期刊名称：GI_FORUM - Journal for Geographic Information Science
电子版ISSN：2308-1708
出版年度：2013
页码：117-126
DOI：10.1553/giscience2013s117
出版社：ÖAW Verlag, Wien
摘要：The analysis and understanding of spatial crime patterns is crucial for law enforcements toimprove strategic and tactical decision-making. In this context, generalized linear models,such as count regressions, are commonly applied. These non-spatial models are challengedby spatial autocorrelation effects, contradicting fundamental model assumptions. Therefore,the purpose of this research is to present a spatially explicit approach, which combines anegative binomial model and spatial filtering to explain the spatial distribution of nonviolentoffences in Houston, TX, for the year 2010. The results provide evidence that thenon-spatial negative binomial model is biased while the supplementary consideration of aspatial filter is capable to absorb these undesirable spatial effects and results in a wellspecifiedregression model. Moreover, besides the significant importance of space in theexplanation of the non-violent crime patterns, only the percentage of renter-occupied housingunits and the percentage of Asian population are significantly related to the crime. Theformer covariate has a stimulating effect while the latter has an inhibiting effect.
