Despite the substantial number of studies on the relationships between crime patterns and built environments, the impacts of street-level built environments on crime patterns have not been definitively determined due to the limitations of obtaining detailed streetscape data and conventional analysis models. To fill these gaps, this study focuses on the non-linear relationships and threshold effects between built environments and local crime patterns at the level of a street segment in the City of Santa Ana, California. Using Google Street View (GSV) and semantic segmentation techniques, we quantify the features of the built environment in GSV images. Then, we examine the non-linear relationships and threshold effects between built environment factors and crime by applying interpretable machine learning (IML) methods. While the machine learning models, especially Deep Neural Network (DNN), outperformed negative binomial regression in predicting future crime events, particularly advantageous was that they allowed us to obtain a deeper understanding of the complex relationship between crime patterns and environmental factors. The results of interpreting the DNN model through IML indicate that most streetscape elements showed non-linear relationships and threshold effects with crime patterns that cannot be easily captured by conventional regression model specifications. The non-linearities and threshold effects revealed in this study can shed light on the factors associated with crime patterns and contribute to policy development for public safety from crime.

中文翻译：

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分析街道建筑环境与当地犯罪模式之间的非线性和阈值效应：一种可解释的机器学习方法


尽管对犯罪模式与建筑环境之间的关系进行了大量研究，但由于获取详细街景数据和传统分析模型的限制，街道建筑环境对犯罪模式的影响尚未明确确定。为了填补这些空白，本研究重点关注加利福尼亚州圣安娜市街道段的建筑环境与当地犯罪模式之间的非线性关系和阈值效应。使用 Google 街景 (GSV) 和语义分割技术，我们量化了 GSV 图像中建筑环境的特征。然后，我们通过应用可解释机器学习（IML）方法来检查建筑环境因素与犯罪之间的非线性关系和阈值效应。虽然机器学习模型，特别是深度神经网络（DNN），在预测未来犯罪事件方面优于负二项式回归，但特别有利的是它们使我们能够更深入地了解犯罪模式与环境因素之间的复杂关系。通过 IML 解释 DNN 模型的结果表明，大多数街景元素与犯罪模式呈现出非线性关系和阈值效应，而传统的回归模型规范无法轻松捕获这些关系。本研究揭示的非线性和阈值效应可以揭示与犯罪模式相关的因素，并有助于制定公共安全免受犯罪的政策。