TY - JOUR
T1 - 融合历史犯罪数据的疑犯社会活动位置预测
AU - Duan, Lian
AU - Dang, Lanxue
AU - Hu, Tao
AU - Zhu, Xinyan
AU - Ye, Xinyue
N1 - Funding Information:
2018-01-16; : 2018 -04-16. 国家自然科学基金项目 ( 41401524 ) ; 广西自然科学基金项目 ( 2015 GXNSFBA139191 ) ; 警用地理信息技术公安部 重点实验室开放课题 ( 2016 LPGIT03 ) ; 北部湾环境演变与资源利用教育部重点实验室系统基金 ( 2014 BGER-LXT14 ) ; 广西高校科学技术研究项目 ( KY 2015YB189、 KY 2016YB281 ) 。 [ Foudation items: National Natural Sci-enceFoundationofChina,No.41401524;GuangxiNaturalScienceFoundation,No.2015GXNSFBA139191;OpenRe-search Program of Key Laboratory of Police Geographic Information T echnology , Ministry of Public Security , No.2016LPGIT03;OpenResearchProgramofKeyLaboratoryofEnvironmentChangeandResourcesUseinBeibu Gulf (Guangxi T eachers Education University), Ministry of Education, No.2014BGERLXT14; Scientific Project of GuangxiEducationDepartment,No.KY2015YB189,KY2016YB281.] 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 党兰学 ( 1980 - ) , 男, 河南唐河人, 博士, 副教授, 硕士生导师, 主要从事空间分析及智能优化算法研究。 E-mail:danglx@vip.henu.edu.cn
Funding Information:
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.
Publisher Copyright:
© 2018, Science Press. All right reserved.
PY - 2018/7/25
Y1 - 2018/7/25
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Location prediction
KW - Matrix decomposition
KW - Suspect spatiotemporal prediction
KW - Tensor decomposition
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U2 - 10.12082/dqxxkx.2018.180036
DO - 10.12082/dqxxkx.2018.180036
M3 - Article
AN - SCOPUS:85122730144
SN - 1560-8999
VL - 20
SP - 929
EP - 938
JO - Journal of Geo-Information Science
JF - Journal of Geo-Information Science
IS - 7
ER -