地理科学 ›› 2016, Vol. 36 ›› Issue (1): 55-62.doi: 10.13249/j.cnki.sgs.2016.01.007

• 研究论文 • 上一篇    下一篇

基于临近相似性考虑的犯罪热点密度图预测准确性比较——以DP半岛街头抢劫犯罪为例

徐冲1(), 柳林1,2(), 周素红1   

  1. 1.中山大学地理科学与规划学院综合地理信息研究中心,广东 广州 510275
    2.辛辛那提大学地理系,美国 辛辛那提 OH45221-0131
  • 收稿日期:2014-12-24 修回日期:2015-04-15 出版日期:2016-01-20 发布日期:2016-01-20
  • 作者简介:

    作者简介:徐冲(1985-),男,河南开封人,博士,主要从事城市犯罪和城市地理研究。E-mail: xcaiwd0123@163.com

  • 基金资助:
    国家自然科学基金项目(41171140、41271166)资助

The Comparison of Predictive Accuracy of Crime Hotspot Density Maps with the Consideration of the Near Similarity:A Case Study of Robberies at DP Peninsula

Chong Xu1(), Lin Liu1,2(), Suhong Zhou1   

  1. 1.School of Geography Science and Planning, Center of Integrated Geographic Information Analysis, Sun Yat-sen University, Guangzhou 510275, Guangdong, China
    2.Department of Geography, University of Cincinnati, OH45221-0131, USA
  • Received:2014-12-24 Revised:2015-04-15 Online:2016-01-20 Published:2016-01-20
  • Supported by:
    National Natural Science Foundation of China (41171140, 41271166)

摘要:

在无时空考虑的密度估计算法基础上,分别加入了案件点之间的时间临近相似性、空间临近相似性和时空临近相似性的考虑,利用DP半岛2006~2007年的街头抢劫犯罪数据为基础计算无时空临近相似性、时间临近相似性、空间临近相似性和时空临近相似性4种不同算法所得到的犯罪热点图,并以之预测2008年的街头抢劫。通过Natural breaks(Jenks)分级方法和等比例面积选取两种方式来划定热点区域进行预测并进行PAI指数得分比较,结果表明时空临近相似性的密度估计算方法在犯罪预测的优势比较显著。

关键词: 临近相似性, 犯罪制图, 密度估计, 街头抢劫

Abstract:

Crime hotspot mapping can forecast the occurrence of potential offense and direct the police activities to restrain the offense by revealing the spatial pattern of the offense. Density estimation usually presents the most significant outcome of the potential offense among all the hotspot mapping methods, for the street crimes in particular. Compared with the density estimation algorithm without the spatial or temporal near similarity, the temporal near similarity, spatial near similarity and spatio-temporal near similarity among offense points were illustrated in this article. With street robbery data of DP Peninsula during the period from 2006 to 2007, four different crime hotspot maps based on non-spatial-temporal near similarity, spatial near similarity, temporal near similarity and spatial-temporal near similarity were produced respectively. Moreover, with the validation data of 2008 cases,two different classification methods, Natural breaks (Jenks) classification and Equal proportional selection by area were utilized to delimit the hot area and then the comparisons among the scores of the Prediction Accuracy Index (PAI) of those four different crime hotspot maps were implemented. The results presented that the density estimation method based on the spatial-temporal near similarity holds significant advantages when predicting the potential offences.

Key words: near similarity, crime mapping, density estimation, street robbery

中图分类号: 

  • TU984