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

• Published in: Crime and delinquency Vol. 71; no. 6-7; pp. 2061 - 2091
• Main Authors: Zhang, Yanji, Cai, Liang, Song, Guangwen, Zhu, Chunwu
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.06.2025; SAGE PUBLICATIONS, INC
• Subjects: Algorithms; Crime prevention; Decision making; Global local relationship; Long term; Machine learning; Measures; Policy making; Predictions; Prevention programs; Theft; nonlinear relationship; spatial heterogeneity; crime prediction; gradient boosting decision tree; interpretable machine learning
• ISSN: 0011-1287; 1552-387X
• DOI: 10.1177/00111287231180102

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.