地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (4): 887-897.doi: 10.12082/dqxxkx.2020.190709

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基于潜在受害者动态时空分布的街面接触型犯罪研究

柳林1,2,3,4,*(), 梁斯毅1,2, 宋广文3   

  1. 1. 中山大学地理科学与规划学院,广州 510275
    2. 广东省公共安全与灾害工程技术研究中心,广州 510275
    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
    4. 辛辛那提大学地理系,辛辛那提 OH 45221-0131
  • 收稿日期:2019-11-21 修回日期:2020-02-03 出版日期:2020-04-25 发布日期:2020-06-10
  • 通讯作者: 柳林 E-mail:lin.liu@uc.edu
  • 作者简介:柳 林(1965— ),男,湖南湘潭人,博士,教授,博导,主要从事犯罪空间模拟、多智能体模拟、GIS应用等研究。
  • 基金资助:
    广州市科技计划项目(201804020016);国家重点研发计划项目(2018YFB0505500);国家重点研发计划项目(2018YFB0505503);国家自然科学基金重点项目(41531178);广东省自然科学基金研究团队项目(2014A030312010);国家自然科学基金项目(41901177);广东省自然科学基金项目(2019A1515011065)

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

LIU Lin1,2,3,4,*(), LIANG Siyi1,2, SONG Guangwen3   

  1. 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    2. Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
    3. Center of Geographic Information Analysis for Public Security, School of Geographic Sciences, Guangzhou 510006, China
    4. Department of Geography, University of Cincinnati, Cincinnati OH 45221-0131, USA
  • Received:2019-11-21 Revised:2020-02-03 Online:2020-04-25 Published:2020-06-10
  • Contact: LIU Lin E-mail:lin.liu@uc.edu
  • Supported by:
    Key Project of Science and Technology Program of Guangzhou City, China(201804020016);National Key Research an Development Program of China(2018YFB0505500);National Key Research an Development Program of China(2018YFB0505503);Key Program of National Natural Science Foundation of China(41531178);Research Team Program of Natural Science Foundation of Guangdong Province, China(2014A030312010);National Natural Science Foundation of China(41901177);Natural Science Foundation of Guangdong Province, China(2019A1515011065)

摘要:

街面接触型犯罪是指犯罪者在街面通过采取与受害者身体接触的方式而实施的违反法律的行为,已有文献研究利用了不同类型的大数据代表的周遭人口表征街面接触型犯罪中“潜在受害者”因素,但由于数据的局限性,无法应用在微观的空间尺度上的街面接触型犯罪研究。微信热力图是具有高时空分辨率和高人口覆盖度,能动态地反映人流量热度的程序。因此,本文以经济发达的ZG市的XT街道为例,结合日常活动理论,并基于微信热力图数据代表的周遭人口表征的“潜在受害者”因素,首先定性地描述和识别街面接触型犯罪的时空分布特征,然后划分不同时段分析街道街面接触型犯罪的影响因素。研究发现:① 街面接触型犯罪案件存在时空的集聚性,街道街面接触型犯罪在晚上(18:00—23:59)是高发期,在白天(07:00—17:59)是低发期,在22:00—22:59数量达到最大值,主要聚集在城中村区域,且不同时期的影响因素存在一定的差异;② 微信人口数量在所有时期均对街面接触型犯罪存在显著的正向影响,其代表的周遭人口能很好地表征日常活动理论中的“潜在受害者”因素,且在凌晨—清晨(00:00—06:59)对街面接触型犯罪的影响最大;③ 不同场所对街面接触型犯罪的影响存在时间上的差异,餐饮点在晚上对街面接触型犯罪存在显著的正向影响,KTV、健身房和公交站点分别对应在凌晨—清晨、白天与晚上对街面接触型犯罪有显著的正向影响,而休闲会所在凌晨、清晨、晚上均有显著的影响,与最近巡逻驻点的距离仅在晚上时期显著影响街面接触型犯罪。本文的研究结论可为警方采用微信热力图来分析街面接触型犯罪和经济发达地区警方部署提供参考依据。

关键词: 犯罪地理, 街面接触型犯罪, 微信热力图, 日常活动理论, 时空分布, 周遭人口, 潜在受害者, 负二项回归模型

Abstract:

Street contact crime refers to violations of the law committed by offenders through directly contact with victims in the street such as pickpocketing, robbery and snatch, etc, which is one of the common crimes in China. It is assumed that street contact crime is the result of interaction among motivated offenders, potential targets and absence of capable guardians. Different types of big data are employed in previous studies as ambient population to represent the potential targets which is one of the essential elements in the routine activity theory. However, these types of big data can not be applied in a micro-scale study of street contact crime because of their limitations. This study aims to fill this gap by using a new type of big data, WeChat heat map, an internet application which shows demographic distribution and changes dynamically with high spatial-temporal resolution to study the street contact crime in XT, ZG city, based on dynamic spatio-temporal distribution of potential Targets. The spatio-temporal pattern of street contact crime in XT, ZG city and their influencing factors were revealed. Street contact crime data, Points of Interest (POI) and data of house prices in XT, ZG city were used in this study as well. The whole day is divided into three intervals (wee hours: 00:00-06:59, daytime:07:00-17:59, night:18:00-23:59) and negative binomial regression models are built for the three intervals accordingly. It is demonstrated that the spatio-temporal distribution of street contact crime in XT, ZG city aggregates obviously. Street contact crime in XT, ZG city mainly concentrate in urban village and night is the peak period while daytime is the low period. The count of street contact crime in XT, ZG city reach its maximum between 22:00 and 22:59. Factors have different impacts on street contact crime from interval to interval. During the wee hours, WeChat population,KTV and leisure Club have significant positive impact on street contact crime. In the daytime, WeChat population and gym have significant positive impact on street contact crime. At night, WeChat population, restaurants, Leisure Club, bus station and distance to the nearest security department have significant positive impact on street contact crime. Others factors such as internet café, shopping mall, house prices and length of road have no significant impact on street contact crime in the whole day. WeChat population as an ambient population represent the potential targets well in routine activity theory as it has significant positive impact on street contact crime in the whole day.

Key words: crime geography, street contact crime, WeChat heat map, routine activity theory, spatio-temporal distribution, ambient population, potential targets, negative binomial regression