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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Bibliographic Details
Authors: Zhang, Yanji (Author) ; Cai, Liang (Author) ; Song, Guangwen (Author) ; Zhu, Chunwu (Author)
Format: Electronic Article
Language:English
Published: 2025
In: Crime & delinquency
Year: 2025, Volume: 71, Issue: 6/7, Pages: 2061-2091
Online Access: Volltext (lizenzpflichtig)
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Keywords:
crime prediction
gradient boosting decision tree
interpretable machine learning
nonlinear relationship
spatial heterogeneity