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ISPRS Int. J. Geo-Inf. 2019, 8(3), 133; https://doi.org/10.3390/ijgi8030133

Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method

1,2
,
1,2,3,*
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1,2
,
1,2
and
1,2
1
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
2
College of Resource and Environment, University of Chinese Academy of Sciences, NO.19 Yuquan Road, Shijingshan District, Beijing 100049, China
3
Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land & Resources, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Received: 10 January 2019 / Revised: 27 February 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue GIS for Safety & Security Management)
Full-Text   |   PDF [3969 KB, uploaded 7 March 2019]   |  

Abstract

In recent years, various types of terrorist attacks have occurred which have caused worldwide catastrophes. The ability to proactively detect and even predict a potential terrorist risk is critically important for government agencies to react in a timely manner. In this study, a method of geospatial statistics was used to analyse the spatio-temporal evolution of terrorist attacks on the Indochina Peninsula. The machine learning random forest (RF) method was adopted to predict the potential risk of terrorist attacks on the Indochina Peninsula on a spatial scale with 15 driving factors. The RF model performed well with AUC values of 0.839 [95% confidence interval of 0.833–0.844]. The map of the potential distribution of terrorist attack risk was obtained with a 0.05×0.05-degree (approximately 5×5 km) resolution. The results indicate that Thailand is the most dangerous area for terrorist attacks, especially southern Thailand, Bangkok and its surrounding cities. Middle Cambodia and the northern and southern parts of Myanmar are also high-risk areas. Other areas are relatively low risk. This study provides the hotspots for terrorist attacks on a more fine-grained geographical unit. Meanwhile, it shows that machine learning algorithms (e.g., RF) combined with GIS have great potential for simulating the risk of terrorist attacks. View Full-Text
Keywords: terrorism incidents; spatio-temporal patterns; Geo-information system; RF Algorithm; Indochina Peninsula terrorism incidents; spatio-temporal patterns; Geo-information system; RF Algorithm; Indochina Peninsula
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hao, M.; Jiang, D.; Ding, F.; Fu, J.; Chen, S. Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method. ISPRS Int. J. Geo-Inf. 2019, 8, 133.

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