Explaining Street Contact Crime based on Dynamic Spatio-Temporal Distribution of Potential Targets

Lin LIU; Siyi LIANG; Guangwen SONG

doi:10.12082/dqxxkx.2020.190709

Journals >Journal of Geo-information Science >Volume 22 >Issue 4 >Page 887 > Article

Journal of Geo-information Science
Vol. 22, Issue 4, 887 (2020)

Explaining Street Contact Crime based on Dynamic Spatio-Temporal Distribution of Potential Targets

Lin LIU^{1、1、2、2、3、3、4、4、*}, Siyi LIANG^{1、1、2、2}, and Guangwen SONG^3、3

Author Affiliations

¹School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

¹中山大学地理科学与规划学院,广州 510275

²Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China

²广东省公共安全与灾害工程技术研究中心,广州 510275

³Center of Geographic Information Analysis for Public Security, School of Geographic Sciences, Guangzhou 510006, China

³广州大学地理科学学院公共安全地理信息分析中心,广州 510006

⁴Department of Geography, University of Cincinnati, Cincinnati OH 45221-0131, USA

⁴辛辛那提大学地理系,辛辛那提 OH 45221-0131

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DOI: 10.12082/dqxxkx.2020.190709 Cite this Article

Lin LIU, Siyi LIANG, Guangwen SONG. Explaining Street Contact Crime based on Dynamic Spatio-Temporal Distribution of Potential Targets[J]. Journal of Geo-information Science, 2020, 22(4): 887 Copy Citation Text

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Fig. 1. Land use map of the study area

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Fig. 2. Conceptual framework of explaining street contact crime based on routine activity theory

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Fig. 3. Hourly change of XT street contact crime count

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Fig. 4. Kernel density map of street contact crime and WeChat population for different time intervals

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变量	平均值	方差	最小值	最大值
街面接触型犯罪数量/件
00:00—06:59	1.16	7.34	0	29
07:00—17:59	1.12	2.77	0	9
18:00—23:59	1.08	4.50	0	15
微信人口（百人）
00:00—06:59	7.21	44.16	0.29	33.12
07:00—17:59	29.34	427.87	0.80	97.16
18:00—23:59	17.37	178.59	0.64	65.53
餐饮点/个	0.70	1.62	0	8
网吧/个	0.08	0.13	0	3
健身房/个	0.02	0.02	0	1
KTV/个	0.01	0.08	0	2
休闲会所/个	0.06	0.07	0	2
购物场所/个	1.73	8.88	0	17
公交站点/个	0.19	0.25	0	3
与最近巡逻驻点的距离/km	0.37	0.11	0	1.34
平均房屋价格/百万元	2.53	0.52	0.87	5.29
道路长度/km	0.70	0.50	0	6.76

Table 1. Descriptive statistics of dependent and independent variables

变量	凌晨—清晨（00:00—06:59）		白天（07:00—17:59）		晚上（18:00—23:59）
	B	IRR	B	IRR	B	IRR
常数	-2.05^*	0.13	-1.02	0.36	-0.85		0.42
微信人口	0.12^***	1.13	0.03^***	1.03	0.04^***		1.04
餐饮点	0.10	1.10	0.07	1.07	0.27^***		1.31
网吧	-0.15	0.86	0.22	1.25	-0.34		0.70
健身房	-0.31	0.73	0.99^*	2.69	0.08		1.08
KTV	1.22^**	3.38	0.02	1.02	0.89		2.44
休闲会所	0.84^*	2.31	0.22	1.25	0.77^*		2.16
购物场所	0.02	1.02	0.03	1.03	-0.01		0.99
公交站点	0.34	1.43	0.24	1.28	0.64^***		1.89
与最近巡逻驻点的距离	0.46	1.58	0.38	1.46	0.62^*		1.86
平均房屋价格	0.15	1.17	-0.16	0.86	-0.31		0.73
道路长度	0.11	1.11	0.07	1.07	0.00		1.00
AIC	612.18		628.35		578.95

Table 2. Negative binomial regression model for different time intervals of street contact crime

Lin LIU, Siyi LIANG, Guangwen SONG. Explaining Street Contact Crime based on Dynamic Spatio-Temporal Distribution of Potential Targets[J]. Journal of Geo-information Science, 2020, 22(4): 887

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