TY - JOUR
T1 - 顾及时空语义的疑犯位置时空预测
AU - Duan, Lian
AU - Hu, Tao
AU - Zhu, Xinyan
AU - Ye, Xinyue
AU - Wang, Shaohua
N1 - Funding Information:
First author: DUAN Lian, PhD, associate professor, specializes in geo⁃policing data mining. E⁃mail:wtusm@163.com Corresponding author: HU Tao, PhD. E⁃mail: thu6@kent.edu Foundation support: The National Natural Science Foundation of China, No. 41401524; the Guangxi Natural Science Foundation, Nos. 2015GXNSFBA139191, 2018JJA150089; the Open Research Program of Key Laboratory of Police Geographic Information Technology, Ministry of Public Security, No. 2016LPGIT03; the Open Research Program of Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, No. 2014BGERLXT14; the Open Research Program of Key Laboratory of Mine Spatial Information Tech⁃ nologies, NASG, No. KLM201409; the Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sens⁃ ing, No.(16)03.
Funding Information:
The National Natural Science Foundation of China, No. 41401524; the Guangxi Natural Science Foundation, Nos. 2015GXNSFBA139191, 2018JJA150089; the Open Research Program of Key Laboratory of Police Geographic Information Technology, Ministry of Public Security, No. 2016LPGIT03; the Open Research Program of Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, No. 2014BGERLXT14; the Open Research Program of Key Laboratory of Mine Spatial Information Technologies, NASG, No. KLM201409; the Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, No.(16)03.
Publisher Copyright:
© 2019, Editorial Department of Wuhan University of Technology. All right reserved.
PY - 2019/5/5
Y1 - 2019/5/5
N2 - 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.
AB - 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.
KW - Bayes model
KW - Crime geographic profiling
KW - Crime spatio-temporal prediction
KW - Spatio-temporal semantics
KW - Suspect location prediction
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U2 - 10.13203/j.whugis20170238
DO - 10.13203/j.whugis20170238
M3 - Article
AN - SCOPUS:85070110764
SN - 1000-050X
VL - 44
SP - 765
EP - 770
JO - Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University
JF - Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University
IS - 5
ER -