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

doi:10.18306/dlkxjz.2018.06.003

Abstract

Abstract:

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.

Key words: space-time kernel density, random forest algorithm, crime hotspot prediction, high crime areas identification

Lin LIU, Wenjuan LIU, Weiwei LIAO, Hongjie YU, Chao JIANG, Rongping LIN, Jiakai JI, Zheng ZHANG. Comparison of random forest algorithm and space-time kernel density mapping for crime hotspot prediction[J].PROGRESS IN GEOGRAPHY, 2018, 37(6): 761-771.

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References 33

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	热点网格数/个	总案件量/起	单个网格最大案件量	热点网格案件量中位数	热点网格案件量平均值
2016年6月份前2周	72	76	2	1	1.05
2016年6月份	140	164	6	1	1.17
2016年6、7月份	282	350	12	1	1.24
2016年6、7、8月份	415	554	20	1	1.33

	周期性	临近性
输入训练数据	2013年5月份案件量	2016年1月份案件核密度
	2014年5月份案件量	2016年2月份案件核密度
	2015年5月份案件量	2016年3月份案件核密度
	/	2016年4月份案件核密度
输入测试数据	2013年6月份案件量	2016年2月份案件核密度
	2014年6月份案件量	2016年3月份案件核密度
	2015年6月份案件量	2016年4月份案件核密度
	/	2016年5月份案件核密度
输入分类标签	2016年5月份热点/非热点	/
输出分类标签	/	2016年6月份热点/非热点

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

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