Spatio-Temporal Prediction of Suspect Location by Spatio-Temporal Semantics

Existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. Therefore, we propose a location prediction model called SSLP (spatio-temporal semantics location prediction) to enhance the location prediction performance. Firstly, the similar suspect groups of the target suspects are extracted using the distributed proximity of the suspects in different semantic periods and semantic positions.Then, their mobility data are applied to estimate transition frequencies and temporal visiting probabilities for unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by a Bayesian-based formula, the spatio-temporal prediction for the individual suspect can be realized. In the experiments with the location recording data set consisting of 158 suspects and their 17 539 location records from January to June 2013 in W city, SSLP model outperforms baseline algorithms by 40%-50%, validating its adaptability for data sparsity problem.