Predictive Hotspot Mapping for Data-Driven Crime Prediction
提出一种基于时空核密度估计的非参数模型，利用历史数据和专家输入预测犯罪热点，并在德里警局实际部署中验证了其辅助巡逻车辆调度的有效性。
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 toward predictive approaches relying on historical data. In this paper, we create a nonparametric model using a spatiotemporal 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.