Using geographically weighted regression to explore local crime patterns
Using geographically weighted regression to explore local crime patterns
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DOI：
10.1177/0894439307298925
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发表时间：
2007-06-01
影响因子：
4.1
通讯作者：
Mulligan, Gordon
中科院分区：
文献类型：
--
作者：
Cahill, Meagan;Mulligan, Gordon
本研究考察了俄勒冈州波特兰暴力犯罪的结构模型，探索犯罪及其协变量的空间模式。使用标准的结构措施，从一个机会框架，研究提供了一个全球普通最小二乘模型，假设适合研究区域内的所有位置的结果。地理加权回归（GWR），然后介绍了作为替代传统的方法来建模犯罪。GWR过程估计局部模型，产生一组可映射的参数估计值和随空间变化的显著性t值。一些结构性措施被发现与犯罪的关系，显着不同的位置。结果表明，一个混合模型-空间变化和固定参数-可能提供最准确的犯罪模型。本研究展示了GWR的实用性，探索当地的过程，推动犯罪水平和检查错误的全球模型的城市暴力。
The present research examines a structural model of violent crime in Portland, Oregon, exploring spatial patterns of both crime and its covariates. Using standard structural measures drawn from an opportunity framework, the study provides results from a global ordinary least squares model, assumed to fit for all locations within the study area. Geographically weighted regression (GWR) is then introduced as an alternative to such traditional approaches to modeling crime. The GWR procedure estimates a local model, producing a set of mappable parameter estimates and t-values of significance that vary over space. Several structural measures are found to have relationships with crime that vary significantly with location. Results indicate that a mixed model - with both spatially varying and fixed parameters-may provide the most accurate model of crime. The present study demonstrates the utility of GWR for exploring local processes that drive crime levels and examining misspecification of a global model of urban violence.