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Modeling Local-Scale Violent Crime Rate: A Comparison of Eigenvector Spatial Filtering Models and Conventional Spatial Regression Models
The Professional Geographer ( IF 2.411 ) Pub Date : 2021-01-29
Yeran Sun, Shaohua Wang, Jing Xie, Xuke Hu

Environmental factors have both direct and indirect impacts on crime behavior decision making. This study aimed to examine to what degree the occurrences of violent crimes can be affected by social and built environment over space. Although a few studies have attempted to model crime rate using spatial regression models, there is a lack of comparison of spatial regression models. Particularly, the eigenvector spatial filtering type of spatial regression models has reportedly been effective in urban and regional studies, but it has not been widely applied to crime data. In this study, we aimed to examine whether the spatial filtering type of spatial regression models outperforms conventional types of spatial regression models in modeling violent crime rates over space. Moreover, we aimed to investigate the impacts of land use mix and street connectivity on the occurrences of violent crimes as the routine activity theory explained. In empirical studies, two types of spatial regression models (i.e., spatial error model and eigenvector spatial filtering model) were selected and estimated successfully to model local-scale violent crime rates across New York City. The eigenvector spatial filtering models outperform the spatial error models as well as the nonspatial models. Model estimation results show that occurrences of violent crimes (i.e., assaults and robberies) can be well determined by socioeconomic and built environment factors and thereby environmental factors can affect the occurrences of violent crimes. The contributions of socioeconomic and built environment factors to violent crime can offer insights on urban planning and policymaking toward violent crime prevention. Particularly, this study offers new evidence on the routine activity theory that increasing land use mix and street connectivity can enhance street activity, thereby reducing occurrences of violent crimes. Policymakers and urban planners should continue to enhance street activity through increasing land use mix and street connectivity. In addition, eigenvector spatial filtering models are advocated for use in crime or other applications in urban and regional studies.



中文翻译:

模拟地方尺度的暴力犯罪率:特征向量空间滤波模型与常规空间回归模型的比较

环境因素对犯罪行为的决策有直接和间接的影响。这项研究旨在研究暴力犯罪的发生在多大程度上受到空间上社会和建筑环境的影响。尽管一些研究尝试使用空间回归模型对犯罪率进行建模,但仍缺乏空间回归模型的比较。特别是,空间回归模型的特征向量空间滤波类型据报道已在城市和区域研究中有效,但尚未广泛应用于犯罪数据。在这项研究中,我们旨在研究在空间暴力犯罪率建模中,空间回归模型的空间过滤类型是否优于传统类型的空间回归模型。此外,正如常规活动理论所解释的那样,我们旨在调查土地使用组合和街道连通性对暴力犯罪发生的影响。在实证研究中,选择并成功估计了两种类型的空间回归模型(即空间误差模型和特征向量空间滤波模型),以模拟整个纽约市的局部暴力犯罪率。特征向量空间滤波模型优于空间误差模型和非空间模型。模型估计结果表明,可以通过社会经济因素和建筑环境因素很好地确定暴力犯罪(例如袭击和抢劫)的发生,从而环境因素可以影响暴力犯罪的发生。社会经济因素和建筑环境因素对暴力犯罪的贡献可以为预防暴力犯罪的城市规划和政策制定提供见识。特别是,这项研究为例行活动理论提供了新的证据,即增加土地使用组合和街道连通性可以增强街道活动,从而减少暴力犯罪的发生。政策制定者和城市规划者应继续通过增加土地使用组合和街道连通性来增强街道活动。另外,本征向量空间滤波模型被提倡用于犯罪或城市和区域研究的其他应用。从而减少暴力犯罪的发生。政策制定者和城市规划者应继续通过增加土地使用组合和街道连通性来增强街道活动。另外,本征向量空间滤波模型被提倡用于犯罪或城市和区域研究的其他应用。从而减少暴力犯罪的发生。政策制定者和城市规划者应继续通过增加土地使用组合和街道连通性来增强街道活动。另外,本征向量空间滤波模型被提倡用于犯罪或城市和区域研究的其他应用。

更新日期:2021-01-29
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