基于随机森林和时空核密度方法的不同周期犯罪热点预测对比

Comparison of random forest algorithm and space-time kernel density mapping for crime hotspot prediction

犯罪预测对于制定警务策略、实施犯罪防控具有重要意义。机器学习和核密度是2类主流犯罪热点预测方法,然而目前还鲜有研究对这2类方法在不同时间周期下的犯罪预测效果进行系统比较,本文试图对此进行补充。本文以2013-2016年5月的公共盗窃犯罪历史数据作为输入,分别对比了在接下来2周、1个月、2个月、3个月4个不同时间周期随机森林方法与基于时空邻近性的核密度方法的犯罪热点预测效果,结果发现:在各时间周期上,随机森林分类热点预测方法的面积和案件量命中率均比时空核密度方法准确性高;并且2种方法均能有效地识别犯罪热点中的高发区域,其中在较小范围较短时间内随机森林识别热点中的高发区效率更高,而在较大范围较长时间周期上时空核密度方法识别高发区更优。

Crime prediction is of great significance for the formulation of police tactics and the implementation of crime prevention and control in different time periods. Machine learning and density mapping are two common approaches for crime hotspot prediction. However, there exists few published work that systematically compares the predicted results of these two approaches. This study aimed to fill the gap. With crime patterns uncovered from 2013 to May 2016, we predicted hot-spot distribution of theft crimes in the period of first two weeks of June, July, and August in 2016 by random forest algorithm and traditional space-time kernel density method and compared the two sets of predictions. The research area was divided into grid cells of 50 m×50 m.Each cell was predicted as either hot-spot or non-hot-spot area in the next predicting period. Then we overlaid the forecast results and location of real cases to evaluate the accuracy of the two methods. The results show that both the hit rate of area and cases of the random forest classification hot-spot prediction method are higher than that of the space-time kernel density within different periods. Both methods can effectively identify high-crime areas of crime hot spots in prediction. In a relatively short period of time and small area, the random forest classification hotspot prediction method is more effective than the space-time kernel density method. However,in a relatively long term and large area, the space-time kernel density crime risk estimation method yields better result in identifying high crime areas.