Driving Forces of Non-Violent Crime in Houston, TX: A Spatially Filtered Negative Binomial Model

摘要

The analysis and understanding of spatial crime patterns is crucial for law enforcements to improve strategic and tactical decision-making. In this context, generalized linear models, such as count regressions, are commonly applied. These non-spatial models are challenged by spatial autocorrelation effects, contradicting fundamental model assumptions. Therefore, the purpose of this research is to present a spatially explicit approach, which combines a negative binomial model and spatial filtering to explain the spatial distribution of nonviolent offences in Houston, TX, for the year 2010. The results provide evidence that the non-spatial negative binomial model is biased while the supplementary consideration of a spatial filter is capable to absorb these undesirable spatial effects and results in a well-specified regression model. Moreover, besides the significant importance of space in the explanation of the non-violent crime patterns, only the percentage of renter-occupied housing units and the percentage of Asian population are significantly related to the crime. The former covariate has a stimulating effect while the latter has an inhibiting effect.