用时空核密度模型预测犯罪热点，助力警力精准部署。
Predictive Hotspot Mapping for Data-driven Crime Prediction
- 基于历史数据构建非参数时空核密度模型
- 在德里警方合作下验证，提升警力调度效率
- 支持专家经验输入，适合城市治安研究者
犯罪热点预测是犯罪预测与管控中的关键问题。精准的热点映射有助于合理调配资源以管理城市犯罪。为实现数据驱动决策并自动化警务与巡逻，全球各地警方正转向依赖历史数据的预测方法。本文提出一种基于时空核密度估计的非参数模型，用于犯罪预测，并可融合来自人类专家的外部输入。该方法通过与德里警察部门合作，在真实场景中进行了广泛评估，旨在辅助巡逻车辆的有效分配以控制街头犯罪。实验结果表明，该方法具有良好的应用前景，可推广至其他场景。我们公开了研究中使用的算法和脱敏数据集，以支持未来研究，推动进一步改进。
原文摘要 · Abstract (English)
Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving towards predictive approaches relying on historical data. In this paper, we create a non-parametric model using a spatio-temporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.
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