Scientific Development Research
Scientific Development Research . 2025; 5: (6) ; 10.12208/j.sdr.20250235 .
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1 湖北经济学院新财经交叉学科研究院 湖北武汉
2 湖北经济学院湖北数字政府建设研究中心 湖北武汉
3 湖北经济学院湖北数据与分析中心 湖北武汉
4 湖北经济学院金融学院 湖北武汉
*通讯作者： 姚金伶,单位：；
犯罪预测是预防犯罪的一项技术方法。近年来，随着大数据技术和人工智能的兴起，机器学习方法被引入到犯罪预测的研究中，取得了丰富的研究成果。本文系统梳理了基于机器学习的犯罪预测研究进展，从环境犯罪学理论出发总结了犯罪预测的理论基础与方法演进，阐述了机器学习模型在犯罪预测中的主要技术路径。研究从预测对象和预测场景两个维度，重点分析了机器学习方法在犯罪趋势预测、犯罪热点预测、犯罪时空预测、犯罪类型识别以及犯罪嫌疑人预测及其落脚点预测等方面的应用，并归纳其在侵财、凶杀、金融及网络犯罪等典型领域的研究成果。结果表明，机器学习方法能够有效提升犯罪预测的准确性与实时性，为社会治安防控提供新思路。然而，当前研究仍面临数据稀疏性、时空相关性处理、区域划分合理性、模型可解释性、预测效果评价、伦理与隐私保护等方面的挑战。
Crime prediction serves as a technical approach to crime prevention. In recent years, with the rapid development of big data and artificial intelligence, machine learning methods have been increasingly applied to crime prediction, yielding substantial research progress. This paper provides a systematic review of research advancements in crime prediction based on machine learning. Grounded in environmental criminology, it summarizes the theoretical foundations and methodological evolution of crime prediction, and outlines the main technical pathways of machine learning models in this field. From the dual perspectives of prediction targets and prediction scenarios, this study focuses on the applications of machine learning in crime trend prediction, crime hotspot detection, spatiotemporal crime forecasting, crime type identification, as well as suspect and offender location prediction. It further synthesizes representative findings across major crime categories, including property crimes, homicides, financial crimes, and cybercrimes. The results indicate that machine learning approaches can significantly enhance the accuracy and timeliness of crime prediction, offering new insights for social security and crime prevention. Nevertheless, current research still faces challenges in data sparsity, spatiotemporal correlation processing, regional partitioning rationality, model interpretability, prediction performance evaluation, as well as ethical and privacy protection issues.
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