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Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach
Ding, Fangyu1,2; Ge, Quansheng1,2; Jiang, Dong1,2; Fu, Jingying1,2; Hao, Mengmeng1,2
2017-06-07
Source PublicationPLOS ONE
ISSN1932-6203
Volume12Issue:6Pages:11
Corresponding AuthorGe, Quansheng(geqs@igsnrr.ac.cn) ; Jiang, Dong(jiangd@igsnrr.ac.cn)
AbstractTerror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.
DOI10.1371/journal.pone.0179057
WOS KeywordSUPPORT VECTOR MACHINES ; SEPTEMBER 11 ; TIME-SERIES ; ATTACKS ; DEEP ; CLASSIFICATION ; NETWORKS ; BEHAVIOR ; PROGRAM ; PACKAGE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41571509] ; Ministry of Science and Technology of China[2016YFC1201300]
Funding OrganizationNational Natural Science Foundation of China ; Ministry of Science and Technology of China
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000402880700082
PublisherPUBLIC LIBRARY SCIENCE
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/63417
Collection中国科学院地理科学与资源研究所
Corresponding AuthorGe, Quansheng; Jiang, Dong
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Ding, Fangyu,Ge, Quansheng,Jiang, Dong,et al. Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach[J]. PLOS ONE,2017,12(6):11.
APA Ding, Fangyu,Ge, Quansheng,Jiang, Dong,Fu, Jingying,&Hao, Mengmeng.(2017).Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach.PLOS ONE,12(6),11.
MLA Ding, Fangyu,et al."Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach".PLOS ONE 12.6(2017):11.
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