地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (6): 827-836.doi: 10.12082/dqxxkx.2018.180009

• 2017年中国地理信息科学理论与方法学术年会优秀论文专辑 • 上一篇    下一篇

点模式条件下的犯罪嫌疑人时空同现模式挖掘与分析

李智(), 李卫红()   

  1. 华南师范大学地理科学学院,广州 510631
  • 收稿日期:2017-12-28 修回日期:2018-02-04 出版日期:2018-06-20 发布日期:2018-06-20
  • 作者简介:

    作者简介:李 智(1992-),男,硕士生,主要从事时空数据挖掘与应用研究。E-mail: thomasleeds@m.scnu.edu.cn

  • 基金资助:
    公安部科技强警基础工作专项项目(2016GABJC47).

Mining and Analyzing Spatiotemporal Co-occurrence Patterns among Criminal Suspects under Point Pattern

LI Zhi(), LI Weihong()   

  1. School of Geography, South China Normal University, Guangzhou 510631, China
  • Received:2017-12-28 Revised:2018-02-04 Online:2018-06-20 Published:2018-06-20
  • Supported by:
    Technical Strong Police of the Ministry of Public Security of China, No.2016GABJC47

摘要:

现有时空同现模式挖掘方法因其在空间和时间频繁阈值等参数值的设定上存在困难且缺乏客观依据的问题而难以被应用到犯罪地理研究中。为此,本文通过引入时空状态同现模式和最小时空参与率等概念对现有挖掘方法进行了重新建模,并结合广义Grubbs异常值检验提出了一种点模式分布下的犯罪嫌疑人时空同现模式挖掘框架。基于该框架对中国某省部分犯罪嫌疑人的真实移动轨迹数据的实验分析结果表明,本文所提出的方法能够有效地挖掘出嫌疑人间显著的时空同现模式,且这些模式的时空分布特征不仅与犯罪活动易发生在非农业生产区这一共识基本相符,还与日常活动理论的基本观点相适应。本文拓展了时空同现模式挖掘在犯罪地理研究中的应用,研究成果对公安机关等执法部门在重点监控某些犯罪嫌疑人以及合理分配和部署警力资源方面具有重要意义。

关键词: 犯罪嫌疑人, 移动轨迹, 时空同现, 异常检验, 时空分布

Abstract:

Spatiotemporal co-occurrence patterns represent subsets of different object-types whose instances are frequently located together in both space and time. Using movement data to mine and analyze spatiotemporal co-occurrence patterns among diverse criminal suspects not only can help us better understand those unusual moving behaviors and relationships of them, but also provide decision-making supports for police departments in key suspects monitoring or arresting. Therefore, such pattern is one of the most important and useful way for the geography of crime researchers and police officers to extract and comprehend the implicit knowledge in large police databases which hold a large amount of crime data with spatiotemporal information. Additionally, to some extent, mining spatiotemporal co-occurrence patterns can also assist the police departments to save the limited police resources and improve their efficiency of handling criminal cases. However, current methods for mining spatiotemporal co-occurrence patterns can hardly be applied to the geography of crime studies directly because the way of determining spatial and temporal prevalence thresholds is presently difficult and lack of objectivity. Thus, in this paper, a novel candidate spatiotemporal co-occurrence pattern mining model was first built based on the spatiotemporal status co-occurrence pattern and the minimum spatiotemporal participation rate. Then, a framework for mining spatiotemporal co-occurrence patterns among criminal suspects under the point distribution was given through combining our proposed model and generalized ESD test. Finally, based on the proposed framework, a real case study in a province of China was conducted with an amount of real trajectory data of two criminal type (fraud and theft). The result shows that our proposed method is feasible in mining and analyzing the spatiotemporal co-occurrence patterns among criminal suspects. Specifically, 219 candidate spatiotemporal co-occurrence patterns were discovered under the condition that spatial neighbor distance equals to 688 meters and temporal neighbor distance equals to 504 seconds, and 6 of them were identified as the spatiotemporal co-occurrence patterns under the condition that significance level equals to 0.05. Importantly, the spatiotemporal distributions of those detected spatiotemporal co-occurrence patterns are not only approximately consistent with the common sense that criminal activities are more common in non-agricultural production areas, but also conform to the basic viewpoints of routine activity theory. This research expands the application of spatiotemporal co-occurrence pattern mining method to the geography of crime studies, and the study result can play an important role for police departments in key suspects monitoring and police resources allocation and deployment.

Key words: criminal suspects, trajectory data, spatiotemporal co-occurrence, outlier test, spatiotemporal distributions