涉毒人员日常活动对盗窃警情空间格局影响的时间差异

doi:10.12082/dqxxkx.2021.210069

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (12): 2187-2200.doi: 10.12082/dqxxkx.2021.210069

• 地理空间分析综合应用 • 上一篇下一篇

涉毒人员日常活动对盗窃警情空间格局影响的时间差异

柳林¹^,²(), 孙秋远¹, 肖露子¹^,^*(), 宋广文¹, 陈建国¹

1.广州大学地理科学与遥感学院公共安全地理信息分析中心,广州 510006
2.辛辛那提大学地理系,辛辛那提 OH45221-1031,美国

收稿日期:2021-02-05 修回日期:2021-03-18 出版日期:2021-12-25 发布日期:2022-02-25
通讯作者: *肖露子（1991— ）,女,江西樟树人,博士,讲师,主要从事犯罪地理与时空行为分析。E-mail: xiaoluzi@gzhu.edu.cn
作者简介:柳林（1965— ）,男,湖南湘潭人,博士,教授,博导,主要从事犯罪地理及地理信息科学研究。E-mail: lin.liu@uc.edu
基金资助:
国家自然科学基金项目(41901177);国家自然科学基金项目(42001171);国家自然科学基金项目(42071184);广东省自然科学基金项目(2019A1515011065);广州市科技计划项目(201804020016)

The Temporal Influence Difference of Drug-related Personnels' Routine Activity on the Spatial Pattern of Theft

LIU Lin¹^,²(), SUN Qiuyuan¹, XIAO Luzi¹^,^*(), SONG Guangwen¹, CHEN Jianguo¹

1. Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
2. Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA

Received:2021-02-05 Revised:2021-03-18 Online:2021-12-25 Published:2022-02-25
Supported by:
National Natural Science Foundation of China(41901177);National Natural Science Foundation of China(42001171);National Natural Science Foundation of China(42071184);Natural Science Foundation of Guangdong Province, China(2019A1515011065);Key Project of Science and Technology Program of Guangzhou City, China(201804020016)

摘要/Abstract

摘要：

根据日常活动理论,犯罪时空格局与受害者、犯罪者日常活动规律均存在较强关系。但受限于数据获取难度,较缺乏有关犯罪者日常活动与实际警情时空格局的研究。已有文献表明涉毒人员与盗窃等财产犯罪存在较大相关性。基于此,本研究通过分析涉毒人员日常活动对盗窃警情时空格局的影响,验证犯罪者日常活动在塑造盗窃警情时空格局中的作用。本文以中国南部大城市ZG市XT派出所为例,以150 m×150 m的格网为分析单元,采用盗窃警情数据、涉毒人员日常活动数据、POI数据以及巡逻盘查数据,划分不同时间段分别建立泊松回归模型。研究发现：① 相对于传统静态的抓获或警情数据,动态的潜在犯罪者、受害者日常活动数据可更有效地提高盗窃模型的拟合优度;② 相对于全天汇总的总人数,动态近实时的涉毒人员活动与居民活动能更好地解释盗窃的空间分布;③ 静态的土地利用混合度在不同时段对盗窃具有不同的影响作用。以上结果验证了涉毒人员日常活动与盗窃警情的时空格局的关系,研究结论验证和丰富了日常活动理论,可为实际犯罪预测与警力布置提供一定的参考。

关键词: 盗窃警情, 潜在犯罪者, 涉毒人员, 日常活动理论, 时空格局, 泊松回归

Abstract:

According to the routine activity theory, the spatiotemporal pattern of crime is strongly related to routine activity of victims and offenders. However, due to the difficulty of data acquisition, there is a lack of research on offenders' routine activity and the spatiotemporal pattern of crime events. The existing literature shows that there is a great correlation between drug-related persons and property crimes such as theft. Based on this, this study verifies the role of the routine activity of offenders in shaping the spatial-temporal pattern of theft through analyzing the impact of the routine activity of drug-related persons on theft. In this paper, taking XT police district with 150 m×150 m grids in ZG city in southern China as an example, the theft data, routine activity data of drug-related persons, POI data, and patrol and interrogation data were used. Poisson regression models were established respectively in different periods. The results show that, firstly, compared with traditional static arrest or policing events data, active routine activity data of potential offenders and victims could promote goodness of fit in models effectively. Secondly, compared with total amount of people in whole day, active real-time activity data of drug-related personnel and residents could explain the spatial pattern of theft better. Thirdly, static land use density has a different influence on theft events in different periods. The above results verify the relationship between the routine activity of drug-related persons and the spatiotemporal pattern of theft. The research conclusions verify and enrich the routine activity theory, which can provide a certain reference for the actual crime prediction and police deployment.

Key words: theft events, potential offenders, drug-related persons, routine activity theory, spatiotemporal pattern, Poisson regression

柳林, 孙秋远, 肖露子, 宋广文, 陈建国. 涉毒人员日常活动对盗窃警情空间格局影响的时间差异[J]. 地球信息科学学报, 2021, 23(12): 2187-2200.DOI:10.12082/dqxxkx.2021.210069

LIU Lin, SUN Qiuyuan, XIAO Luzi, SONG Guangwen, CHEN Jianguo. The Temporal Influence Difference of Drug-related Personnels' Routine Activity on the Spatial Pattern of Theft[J]. Journal of Geo-information Science, 2021, 23(12): 2187-2200.DOI:10.12082/dqxxkx.2021.210069

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变量类型	变量名称	计算方法
因变量	盗窃警情	将2018年的盗窃警情地址通过地理编码生成坐标落点至XT派出所内382个网格中,统计各网格内警情数目
潜在犯罪者	涉毒人员日常活动	对于移动基站估算各个网格人口数据的方法^[24],本文根据94个摄像头的点坐标（其中4个摄像头坐标与其他摄像头位置完全重叠）建立90个泰森多边形,通过泰森多边形与研究区382个网格进行叠置,根据叠置后的面积占比统计各个网格内涉毒人员活动人次,从而将摄像头识别的涉毒人员日常活动信息较好地分配至研究区内所有网格
潜在受害者	微信活动人口	根据微信活动人口数据计算各个时段网格内活动人口数目
监管因素	巡逻盘查点	用0或1表示各个时段网格内是否有民警巡逻盘查
建成环境	土地利用混合度	根据POI数据通过信息熵公式计算网格内土地利用混合度
	公交车站	各网格内公交车站POI的个数
	休闲娱乐设施	各网格内休闲娱乐设施POI的个数
	农贸批发市场	各网格内农贸批发市场POI的个数
	购物设施	各网格内购物设施POI的个数

变量		平均值	方差	最小值	最大值
盗窃警情/起	07:00—10:00	0.141	0.206	0	4
	10:00—13:00	0.178	0.252	0	4
	13:00—16:00	0.194	0.252	0	3
	16:00—19:00	0.181	0.269	0	4
	07:00—19:00	0.694	1.751	0	8
	19:00—07:00	0.432	1.222	0	10
涉毒人员日常活动/百人次	07:00—10:00	0.213	0.171	0	3.958
	10:00—13:00	0.188	0.109	0	1.946
	13:00—16:00	0.200	0.144	0	2.538
	16:00—19:00	0.205	0.128	0	1.833
	07:00—19:00	0.806	2.007	0	9.675
	19:00—07:00	-	-	-	-
微信居民活动/千人次	07:00—10:00	0.398	0.232	0	2.481
	10:00—13:00	0.517	0.366	0	2.908
	13:00—16:00	0.495	0.336	0	2.705
	16:00—19:00	0.523	0.361	0	2.585
	07:00—19:00	1.933	5.072	0	10.650
	19:00—07:00	1.267	2.787	0	8.747
巡逻盘查点（0或1）	07:00—10:00	0.377	0.235	0	1
	10:00—13:00	0.518	0.250	0	1
	13:00—16:00	0.508	0.251	0	1
	16:00—19:00	0.476	0.250	0	1
	07:00—19:00	0.652	0.228	0	1
	19:00—07:00	0.545	0.249	0	1
土地利用混合度		0.029	0.0005	0	0.124
公交车站/个		0.154	0.341	0	5
休闲娱乐设施/个		0.236	0.622	0	10
农贸批发市场/个		0.325	1.180	0	8
购物设施/个		0.387	1.267	0	12

变量	静态数据（00:00—12:00 ）		活动数据（00:00—12:00）		白天（7:00—19:00 ）		夜晚-凌晨（19:00 —7:00）		上午（7:00—10:00）		中午（10:00 —13:00）		下午（13:00 —16:00）		傍晚（16:00—19:00）
	IRR	beta	IRR	beta	IRR	beta	IRR	beta	IRR	beta	IRR	beta	IRR	beta	IRR	beta
涉毒人员日常活动/百人			1.183	0.168^***	1.205	0.187^***	1.134	0.126^**	1.229	0.206^**	1.161	0.149^*	1.140	0.131^*	1.237	0.212^**
微信活动人口/千人			1.739	0.553^***	1.708	0.536^***	1.706	0.534^***	1.981	0.684^***	1.811	0.594^***	1.644	0.497^***	1.480	0.392^***
有无民警盘查	4.684	1.544^***	4.733	1.555^***	3.248	1.178^***	2.572	0.945^***	1.398	0.335^*	1.339	0.292	2.603	0.957^***	1.937	0.661^***
土地利用混合度	1.264	0.234^***	1.201	0.183^***	1.023	0.023	1.475	0.388^***	1.195	0.178	0.975	-0.025	1.047	0.046	1.024	0.024
公交站点/个	1.004	0.004	1.028	0.028	1.024	0.024	1.039	0.039	1.092	0.088	0.995	-0.005	0.994	-0.006	1.062	0.060
休闲娱乐设施/个	1.028	0.028	1.017	0.017	1.019	0.019	1.000	0.000	0.858	-0.154	1.124	0.117	1.053	0.051	0.939	-0.063
农贸批发市场/个	1.035	0.034	1.025	0.024	1.016	0.016	1.015	0.015	1.023	0.023	0.976	-0.025	1.003	0.003	1.059	0.058
购物设施/个	1.029	0.029	0.992	-0.008	1.034	0.033	0.913	-0.091	1.123	0.116	0.980	-0.020	1.026	0.026	1.117	0.110
历史毒品交易点/个	1.097	0.093^***
居民数目/千人	1.952	0.669^***
常量	0.307	-1.182^***	0.311	-1.168^***	0.248	-1.392^***	0.146	-1.926^***	0.063	-2.764^***	0.109	-2.217^***	0.077	-2.558^***	0.090	-2.411^***
最大VIF值	1.87		1.92		1.97		1.47		1.39		1.45		1.46		1.56
AIC	937.728		868.933		674.285		526.006		262.504		339.825		326.459		331.422
BIC	973.237		904.442		709.794		561.514		298.013		375.334		361.968		366.931

涉毒人员日常活动对盗窃警情空间格局影响的时间差异

The Temporal Influence Difference of Drug-related Personnels' Routine Activity on the Spatial Pattern of Theft

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