Prediction of suspect location based on spatiotemporal semantics

Lian Duan, Xinyue Ye, Tao Hu, Xinyan Zhu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, 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. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.

Original languageEnglish (US)
Article number185
JournalISPRS International Journal of Geo-Information
Volume6
Issue number7
DOIs
StatePublished - Jul 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

Keywords

  • Bayes model
  • Crime analysis
  • Geographic profiling
  • Spatiotemporal prediction
  • Spatiotemporal semantics
  • Suspect location prediction

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