Unraveling the spatial heterogeneity of urban traffic crashes: A GeoXGBoost and point-mapping SHAP framework.

Objectives

In urban traffic risk research, traditional models struggle to simultaneously capture the complex nonlinearity and spatial heterogeneity of risk-driving factors, while often lacking fine-grained spatial interpretability. To address these limitations, this study develops a comprehensive traffic risk analysis framework that integrates high predictive accuracy with micro-scale causal attribution.

Methods

Using the central urban area of Liyang as an empirical case, an overlapping grid sampling strategy is employed to overcome the constraints of sample sparsity. A GeoXGBoost-based prediction framework is constructed to internalize spatial heterogeneity within the boosting algorithm, supplemented by a local spatial SHAP interpretation framework using a point-mapping strategy to enable precise risk attribution from global statistics to micro-spatial units.

Results

The GeoXGBoost model achieves a superior goodness-of-fit (OOB-predicted R2) of 0.896, significantly outperforming five benchmark models and effectively eliminating the spatial autocorrelation of prediction residuals. Core risk factors, including shopping services, transport facility services, and population density, exhibit pronounced spatial non-stationarity and location-specific functional differentiation, with their driving effects highly dependent on the local road network context.

Conclusions

A deep interactive coupling mechanism of "place-exposure-function" is identified. Standalone static facilities and population scale constitute only potential hazards; however, when superimposed with high-load dynamic traffic flows or highly mixed functions, they trigger an abrupt, nonlinear surge in local crash probabilities. This framework provides a scientific basis for fine-grained, location-specific spatial interventions in urban traffic safety governance.