微观尺度下毒品犯罪时空分布及成因分析——以SZ市NH、DM街道为例

doi:10.18306/dlkxjz.2020.05.012

Abstract

Abstract:

Drug crime is a criminal act of common concern in many places of the world. Many scholars have carried out research from different professional fields. The causes of drug-related crimes have been analyzed in the existing literature. Because of the limitations of data, there is little research on the spatial and temporal distribution of drug-related crime hotspots at the micro-scale. This study took NH and DM residential communities in SZ as an example. Based on the data of drug crime cases, this study used exploratory data analysis and spatiotemporal scanning to identify the distribution of hotspots of drug crimes. Then, by combining the spatial data of land use types and dynamic population flow, it quantitatively analyzed the influencing factors of the spatiotemporal distribution pattern of drug crime cases. The results show that: 1) Drug crimes mainly distribute in developed commercial areas and urban villages, and the hotspot distribution of drug crimes in urban villages occurred earlier than in developed commercial areas, and the scope of influence is larger in the former; 2) The distribution of drug crimes in different land types is uneven; the land use types of accommodation, tourism, and entertainment, wholesale and retail commercial department stores, and catering and business services are highly correlated with drug crimes. 3) The proportion of the area in hotspot areas with high flow of people has a certain correlation with the occurrence of drug crimes. When the proportion of hotspot areas is greater than 5% or 0, it can restrain the occurrence of drug-related crimes; when the proportion of hotspot areas is between 0 and 5%, it can promote the occurrence of drug-related crimes.

Key words: drug crime, land use type, dynamic population flow, crime geography

LIU Yimeng, LI Weihong, WANG Xin. Spatiotemporal distribution of drug crimes at micro-scale: Taking NH and DM residential communities in SZ City as an example[J].PROGRESS IN GEOGRAPHY, 2020, 39(5): 841-852.

Figures/Tables 12

Fig.1

Tab.1

Fig.2

Tab.2

Fig.3

Fig.4

Tab.3

Tab.4

Tab.5

Tab.6

Fig.5

Fig.6

References 32

[1]	梅建明 . 论环境犯罪学的起源、发展与贡献 [J]. 中国人民公安大学学报(社会科学版), 2006(5):66-72.
	[ Mei Jianming . On the origin, development and contribution of environmental criminology. Journal of the People's Public Security University of China (Social Sciences), 2006(5):66-72. ]
[2]	姜超, 唐焕丽, 柳林 . 中国犯罪地理研究述评 [J]. 地理科学进展, 2014,33(4):561-573. doi: 10.11820/dlkxjz.2014.04.013
	[ Jiang Chao, Tang Huanli, Liu Lin . Review of crime geography in China. Progress in Geography, 2014,33(4):561-573. ] doi: 10.11820/dlkxjz.2014.04.013
[3]	Weisburd D, Mazerolle L G . Crime and disorder in drug hot spots: Implications for theory and practice in policing [J]. Police Quarterly, 2000,3(3):331-349.
[4]	Sherman L W, Gartin P R, Buerger M E . Hot spots of predatory crime: Routine activities and the criminology of place [J]. Criminology, 1989,27(1):27-56.
[5]	李卫红, 童昊昕 . 针对非平衡警情数据改进的K-Means-Boosting-BP模型 [J]. 中国图象图形学报, 2017,22(9):1314-1324.
	[ Li Weihong, Tong Haoxin . Improved K-Means-Boosting BP model that targets unbalanced police intelligence data. Journal of Image and Graphics, 2017,22(9):1314-1324. ]
[6]	柳林, 张春霞, 冯嘉欣 , 等. ZG市诈骗犯罪的时空分布与影响因素 [J]. 地理学报, 2017,72(2):315-328.
	[ Liu Lin, Zhang Chunxia, Feng Jiaxin , et al. The spatial-temporal distribution and influencing factors of fraud crime in ZG City, China. Acta Geographica Sinica, 2017,72(2):315-328. ]
[7]	张宁, 王大为 . 基于风险地形建模的毒品犯罪风险评估和警务预测 [J]. 地理科学进展, 2018,37(8):1131-1139.
	[ Zhang Ning, Wang Dawei . Drug-related crime risk assessment and predictive policing based on risk terrain modeling. Progress in Geography, 2018,37(8):1131-1139. ]
[8]	李卫红, 闻磊, 陈业滨 . 改进的GA-BP神经网络模型在财产犯罪预测中的应用 [J]. 武汉大学学报(信息科学版), 2017,42(8):1110-1117.
	[ Li Weihong, Wen Lei, Chen Yebin . Property crime forecast based on improved GA-BP neural network model. Geomatics and Information Science of Wuhan University, 2017,42(8):1110-1117. ]
[9]	陈鹏, 疏学明 . 时空犯罪热点预测模型研究 [J]. 系统仿真学报, 2011,23(9):1782-1786.
	[ Chen Peng, Shu Xueming . Research about spatial-temporal forecasting of crime hotspots. Journal of System Simulation, 2011,23(9):1782-1786. ]
[10]	Hart T C, Miethe T D . Street robbery and public bus stops: A case study of activity nodes and situational risk [J]. Security Journal, 2014,27(2):180-193.
[11]	肖露子, 柳林, 周素红 , 等. ZG市工作日地铁站点扒窃案件的时空分布及其影响因素 [J]. 地理科学, 2018,38(8):1227-1234.
	[ Xiao Luzi, Liu Lin, Zhou Suhong , et al. Spatio-temporal pattern of pickpocketing at subway stations on weekdays of ZG City and its influencing factors. Scientia Geographica Sinica, 2018,38(8):1227-1234. ]
[12]	Qin X X, Liu L . Evaluating the relationships of bus transit with street and off-street robberies [J]. The Professional Geographer, 2016,68(2):1-11.
[13]	闻磊, 李卫红, 戴侃 , 等. 空间视角下中国城市财产犯罪成因分析 [J]. 测绘科学, 2015,40(8):152-157.
	[ Wen Lei, Li Weihong, Dai Kan , et al. Causes research of urban property crime in the spatial perspective in China. Science of Surveying and Mapping, 2015,40(8):152-157. ]
[14]	Stucky T D, Ottensmann J R . Land use and violent crime [J]. Criminology, 2009,47(4):1223-1264.
[15]	李卫红, 戴侃, 闻磊 . 顾及地理因素的犯罪地理目标模型改进方法 [J]. 测绘科学, 2015,40(7):86-91, 149.
	[ Li WeiHong, Dai Kan, Wen Lei . Improvement of criminal geographic targeting model considering geographic factors. Science of Surveying and Mapping, 2015,40(7):86-91, 149. ]
[16]	Braga AA, Papachristos A V, Hureau D M . The effects of hot spots policing on crime: An updated systematic review and meta-analysis [J]. Justice Quarterly, 2014,31(4):633-663.
[17]	Copes H . Routine activities and motor vehicle theft: A crime specific approach [J]. Journal of Crime and Justice, 1999,22(2):125-146.
[18]	Lu Y, Chen X . On the false alarm of planar K-function when analyzing urban crime distributed along streets [J]. Social Science Research, 2007,36(2):611-632.
[19]	徐冲, 柳林, 周素红 . DP半岛街头抢劫案件的临近重复发生模式 [J]. 地理研究, 2015,34(2):384-394.
	[ Xu Chong, Liu Lin, Zhou Suhong . Patterns of near-repeat street robbery in DP Peninsula. Geographical Research, 2015,34(2):384-394. ]
[20]	柳林, 杜方叶, 肖露子 , 等. 不同类型道路密度对公共空间盗窃犯罪率的影响: 基于ZG市的实证研究 [J]. 人文地理, 2017,32(6):32-38, 46.
	[ Liu Lin, Du Fangye, Xiao Luzi , et al. The density of various road types and larceny rate: An empirical analysis of ZG City. Human Geography, 2017,32(6):32-38, 46. ]
[21]	Toomey T L, Erickson D J, Carlin B P , et al. The association between density of alcohol establishments and violent crime within urbanneighborhoods [J]. Alcoholism: Clinical & Experimental Research, 2012,36(8):1468-1473.
[22]	Wang Z, Wu J, Yu B. Analyzing spatio-temporal distribution of crime hot-spots and their related factors in Shanghai, China[C]// 2011 19th International Conference on Geoinformatics. IEEE, 2011: 1-6. doi: 10.1109/GeoInformatics.2011.5981000.
[23]	Nelson A L, Bromley R D F, Thomas C J. Identifying micro-spatial and temporal patterns of violent crime and disorder in the British city centre [J]. Applied Geography, 2001,21(3):249-274.
[24]	毛媛媛, 丁家骏 . 抢劫与抢夺犯罪行为时空分布特征研究: 以上海市浦东新区为例 [J]. 人文地理, 2014,29(1):49-54.
	[ Mao Yuanyuan, Ding Jiajun . Study on spatial-temporal patterns of robbery and snatch: A case study of Pudong New Area, Shanghai. Human Geography, 2014,29(1):49-54. ]
[25]	Ceccato V, Haining R . Short and medium term dynamics and their influence on acquisitive crime rates in the transition states of Estonia, Latvia and Lithuania [J]. Spatial Analysis, 2008,1(3):215-244.
[26]	赵丹丹 . 盗三车类案件时空分布特征及预测: 以上海市长宁区为例[D]. 开封: 河南大学, 2017.
	[ Zhao Dandan . The spatial-temporal distribution characteristics and crime prediction of three classes car stolen: Take Changning District in Shanghai as an example. Kaifeng,China: Henan University, 2017. ]
[27]	徐嘉祥 . 基于环境犯罪学理论的入室盗窃时空分布研究: 以北京市主城区案件的分析为例 [J]. 人文地理, 2018,33(1):43-50.
	[ Xu Jiaxiang . Research on spatial-temporal distributions of burglary based on the environmental criminology: Based on analysis of crime in Beijing. Human Geography, 2018,33(1):43-50. ]
[28]	陈鹏, 李欣, 胡啸峰 , 等. 北京市长安街沿线的扒窃案件高发区分析及防控对策 [J]. 地理科学进展, 2015,34(10):1250-1258.
	[ Chen Peng, Li Xin, Hu Xiaofeng , et al. Clustering pattern analysis and prevention strategies to pickpocketing offence along the Chang'an Street in Beijing. Progress in Geography, 2015,34(10):1250-1258. ]
[29]	徐冲, 柳林, 周素红 , 等. 微观空间因素对街头抢劫影响的空间异质性: 以DP半岛为例 [J]. 地理研究, 2017,36(12):2492-2504.
	[ Xu Chong, Liu Lin, Zhou Suhong , et al. Spatial heterogeneity of micro-spatial factors' effects on street robberies: A case study of DP Peninsula. Geographical Research, 2017,36(12):2492-2504. ]
[30]	程连生, 马丽 . 北京城市犯罪地理分析 [J]. 人文地理, 1997,12(2):7-12.
	[ Cheng Liansheng, Ma Li . Geographical analysis of urban crime in Beijing. Human Geography, 1997,12(2):7-12. ]
[31]	刘大千, 宋伟, 修春亮 . 长春市“两抢两盗”犯罪的空间分析 [J]. 地理科学, 2014,34(11):1344-1352.
	[ Liu Daqian, Song Wei , XiuChunliang. Spatial analysis of the crime of "two robberies and two robbers" in Changchun. Scientia Geographica Sinica, 2014,34(11):1344-1352. ]
[32]	柳林, 姜超, 周素红 , 等. 城市入室盗窃犯罪的多尺度时空格局分析: 基于中国H市DP半岛的案例研究 [J]. 地理研究, 2017,36(12):2451-2464.
	[ Liu Lin, Jiang Chao, Zhou Suhong , et al. Spatial-temporal patterns of burglary at multiple scales: The case of DP Peninsula in H City, China. Geographical Research, 2017,36(12):2451-2464. ]

区域	面积/km²	居民面面积密度/(km²/km²)	流动人口密度/(万人/km²)	道路密度/(km/km²)	毒品犯罪数量/个
HR社区	0.2719	0.4386	7.3145	27.7034	397
HB社区	0.2366	0.3782	6.6923	36.0450	392
XX社区	0.2113	0.3746	7.3594	34.4651	618
JN社区	0.2647	0.3108	4.0139	42.4013	452
DM社区	0.1673	0.4446	5.8316	35.8341	153
JB社区	0.1496	0.4356	5.3669	46.8281	173
YY社区	0.2439	0.3217	0.5489	29.1530	261
LX社区	0.5615	0.1598	0.7651	34.4173	66
CD社区	0.0909	0.5629	3.8510	32.1671	75
WJ社区	0.2767	0.2004	4.7269	33.3276	227
LL社区	0.2687	0.3776	4.5917	34.2651	354
XN社区	0.1139	0.2606	2.9650	31.3794	77
LHQ社区	0.0925	0.3358	2.1895	32.7419	99
HP社区	0.1183	0.3238	1.6810	42.9612	79
YC社区	0.1118	0.2586	4.4269	47.4451	147
HC社区	0.4192	0.2363	1.8833	32.8779	87

毒品犯罪资料年度	平均最近邻距离/m	期望最近邻距离/m	最近邻指数	Z值
2009	18.13	36.04	0.503174^***	-25.485873
2010	20.37	41.85	0.486784^***	-23.563643
2011	27.90	46.14	0.604639^***	-15.883410
2012	26.35	49.44	0.532951^***	-17.394550
2013	30.63	50.21	0.609894^***	-13.981935
2014	32.84	52.59	0.624494^***	-13.128682
2015	34.22	54.64	0.626347^***	-12.686879
2016	45.14	66.37	0.680198^***	-8.781033
2017	47.45	71.96	0.659333^***	-8.371492
2018	48.13	71.38	0.674258^***	-8.220137
2009—2018年	6.03	16.56	0.364209^***	-74.830417

序号	用地类型	面积/km²	毒品犯罪数量/个	毒品犯罪率区位熵(Q_i)
1	党政机关政法团体	0.54	4	0.243934
2	科研开发教育机构	0.35	1	0.093903
3	医疗卫生保健福利	0.51	10	0.650487
4	新闻媒体文化体育	0.1	3	0.960384
5	水电建筑城建环保	0	0	0
6	金融保险邮政电信	0.82	24	0.959366
7	农林牧渔宠物园艺	0.05	1	0.642476
8	商业科技贸易公司	0.39	6	0.506511
9	纺织建材食品加工	0.01	0	0
10	机械化工电器制造	0	0	0
11	住宿旅游娱乐	2.45	128	1.723174
12	居民服务商务服务	1.76	71	1.327987
13	房产园区商务楼宇	6.54	175	0.880289
14	商业百货批发零售	1.92	68	1.168733
15	交通运输物流仓储	2.97	65	0.719284
16	餐饮经营服务品牌	2.72	86	1.042033

序号	用地类型	面积/km²	毒品犯罪数量/个	毒品犯罪率区位熵(Q_i)
1	党政机关政法团体	1.83	24	0.817773
2	科研开发教育机构	0.91	11	0.756084
3	医疗卫生保健福利	0.95	11	0.722779
4	新闻媒体文化体育	1.22	18	0.923064
5	水电建筑城建环保	0.15	0	0
6	金融保险邮政电信	1.94	21	0.675579
7	农林牧渔宠物园艺	0.14	0	0
8	商业科技贸易公司	0.59	7	0.737111
9	纺织建材食品加工	0.02	0	0
10	机械化工电器制造	0	0	0
11	住宿旅游娱乐	4.28	208	3.031710
12	居民服务商务服务	2.73	39	0.892887
13	房产园区商务楼宇	15.89	173	0.679480
14	商业百货批发零售	9.85	163	1.032380
15	交通运输物流仓储	5.79	63	0.678177
16	餐饮经营服务品牌	4.57	77	1.050990

序号	用地类型	面积/km²	毒品犯罪数量/个	毒品犯罪率区位熵(Q_i)
1	党政机关政法团体	0.97	9	0.521638
2	科研开发教育机构	1.83	3	0.092286
3	医疗卫生保健福利	1.42	21	0.835206
4	新闻媒体文化体育	0.45	4	0.501637
5	水电建筑城建环保	0	0	0
6	金融保险邮政电信	0.81	3	0.209177
7	农林牧渔宠物园艺	0.16	1	0.343831
8	商业科技贸易公司	0.02	0	0
9	纺织建材食品加工	0	0	0
10	机械化工电器制造	0	0	0
11	住宿旅游娱乐	3.85	149	2.176620
12	居民服务商务服务	2.02	20	0.557126
13	房产园区商务楼宇	4.27	62	0.818716
14	商业百货批发零售	2.79	51	1.028359
15	交通运输物流仓储	4.80	81	0.950060
16	餐饮经营服务品牌	3.08	66	1.207736

Spatiotemporal distribution of drug crimes at micro-scale: Taking NH and DM residential communities in SZ City as an example

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 32

Related Articles 4

Metrics

Comments

Recommended 0

影响因子		显著性水平
道路密度	-0.010	0.969
流动人口密度	0.653^**	0.006
居民面密度	0.262	0.327

[1]	LONG Dongping, LIU Lin, CHEN Jianguo, XIAO Luzi, SONG Guangwen, XU Chong. Impact of prior crime experiences of street robbers on subsequent crime location choices [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 815-828.
[2]	LIU ling, LI Gang, XUE Shuyan, MA Xueyao, ZHOU Junjun, XU Tingting, WANG Jiaobei. Spatiotemporal change and influencing factors of child trafficking crime in Sichuan Province [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 853-865.
[3]	JIANG Chao, TANG Huanli, LIU Lin. Review of crime geography in China [J]. PROGRESS IN GEOGRAPHY, 2014, 33(4): 561-573.
[4]	WANG Quanfang1, WANG Qian1, ZHANG Qipeng2, MEI Xin1 . Programming on Calculating Fractal Dimension of Land Use Types for 2D Vector Data [J]. PROGRESS IN GEOGRAPHY, 2010, 29(3): 259-265.