Analysing non-linearities and threshold effects between street-level built environments and local crime patterns: An interpretable machine learning approach

Abstract

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.

尽管对犯罪模式与建成环境之间关系的研究有很多，但由于无法获得详细的街景数据以及传统分析模型的局限性，街道建成环境对犯罪模式的影响尚未得到明确确定。为了填补这些空白，本文重点关注加利福尼亚州圣安娜市街道层面的建成环境与当地犯罪模式之间的非线性关系和阈值效应。我们使用谷歌街景 (GSV) 和语义分割技术，量化了 GSV 图像中建成环境的特征。然后，我们应用可解释机器学习（IML）方法考察建成环境因素和犯罪之间的非线性关系和阈值效应。机器学习模型，尤其是深度神经网络（DNN）在预测未来犯罪事件方面的表现优于负二项回归，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。我们通过 IML 解释 DNN 模型，结果表明，大多数街景元素与犯罪模式表现出非线性关系和阈值效应，这是传统回归模型设置难以捕捉到的。本文揭示的非线性关系和阈值效应可以让人们对与犯罪模式相关的因素有进一步的了解，并有助于制定预防犯罪的公共安全政策。