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- W165109416 abstract "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 wellspecified 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." @default.
- W165109416 created "2016-06-24" @default.
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- W165109416 date "2013-01-01" @default.
- W165109416 modified "2023-09-26" @default.
- W165109416 title "Driving Forces of Non-Violent Crime in Houston, TX: A Spatially Filtered Negative Binomial Model" @default.
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- W165109416 doi "https://doi.org/10.1553/giscience2013s117" @default.
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