Exploring the Influence of Neighborhood Characteristics on Burglary Risks: A Bayesian Random Effects Modeling Approach

Abstract

A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization and routine activity theories, five covariates extracted from the available data at the neighborhood level were used in the modeling. Three regression models were fitted and compared by the deviance information criterion to identify which model best fit our data. A comparison of the results from the three models indicates that the Bayesian random effects model is superior to the non-spatial models in fitting the data and estimating regression coefficients. Our results also show that neighborhoods with above average bar density and department store density have higher burglary risks. Neighborhood-specific burglary risks and posterior probabilities of neighborhoods having a burglary risk greater than 1.0 were mapped, indicating the neighborhoods that should warrant more attention and be prioritized for crime intervention and reduction. Implications and limitations of the study are discussed in our concluding section.

The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China

“…Strictly speaking, the spatial association is not enough to expose the causal relationships between land-use features and crimes. Such causal inferences can be made with the help of cross-sectional studies [46]. Future work will focus on collecting environmental, economic, and demographic data.…”

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

“…Social disorganization theory has been widely used to explore the relationship between crime and related neighborhood characteristics [8]. One of the premises of social disorganization theory is that the crime rate of disadvantageous communities is higher than others, which has been supported by many empirical studies [9]. Statistical techniques have been used to quantitatively investigate the relationship between crime and influencing factors, such as ordinary least squares (OLS) [10,11].…”

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

“…According to the social disorganization and routine activity theories, various environmental factors are found to be related to burglary, including road configurations [1,2], residential instability [3,4], demographics [5], income [6,7], unemployment rate [8], land use mix [9], housing characteristics [10][11][12][13], guardianship [14,15], accessibility [16], and the physical environment [17].…”