地理研究 ›› 2017, Vol. 36 ›› Issue (12): 2451-2464.doi: 10.11821/dlyj201712015

• 犯罪地理专栏 • 上一篇    下一篇

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

柳林1,2,3(), 姜超1, 周素红1,2, 刘凯1,2(), 徐冲4, 曹晶晶1   

  1. 1. 中山大学地理科学与规划学院综合地理信息研究中心,广州 510275
    2. 广东省城市化与地理环境空间模拟重点实验室,广州 510275
    3. 辛辛那提大学地理系,辛辛那提 OH45221-0131,美国
    4. 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
  • 收稿日期:2017-06-09 修回日期:2017-09-06 出版日期:2017-12-15 发布日期:2018-01-18
  • 作者简介:

    作者简介:柳林(1965- ),男,湖南湘潭人,教授,博士生导师,主要研究方向为人文地理信息科学、犯罪时空分析与模拟。E-mail:lin.liu@uc.edu

  • 基金资助:
    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(41522104,41171140,41601138);广东省科技计划项目(2015A020217003)

Spatial-temporal patterns of burglary at multiple scales: The case of DP peninsula in H city, China

Lin LIU1,2,3(), Chao JIANG1, Suhong ZHOU1,2, Kai LIU1,2(), Chong XU4, Jingjing CAO1   

  1. 1. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
    2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
    3. Department of Geography, University of Cincinnati, Cincinnati OH45221-1031, Ohio, USA
    4. Center of Geographic Information Analysis for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, China
  • Received:2017-06-09 Revised:2017-09-06 Online:2017-12-15 Published:2018-01-18

摘要:

以中国H市DP半岛为例,分析城市入室盗窃犯罪的多尺度时空格局特征与成因,以探索推进区域内犯罪者画像研究。基于标准化犯罪强度指数和核密度估计的分析表明,DP半岛在中部和西部具有两个相对稳定的犯罪热点;但在年内月尺度上,年末犯罪高发并向交通可达性较好的商品房社区集聚;月内日尺度上,犯罪热点呈现出“振荡式”空间转移;日内时尺度上,犯罪热点沿道路进行空间转移,夜间覆盖范围最广。基于时空格局成因理论和实地调研资料的分析表明,DP半岛内以“理智型”入室盗窃者为主,主体与环境因素在不同时空区位上的耦合差异导致了犯罪空间格局的演变。总体来看,基于案件数据的多尺度犯罪时空格局研究,能够揭示特定区域内犯罪者的行为特征。

关键词: 入室盗窃, 时空格局, 多尺度, 犯罪热点, 犯罪地理学

Abstract:

With an aim of exploring main behavioral patterns of criminals in a specific region, this research analyzed the spatial-temporal distribution and shift patterns of urban burglary hotspot in the DP peninsula of H city, China. Calls for service data on burglary crime during 2006-2010 were obtained from the Public Security Bureau of H city, and several field investigations on residents' behavior and geographic environment were carried out. Based on the 1068 burglary incidents geocoded in space, the temporal, spatial, and spatial-temporal patterns of burglary were depicted with standardized crime intensity index and kernel density estimation. Thereafter, a theoretical framework for analyzing the causes of these spatial-temporal patterns of burglary was constructed, which was then examined based on the materials collected from field investigations. Apart from qualitative analysis, the box-plot was used to discern the impact of "attractiveness" and "accessibility" on burglary occurrence. The empirical results showed that burglary incidents were not evenly distributed in time and space in the DP peninsula, and obvious spatial-temporal patterns of crime shift at multiple scales can be consistently observed. At the "month of year" scale, burglaries were most concentrated in commercial communities along the main roads at the end of the year, while at the "time of day" scale, the burglary hotspots shifted along the roads as time goes on. At the "day of month" scale, two crime shift patterns can be clearly observed around the main hotpots in the western and central parts of the DP peninsula. In particular, the centripetal distribution of shifting crime hotspots in space highly resembles the shift patterns of individual burglars' movement patterns. These spatial-temporal crime patterns indicated that most burglars in DP peninsula were rational, as these patterns were generated from the heterogeneous couplings of agents and environment in different spatial-temporal locations. Most burglaries happened in the communities with relatively high expected value and relatively low guardianship level. The conjunctive analysis of the impacts of "attraction" and "accessibility" showed that the accessibility factor played a much more important role in the occurrence of burglary, which further demonstrated the critical role of "opportunity" for potential burglars. Overall, this study showed the promise of criminal profiling based on aggregate crime incidents data in a specific region.

Key words: burglary, spatial-temporal pattern, multi-scale, crime hotspot, crime geography