor related to crime in previous studies due to high mobility [25]. According to the social disorganization theory, the increase of resident mobility will lead to more crimes [8].…”

“…The number of residential burglaries in each PSMA varied from 9 to 3547, as shown in Figure 1. Following previous studies [24,25,[35][36][37], the variables selected for this research are shown in Table 1, as well as their descriptive statistics. The number of burglaries was adopted as the dependent variable in this study.…”

“…According to the type of response variable, GWR has evolved into different versions, such as geographic weight Gaussian regression, geographic weight Poisson regression (GWPR), and geographic weight logistic regression (GWLR). Crime data are usually reported in the form of the number of criminal cases, which can be used as the response variable without any transformation in the GWPR model [24,25].…”

Using the PROSA Method in Offshore Wind Farm Location Problems

“…Therefore, it is easy to examine whether all components of a given variant are close to each other, i.e., whether the variant is sustainable in terms of economic, social and environmental criteria. In the PROSA method, we propose to use the measure of mean absolute deviation (MAD) [47] extended by the weights of the individual components. The basic measure of MAD is described by the formula (8):…”