ZG市工作日地铁站点扒窃案件的时空分布及其影响因素

以东南沿海城市ZG市为例,分析工作日地铁扒窃案件的时空分布特征,并进一步结合日常活动理论,探讨其形成机理。研究发现：① 时空分布上,地铁扒窃案件存在早晚2 个峰值,但滞后于地铁客流量高峰;白天,地铁扒窃主要集中在中心城区,在早晚时段,除中心城区外,地铁扒窃在城郊地区也有较多分布。② 影响因素上,地铁扒窃主要受到客流量及建成环境的影响,不同时段影响的因素存在差异。客流量及换乘点在地铁运营的任意时段均对地铁扒窃有正向的影响,地铁站周边的休闲场所对地铁扒窃犯罪的影响主要体现在9:00以后的时段。居住地及工作地虽然整体上对地铁扒窃没有显著影响,但是他们对地铁扒窃案件的作用方向在各时段模型中的作用力度均相反。③ 影响强度上,客流量在不同时段对地铁扒窃的影响强度存在差异,而换乘点及休闲场所在显著的时间段对地铁扒窃的影响力度并无明显差别。

Spatio-temporal Pattern of Pickpocketing at Subway Stations on Weekdays of ZG City and Its Influencing Factors

Subway is one of the key transport modes in cities due to its fast and convenient system. At the same time, the criminal events that occur in the subway stations and subway trains should not be neglected. Although there has been a certain amount of literature emphasized the impact of environmental design on pickpocketing in subway systems, these studies ignored that residents’ daily activities may also act as important factors on subway crimes. In order to fill this gap, this article chose ZG city as a case, the spatio-temporal pattern of pickpocketing in the subway stations on weekdays and their influencing factors were revealed based on residents’ routine activity, environmental design in the subway system and the activity facilities around subway stations. Pickpocketing data in subway stations of ZG city, points of interest(POI) nearby subway stations, and detail information of subways stations (spatial distribution and structure of subway stations) were used in this study. In order to find out the spatio-temporal pattern of pickpocketing, we separated a set of full weekdays into four periods based on the passenger traffic, and allocated pickpocket cases to every subway station for each time period. Then, negative binomial regression models were used to find out the influencing factors on the spatio-temporal pattern on pickpocketing. Furthermore, Wald tests were used to compare whether there was a significant difference between the same independent variables in each model. The results have shown that: 1) From the perspective of spatio-temporal distribution, rates of pickpocketing were highly correlated to the passenger traffic in subways, but it should be aware of that the peak pickpocketing lagged behind the peak passenger traffic. As time went on in the morning, pickpocketing in the subway transferred from the suburbs to the city center, while the working time finished, the direction of pickpocketing transferred reversely. 2) Passenger traffic in subways had a steady significant positive impact on pickpocketing. In the meantime, subways surrounded by more leisure activity places were more vulnerable to be victimized. 3) Passenger traffic in subways had different degree of influence at different time, while other variables had no significant difference. From the above results we can conclude that there was spatio-temporal agglomeration of pickpocketing cases in subway stations, and this agglomeration changed as time went by during the day. This agglomeration was caused by the residents’ daily activity, subways stations that were with more passenger traffic and more active facilities were more likely to become criminal places. In addition, factors were not equally contributed to crime events at different period of the day. Our results may help understand the relationship between residents’ activity and pickpocketing in subway systems more thoroughly. The conclusions can also be applied in the policing practice.