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GI_Forum 2013, Volume 1Creating the GISociety – Conference Proceedings
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2013, Volume 1, pp. 117-126, 2013/06/20
Creating the GISociety – Conference Proceedings
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.