基于DBSCAN算法的A区犯罪预测

Crime prediction in area A based on DBSCAN clustering

为有效提升公安部门在实践工作中的犯罪预测能力,提出基于DBSCAN算法的A区犯罪预测方法。该方法采用了时空分析可视化技术和DBSCAN算法,对A区的犯罪数据进行分析。首先,对A区的犯罪数据进行描述性统计分析;然后,利用DBSCAN算法构建犯罪预测模型,并进行可视化处理;最后,通过对不同类型犯罪进行分析,预测犯罪热点,识别犯罪模式。实验结果表明,与传统的经验预测相比,该方法具有更好的预测效果,为公安机关打击犯罪和优化警力配置提供了决策依据。

In order to effectively improve the crime prediction ability of the public security department in practical work,this paper proposed a crime prediction method in area A based on DBSCAN algorithm.This method used the spatio-temporal analysis visualization technology and DBSCAN algorithm to analyze the crime data in area A.Firstly,this method conducted descriptive statistical analysis of the crime data in Area A;then,it used the DBSCAN algorithm to build a crime prediction model and visualized it;finally,it predicted the hot spots of crime and identify crime patterns by analyzing different types of crime.The results show that compared with traditional empirical prediction,this method has a better prediction effect and provides a decision basis for public security organs to fight crime and optimize police force allocation.