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