Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests
Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests
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DOI：
10.1007/s10109-008-0073-5
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发表时间：
2009-03-01
影响因子：
2.9
通讯作者：
Waller, Lance A.
中科院分区：
文献类型：
--
作者：
Wheeler, David C.;Waller, Lance A.
在本文中，我们比较和对比贝叶斯空间变化系数过程（SVCP）模型与地理加权回归（GWR）模型的潜在空间变化的回归影响的酒精出口和非法毒品活动的暴力犯罪在德克萨斯州休斯顿的估计。此外，我们专注于固有的系数收缩属性的贝叶斯SVCP模型作为一种方式来解决增加的系数方差，从GWR模型中的共线性。我们概述了贝叶斯模型的优势，减少膨胀系数方差，增强模型的灵活性，更正式的测量模型的不确定性进行预测。我们发现空间上不同的影响，酒精出口和毒品违法行为，但变化量取决于所使用的模型的类型。对于贝叶斯模型，这种变化可以通过对系数方差的先验影响量进行控制。例如，当在贝叶斯模型中使用相对较大的先验方差时，GWR和贝叶斯模型的系数的空间模式是相似的。
In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.