Abstract
A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.
Highlights
Ecological studies of crime are of great interest to geographers and criminologists as they can reveal the geographic pattern of crime risks as well as the relevant risk factors explaining that pattern
The major difference is the way in which the random effects were specified; one is in the univariate form, while the other is in the multivariate form
In terms of deviance information criterion (DIC), the overall model comparison criterion that takes into account both model fit and model complexity, the multivariate model was better suited to our data
Summary
Ecological studies of crime are of great interest to geographers and criminologists as they can reveal the geographic pattern of crime risks as well as the relevant risk factors explaining that pattern. Craglia et al [1] adopted a standard logistic regression model to explore the relationship between socioeconomic characteristics and high-intensity crime areas Such non-spatial models assume that the observations are independent and identically distributed. Adjacent areas often have similar crime data, i.e., crime data are very likely to be spatially auto-correlated, especially at small-area scales as often indicated by clusters appearing on crime maps. Ignoring this spatial dependence in ecological studies can result in estimates with underestimated standard errors [2]. The traditional non-spatial models are inappropriate to analyze crime at the small-area level
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