TY - JOUR
T1 - Prediction of suspect location based on spatiotemporal semantics
AU - Duan, Lian
AU - Ye, Xinyue
AU - Hu, Tao
AU - Zhu, Xinyan
N1 - Funding Information:
This study has primarily been funded by National Natural Science Foundation of China (Grant No. 41401524), Guangxi Natural Science Foundation (Grant No. 2015GXNSFBA139191), Scientific Project of Guangxi Education Department (Grant No. KY2015YB189), Open Research Program of Key Laboratory of Police Geographic Information Technology, Ministry of Public Security (Grant No. 2016LPGIT03), Open Research Program of Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Guangxi Teachers Education University), Ministry of Education (Grant No. 2014BGERLXT14), Open Research Program of Key Laboratory of Mine Spatial Information Technologies of National Administration of Surveying, Mapping and Geoinformation (Grant No. KLM201409), National Science Foundation (Grant No. 1535031, 1637242), the Fundamental Research Funds for the Central Universities (Grant No. 413000010), the Open Foundation of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Grant No. 16(03)), and National Science Foundation (1637242, 1535031).
Publisher Copyright:
© 2017 by the authors. Licensee MDPI.
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
KW - Bayes model
KW - Crime analysis
KW - Geographic profiling
KW - Spatiotemporal prediction
KW - Spatiotemporal semantics
KW - Suspect location prediction
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U2 - 10.3390/ijgi6070185
DO - 10.3390/ijgi6070185
M3 - Article
AN - SCOPUS:85026383882
SN - 2220-9964
VL - 6
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 7
M1 - 185
ER -