Spatiotemporal Prediction of Suspect under Location Data Sparsity Constraint

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Spatiotemporal Prediction of Suspect under Location Data Sparsity Constraint

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[1]Duan Lian,Dang Lanxue,Li Ming,et al.Spatiotemporal Prediction of Suspect under Location Data Sparsity Constraint[J].Journal of Zhengzhou University (Engineering Science),2018,39(05):58-62.[doi:10.13705/j.issn.1671-6833.2018.05.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 39卷 Number of periods: 2018 05 Page number: 58-62 Column: Public date: 2018-08-21

Title:: Spatiotemporal Prediction of Suspect under Location Data Sparsity Constraint

Author(s):: Duan Lian1; Dang Lanxue2; Li Ming3; Gao Chao4; Zhu Xinyan5; 1. School of Geographical Science and Planning, Guangxi Normal University, Nanning, Guangxi 530001; 2. Key Laboratory of Beibu Gulf Environmental Change and Resource Utilization of the Ministry of Education, Guangxi Normal University, Nanning 530001, Guangxi ;3. School of Computer and Information Engineering, Henan University, Kaifeng, Henan 475001

Keywords:: Suspect spatio-temporal prediction; Tensor decomposition; Crime prediction; Location prediction

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2018.05.003

Abstract:: Due to the low monitoring intensities on key tracking persons（suspects）, the police suffered from the very small amounts of suspect social location data, which was hard to effectively reveal the social mobility patterns of suspects, and restrict the police action validity for suspects filtering and crime blockading etc. Facing this data sparsity challenge, a novel Tensor Collective Decomposition Location Prediction (TCDLP) MODEL WAS PROPOSED, to estimate the latent visiting intensity at an arbitrary spatiotemporal node. Specifically, it modeled the visiting intensities of suspects with 3D tensor, where the three dimensions stood for suspects, locations, and time slots respectively. Then, the missing entries in the tensor would be filled through a multi-data fusion tensor decomposition approach, which integrates the correlations of locations and suspects relying on multiple social environment data. So by supplementing the visiting intensities in this tensor, the social spatiotemporal distribution pattern for each suspect could uncovered.TCDLP was evalvated by using a real-world suspect dataset collected form 241 suspects over 6 months with about 19 thousands location records, showing our model outperformed ststed-of -the -art approaches to the problem..

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Last Update: 2018-08-22