Nonlinear and spatially heterogeneous relationship between environmental factors and violent crime:Based on interpretable machine learning method
The nonlinear relationship and spatially heterogeneous relationship between environmental factors and criminal activities are the main reasons for both the theoretical and empirical divergence,but the relevant analysis remains fragmented and faces limitations such as linear relationship hypothesis,collinearity problems and omitted variable bias.This study uses Gradient Boosting Decision Tree(GBDT)algorithm and Shapley Additive Explanation(SHAP)interpreter in machine learning to systematically reveal the nonlinear and spatially heterogeneous relationships between 48 built and social environmental factors on violent crime in Beijing.Our research has revealed the existence of seven distinct types of nonlinear relationships between environmental factors and violent crime,each exhibiting unique trends in the direction of influence and marginal effects.Furthermore,we have found that the association between environmental factors and violent crime exhibits varying degrees of spatial heterogeneity.By utilizing K-means clustering analysis,the entire area can be segmented into six distinct regions,each characterized by different critical criminogenic factors.These findings suggest that the applicability of crime geography theories,such as the classification of crime generators,attractors,and inhibitors based on crime pattern theory,the validity of street eye theory and defensible space theory,and the impact of social attributes as proposed by social disorganization theory,may depend on the value range of environmental factors and differ across locations.In light of these findings,it is recommended that crime prevention strategies shift from universal to targeted approaches,wherein public resources are allocated to specific value ranges of environmental variables and prioritized regions.
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