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ISPRS Int. J. Geo-Inf. 2017, 6(7), 185; doi:10.3390/ijgi6070185

Prediction of Suspect Location Based on Spatiotemporal Semantics

1,2
,
3,* , 4,5,6,* and 4,5
1
School of Geographical Sciences and Planning, Guangxi Teachers Education University, Nanning 530001, China
2
Education Ministry Key Laboratory of Environment Evolution and Resources Utilization in Beibu Bay, Guangxi Teachers Education University, Nanning 530001, China
3
Department of Geography and Computational Social Science Lab, Kent State University, Kent, OH 44240, USA
4
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
5
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
6
School of Library and Information Science, Kent State University, Kent, OH 44240, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 8 March 2017 / Revised: 14 May 2017 / Accepted: 18 June 2017 / Published: 23 June 2017

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. View Full-Text
Keywords: suspect location prediction; spatiotemporal prediction; geographic profiling; spatiotemporal semantics; bayes model; crime analysis suspect location prediction; spatiotemporal prediction; geographic profiling; spatiotemporal semantics; bayes model; crime analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Duan, L.; Ye, X.; Hu, T.; Zhu, X. Prediction of Suspect Location Based on Spatiotemporal Semantics. ISPRS Int. J. Geo-Inf. 2017, 6, 185.

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