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

doi:10.13249/j.cnki.sgs.2016.01.007

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

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.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 Liu^1,²(), Suhong Zhou¹

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/Abstract

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

徐冲, 柳林, 周素红. 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[J]. 地理科学, 2016, 36(1): 55-62.

Chong Xu, Lin Liu, Suhong Zhou. 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[J]. SCIENTIA GEOGRAPHICA SINICA, 2016, 36(1): 55-62.

图/表 5

图1

图2

表1

图3

表2

参考文献 21

[1]	Brantingham P J, Brantingham P L.Patterns in crime[M]. New York: Macmillan, 1984.
[2]	Cohen L E, Felson M.Social change and crime rate trends: A routine activity approach[J]. American Sociological Review, 1979, (44): 588-608. doi: 10.2307/2094589
[3]	Cornish Derek B, Ronald V Clarke.The reasoning criminal: Rational choice perspectives on offending[M]. New York: Springer, 2014.
[4]	Osborne D, Wernicke S.Introduction to crime analysis: Basic resources for criminal justice practice[M]. London: Psychology Press, 2003.
[5]	Home Office of UK. Crime mapping: improving performance, a good practice guide for front line officers[R/OL]. London: Home Office. Available online at: .
[6]	刘大千, 修春亮,于嘉.长春市财产犯罪的空间分析[J].地理科学, 2012, 32(4): 477-484.
	[Liu Daqian, Xiu Chunliang, Yu Jia.Spatial analysis of property crimes in Changchun. Scientia Geographica Sinica, 2012,32(4): 477-484.]
[7]	严小兵. 中国省域刑事犯罪率的时空演变及机制研究[J]. 地理科学, 2013,33(5): 529-537.
	[Yan Xiaobing.Spatio-temporal pattern of crime rates in China. Scientia Geographica Sinica, 2013, 33(5): 529-537.]
[8]	徐冲, 柳林, 周素红,等.DP半岛街头抢劫犯罪案件热点时空模式研究[J].地理学报, 2013, 68(12):1714-1723. doi: 10.11821/dlxb201312011
	[Xu Chong, Liu Lin, Zhou Suhong.The spatio-temporal patterns of street robbery in DP peninsula. Acta Geographica Sinica, 2013, 68(12): 1714-1723.] doi: 10.11821/dlxb201312011
[9]	冯健, 黄琳珊,董颖. 城市犯罪时空特征与机制: 以北京城八区财产类犯罪为例[J]. 地理学报, 2012, 67(12): 1645-1656.
	[Feng Jian, Huang Linshan, Dong Ying.Research on the Spatial-temporal Characteristics and Mechanism of Urban Crime: A Case Study of Property Crime in Beijing. Acta Geographica Sinica, 2012, 67(12):1645-1656.]
[10]	Chainey S, Tompson L, Uhlig S.The utility of hotspot mapping for predicting spatial patterns of crime[J]. Security Journal, 2008, 21(1): 4-28. doi: 10.1057/palgrave.sj.8350066
[11]	Gorr W, Olligschlaeger A.Crime hotspot forecasting: Modelling and comparative evaluation[J]. Washington, DC: National Institute of Justice, 2002.
[12]	Gorr W, Olligschlaeger A, Thompson Y.Short-term forecasting of crime[J]. International Journal of Forecasting, 2003, 19(4): 579-594. doi: 10.1016/S0169-2070(03)00092-X
[13]	Groff E R, La Vigne N G. Forecasting the future of predictive crime mapping[J]. Crime Prevention Studies, 2002, 13: 29-58.
[14]	Ratcliffe J H, Rengert G F.Near-repeat patterns in Philadelphia shootings[J]. Security Journal, 2008, 21(1): 58-76.
[15]	Wells W, Wu L, Ye X.Patterns of near-repeat gun assaults in Houston[J]. Journal of Research in Crime and Delinquency, 2012, 49(2): 186-212. doi: 10.1177/0022427810397946
[16]	徐冲,柳林,周素红.DP半岛街头抢劫犯罪的临近重复发生模式研究[J].地理研究,2015,34(2):384-394. doi: 10.11821/dlyj201502017
	[Xu Chong, Liu Lin, Zhou Suhong.Patterns of near-repeat street robbery in DP peninsula. Geographical Research, 2015, 34(2): 384-394.] doi: 10.11821/dlyj201502017
[17]	Liu L, Wang X, Eck J et al. Simulating crime events and crime patterns in RA/CA model[M]//Wang Fahui. Geographic information systems and crime analysis. Singapore: IGI Global, 2005: 197-213.
[18]	West M.Kernel Density Estimation and Marginalization Consistency[M]. Biometrika,1991,78(2):421-425.
[19]	Xie Z X, Yan J.Kernel Density Estimation of traffic accidents in a network space Computers[J]. Environment and Urban Systems,2008,32(5): 396-406.
[20]	Slocum T A, McMaster R B, Kessler F C, et al. Thematic cartography and geovisualisation[J]. New Jersey, NJ: Prentice Hall, 2009.
[21]	Brewer C A.Cartography: thematic map design[J]. Cartographic Perspectives, 1994, (17): 26-27.

	3级分类			4级分类				5级分类
	08案件		栅格数	PAI	08案件	栅格数	PAI	08案件	栅格数	PAI
无时空	22	3834	3.65	17	1180	9.17		13	683	12.11
时间	17	1380	7.84	13	1110	7.45		5	256	12.43
空间	29	1674	11.02	21	1173	11.39		12	692	11.03
时空	21	1141	11.71	17	662	16.34		13	450	18.37

	0.5%			1%		5%			10%
	08案件		PAI指数		08案件	PAI指数	08案件	PAI指数	08案件	PAI指数
无时空	4	11.27		10	14.08	21	5.92		35	4.93
时间	5	14.08		7	9.86	22	6.20		32	4.51
空间	2	5.63		4	5.63	23	6.48		46	6.48
时空	6	16.90		13	18.31	28	7.89		43	6.06

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

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

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 21

相关文章 9

Metrics

本文评价

推荐阅读 0

[1]	王颖志,王立君. 基于网络时空核密度的交通事故多发点鉴别方法[J]. 地理科学, 2019, 39(8): 1238-1245.
[2]	刘峻峰,李巍,王绍博. 中国金融抑制水平测度及时空动态演变特征[J]. 地理科学, 2019, 39(7): 1102-1110.
[3]	司增绰, 周坤, 仇方道, 邵军. 中国对“一带一路”沿线国家出口增长的边际特征时空变化[J]. 地理科学, 2018, 38(11): 1777-1787.
[4]	周素红, 谢蔚翰, 宋广文, 柳林. 土地利用对街头抢劫影响的空间分异模式：以H市为例[J]. 地理科学, 2017, 37(6): 885-894.
[5]	孙才志, 覃雄合, 李博, 王泽宇. 基于WSBM模型的环渤海地区海洋经济脆弱性研究[J]. 地理科学, 2016, 36(5): 705-714.
[6]	王泽宇, 张震, 韩增林, 孙才志. 中国15个副省级城市经济转型成效测度及影响因素分析[J]. 地理科学, 2015, 35(11): 1388-1396.
[7]	陈晨, 修春亮, 陈伟, 李程洋. 基于GIS的北京地名文化景观空间分布特征及其成因[J]. 地理科学, 2014, 34(4): 420-429.
[8]	郭腾云, 董冠鹏. 京津冀都市区经济分布演化及作用机制模拟研究[J]. 地理科学, 2012, 32(5): 550-556.
[9]	刘锐, 胡伟平, 王红亮, 吴驰, 何劲. 基于核密度估计的广佛都市区路网演变分析[J]. 地理科学, 2011, 31(1): 81-86.