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

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.