Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model

doi:10.3390/ijgi10080547

	Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model
	Zhao, Ruifang 1; Xie, Xiaolan 2; Zhang, Xun 1,2; Jin, Min 3; Hao, Mengmeng 2
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2021-08-01
卷号	10 期号:8 页码:22
关键词	assessment terrorist attack types I-MLKNN multi-source factors
DOI	10.3390/ijgi10080547
通讯作者	Zhang, Xun(zhangxun@btbu.edu.cn)
英文摘要	Terrorist attacks are harmful to lives and property and seriously affect the stability of the international community and economic development. Exploring the regularity of terrorist attacks and building a model for assessing the risk of terrorist attacks (a kind of public safety risk, and it means the possibility of a terrorist attack) are of great significance to the security and stability of the international community and to global anti-terrorism. We propose a fusion of Inverse Distance Weighting (IDW) and a Multi-label k-Nearest Neighbor (I-MLKNN)-based assessment model for terrorist attacks, which is in a grid-scale and considers 17 factors of socio-economic and natural environments, and applied the I-MLKNN assessment model to assess the risk of terrorist attacks in Southeast Asia. The results show the I-MLKNN multi-label classification algorithm is proven to be an ideal tool for the assessment of the spatial distribution of terrorist attacks, and it can assess the risk of different types of terrorist attacks, thus revealing the law of distribution of different types of terrorist attacks. The terrorist attack risk assessment results indicate that Armed Attacks, Bombing/Explosions and Facility/Infrastructure Attacks in Southeast Asia are high-risk terrorist attack events, and the southernmost part of Thailand and the Philippines are high-risk terrorist attack areas for terrorism. We do not only provide a reference for incorporating spatial features in multi-label classification algorithms, but also provide a theoretical basis for decision-makers involved in terrorist attacks, which is meaningful to the implementation of the international counter-terrorism strategy.
资助项目	National Natural Science Foundation of China[42001238] ; Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan[CITTCD201904037] ; R&D Program of Beijing Municipal Education Commission[KM202010011012] ; Postgraduate Research Capacity Improvement Program from Beijing Technology and Business University in 2021
WOS关键词	CONFLICT ; RISK ; DIMENSIONALITY ; REDUCTION ; ALGORITHM ; PATTERNS ; THAILAND ; DISTANCE ; TIME
WOS研究方向	Computer Science ; Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000689301900001
资助机构	National Natural Science Foundation of China ; Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan ; R&D Program of Beijing Municipal Education Commission ; Postgraduate Research Capacity Improvement Program from Beijing Technology and Business University in 2021
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/165301]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Xun
作者单位	1.Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Resources Utilizat & Environm Remediat, Beijing 100101, Peoples R China 3.State Grid Informat & Telecommun Grp Co Ltd, Beijing 102211, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Ruifang,Xie, Xiaolan,Zhang, Xun,et al. Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2021,10(8):22.
APA	Zhao, Ruifang,Xie, Xiaolan,Zhang, Xun,Jin, Min,&Hao, Mengmeng.(2021).Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,10(8),22.
MLA	Zhao, Ruifang,et al."Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 10.8(2021):22.