地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (7): 929-938.doi: 10.12082/dqxxkx.2018.180036

• 地球信息科学理论与方法 • 上一篇    下一篇

融合历史犯罪数据的疑犯社会活动位置预测

段炼1,2(), 党兰学3,*(), 胡涛4, 朱欣焰4, 叶信岳5   

  1. 1. 广西师范学院 地理科学与规划学院,南宁 530001
    2. 广西师范学院 北部湾环境演变与资源利用教育部重点实验室,南宁 530001
    3. 河南大学 环境与规划学院,开封 475004
    4. 武汉大学 测绘遥感信息工程国家重点实验室,武汉 430079
    5. 肯特州立大学 地理学院,美国俄亥俄肯特,44240
  • 收稿日期:2018-01-16 修回日期:2018-04-16 出版日期:2018-07-20 发布日期:2018-07-13
  • 通讯作者: 党兰学 E-mail:wtusm@163.com;danglx@vip.henu.edu.cn
  • 作者简介:

    作者简介:段 炼(1981-),男,湖南祁阳人,博士,副教授,硕士生导师,研究方向为时空数据挖掘与犯罪时空预测。E-mail:wtusm@163.com

  • 基金资助:
    国家自然科学基金项目(41401524);广西自然科学基金项目(2015GXNSFBA139191);警用地理信息技术公安部重点实验室开放课题(2016LPGIT03);北部湾环境演变与资源利用教育部重点实验室系统基金(2014BGERLXT14);广西高校科学技术研究项目(KY2015YB189、KY2016YB281)

Mobility Prediction of Suspect Based on Historical Crime Records

DUAN Lian1,2(), DANG Lanxue3,*(), HU Tao4, ZHU Xinyan4, YE Xinyue5   

  1. 1. School of Geographical Sciences and Planning, Guangxi Teachers Education University, Nanning 530001, China
    2. Education Ministry Key Laboratory of Environment Evolution and Resources Utilization in Beibu Bay, Guangxi Teachers Education University, Nanning 530001, China
    3. College of Environment and Planning, Henan University, Kaifeng 475004, China
    4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    5. Department of Geography and Computational Social Science Lab, Kent State University, Kent, OH 44240, USA
  • Received:2018-01-16 Revised:2018-04-16 Online:2018-07-20 Published:2018-07-13
  • Contact: DANG Lanxue E-mail:wtusm@163.com;danglx@vip.henu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No.41401524;Guangxi Natural Science Foundation, No.2015GXNSFBA139191;Open Research Program of Key Laboratory of Police Geographic Information Technology, Ministry of Public Security, No.2016LPGIT03;Open Research Program of Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Guangxi Teachers Education University), Ministry of Education, No.2014BGERLXT14;Scientific Project of Guangxi Education Department , No.KY2015YB189, KY2016YB281

摘要:

由于重点跟踪人员(疑犯)的社会活动监控数据可获取性差,难以直接反映疑犯的社会活动时空模式,降低了案情分析和犯罪风险预测的有效性。为此,本文提出了融合犯罪记录的位置预测(Crime Records enhanced Location Prediction,CReLP)模型,将疑犯犯罪记录信息融入协同过滤算法,预测疑犯在未来对任意位置的访问频度。该方法利用张量(Tensor)表达疑犯在不同时段和位置上的访问频度,基于疑犯的犯罪事件数据构建疑犯时空关联度矩阵,利用该矩阵约束正则化的张量分解(Tensor Decomposition)过程,以解算出张量中的缺失值,进而获得各疑犯的潜在时空分布模式。实验采用包含了241个疑犯、1.9万个位置记录的真实疑犯位置数据集进行了模型测试,结果表明本文方法在均方根误差和 top-k 最小搜寻距离2个指标上都超过其他Baseline方法32%~63%和14%~26%,大幅提高了位置时空预测的有效性和健壮性。

关键词: 时空预测, 疑犯位置预测, 矩阵分解, 张量分解, 协同过滤

Abstract:

Suspect mobility prediction enables proactive experiences for location-aware crime investigations and offers essential intelligence to the crime initiative prevention. Recent practical studies and Rational Choice Theory suggest that the crime suspect mobility is predictable. The previous approaches for suspect location prediction focused on the forecasting the spatial likelihood of anchor point (i.e. the residential or future offending place) for a suspect who committed a series of crimes. However, the monitoring data are usually poor in availability for tracking suspects. Thus, the existing methodologies failed to capture the complex social location transition patterns for suspects and lacked the capacity to address the mobility data scarcity issue. Therefore, it is intractable to reflect suspects mobility patterns from sparse monitoring data, which reduces the effectiveness of case analysis and crime risk prediction. To address this challenge, we presented a novel Crime Records enhanced Location Prediction (CReLP) model. By merging the historical crime cases information by a collaborative filtering process, the CReLP model the estimate the visiting intensity at any arbitrary spatiotemporal node for and individual suspect. Particularly, we first obtained basic spatial and temporal units by partitioning the target areas into 100×100 2D grids and segmenting the daytime into 24 time bins. Second, we built a 3D tensor to model the social mobilities of all suspects with each entry in it representing the visiting intensity at each location and each time bin for each suspect. Meanwhile, this approach employed two matrices to express general movement trends among all suspects. Third, we created a suspect-correlation matrix relying on the spatial and temporal proximities of their historical crime events, as well as the similarities between their personal properties. At last, the missing entries in the 3D tensor were filled through the joint decomposition across all tensors and matrices mentioned above. This way were able to uncover the spatial distribution pattern for each suspect at any time. We evaluated the CReLP model using a real-world suspect mobility dataset collected from 241 suspects over 6 months with about 19 thousand location records. The results showed that our model outperformed three baseline approaches by 32% to 63% at RMSE (Root Mean Square Error) and 14% to 26% in TMSD (Top-k Minimum Search Distance), respectively. Finally, a suspect's visiting spatial distributions of the ground truth and predicting results between 8 to 12 a.m. were illustrated to show the performance of our proposed model.

Key words: location prediction, suspect spatiotemporal prediction, matrix decomposition, tensor decomposition, collaborative filtering