The Long-Term Theft Prediction in Beijing Using Machine Learning Algorithms: Comparison and Interpretation

To advance the interpretability of machine learning for long-term crime prediction in China, we compared the performance of multiple machine learning algorithms in predicting the spatial pattern of theft in Beijing. Gradient boosting decision tree emerged as the algorithm with best predictive accura...

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Detalles Bibliográficos
Autores principales: Zhang, Yanji (Autor) ; Cai, Liang (Autor) ; Song, Guangwen (Autor) ; Zhu, Chunwu (Autor)
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2025
En: Crime & delinquency
Año: 2025, Volumen: 71, Número: 6/7, Páginas: 2061-2091
Acceso en línea: Volltext (lizenzpflichtig)
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Palabras clave:
crime prediction
gradient boosting decision tree
interpretable machine learning
nonlinear relationship
spatial heterogeneity
Descripción
Sumario:To advance the interpretability of machine learning for long-term crime prediction in China, we compared the performance of multiple machine learning algorithms in predicting the spatial pattern of theft in Beijing. Gradient boosting decision tree emerged as the algorithm with best predictive accuracy. After identifying the importance of criminogenic features, we extended the interpreter SHAP to reveal nonlinear and spatially heterogeneous associations between environmental features and theft and we summarized six relation types of such associations at the global scale. At the local scale, we clustered six area types according to the contribution of environmental attributes to theft prediction in each grid. Policy makers should adopt place-based crime prevention measures based on the specific type of each grid belongs to.
ISSN:1552-387X
DOI:10.1177/00111287231180102