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Open AccessArticle

On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors

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Beijing Key Laboratory of Food Safety Big Data Technology, School of Computer and Information Engineering, Beijing Technology and Business University, No. 11, Fucheng Road, Haidian District, Beijing 100048, China
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Key Laboratory of Resources utilization and Environmental Remediation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
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University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(9), 354; https://doi.org/10.3390/ijgi7090354
Received: 31 July 2018 / Revised: 20 August 2018 / Accepted: 23 August 2018 / Published: 27 August 2018
(This article belongs to the Special Issue GIS for Safety & Security Management)
Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas. View Full-Text
Keywords: location recommendation algorithm; risk assessment; terrorist attacks; multi-source factors; spatial clustering location recommendation algorithm; risk assessment; terrorist attacks; multi-source factors; spatial clustering
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MDPI and ACS Style

Zhang, X.; Jin, M.; Fu, J.; Hao, M.; Yu, C.; Xie, X. On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors. ISPRS Int. J. Geo-Inf. 2018, 7, 354.

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