用随机森林预测城市犯罪高发时空，帮警方提前部署警力。
Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety
- 基于警情数据构建时空特征，用随机森林分类危险事件
- 本地化预测准确率达85%，假阴性极低，适合紧急响应
- 为警方资源调配提供可解释的智能支持，适合城市安全部门
近年来，城市安全已成为城市规划者和执法部门的核心关切。准确预测犯罪发生位置与时间，能显著提升预防措施与资源分配效率。然而，许多执法部门缺乏分析并应用先进人工智能与机器学习技术的工具，难以支持城市规划、治安监控和安全管理者采取主动行动。本文研究机器学习方法在预测城市区域犯罪时空模式中的有效性。利用美国圣何塞市的警察出警记录数据，研究目标是高精度地将警情分类为优先等级，特别是需要立即响应的高危情形。分类依据包括事件的时间、地点及性质。研究流程涵盖数据提取、预处理、特征工程、探索性数据分析、多种监督学习模型与神经网络的实现、优化与调参。在不同犯罪类别粒度与地理位置精度下评估模型的准确率与精确率。结果表明，相较于其他模型，随机森林分类器在本地层面识别高危情境及其对应优先级时表现最优，准确率达到85%，AUC为0.92，同时保持极低的假阴性率。尽管未来仍需纳入更多社会经济因素，当前结果已为执法部门优化资源配置、制定主动部署策略、调整响应模式提供了重要参考，助力实现公平、无偏的公共安全提升。
原文摘要 · Abstract (English)
In recent years, urban safety has become a paramount concern for city planners and law enforcement agencies. Accurate prediction of likely crime occurrences can significantly enhance preventive measures and resource allocation. However, many law enforcement departments lack the tools to analyze and apply advanced AI and ML techniques that can support city planners, watch programs, and safety leaders to take proactive steps towards overall community safety. This paper explores the effectiveness of ML techniques to predict spatial and temporal patterns of crimes in urban areas. Leveraging police dispatch call data from San Jose, CA, the research goal is to achieve a high degree of accuracy in categorizing calls into priority levels particularly for more dangerous situations that require an immediate law enforcement response. This categorization is informed by the time, place, and nature of the call. The research steps include data extraction, preprocessing, feature engineering, exploratory data analysis, implementation, optimization and tuning of different supervised machine learning models and neural networks. The accuracy and precision are examined for different models and features at varying granularity of crime categories and location precision. The results demonstrate that when compared to a variety of other models, Random Forest classification models are most effective in identifying dangerous situations and their corresponding priority levels with high accuracy (Accuracy = 85%, AUC = 0.92) at a local level while ensuring a minimum amount of false negatives. While further research and data gathering is needed to include other social and economic factors, these results provide valuable insights for law enforcement agencies to optimize resources, develop proactive deployment approaches, and adjust response patterns to enhance overall public safety outcomes in an unbiased way.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。