Duan Lian1,Dang Lanxue2,Li Ming3,Gao Chao4,Zhu Xinyan5
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..