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Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

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  • Fangyu Ding
  • Quansheng Ge
  • Dong Jiang
  • Jingying Fu
  • Mengmeng Hao

Abstract

Terror 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.

Suggested Citation

  • Fangyu Ding & Quansheng Ge & Dong Jiang & Jingying Fu & Mengmeng Hao, 2017. "Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-11, June.
    • Handle: RePEc:plo:pone00:0179057
    DOI: 10.1371/journal.pone.0179057
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    References listed on IDEAS

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    1. Tamara Makarenko, 2004. "The Crime-Terror Continuum: Tracing the Interplay between Transnational Organised Crime and Terrorism," Global Crime, Taylor & Francis Journals, vol. 6(1), pages 129-145, February.
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    Cited by:

    1. Peri, Giovanni & Rees, Daniel I. & Smith, Brock, 2023. "Terrorism and political attitudes: Evidence from European social surveys," Regional Science and Urban Economics, Elsevier, vol. 99(C).
    2. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    3. Peri, Giovanni & Rees, Daniel I. & Smith, Brock, 2020. "Terrorism, Political Opinions, and Election Outcomes: Evidence from Europe," IZA Discussion Papers 13090, Institute of Labor Economics (IZA).
    4. Zeng Li & Jingying Fu & Dong Jiang & Gang Lin & Donglin Dong & Xiaoxi Yan, 2017. "Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China," IJERPH, MDPI, vol. 14(11), pages 1-12, November.
    5. Raj Bridgelall, 2022. "Applying unsupervised machine learning to counterterrorism," Journal of Computational Social Science, Springer, vol. 5(2), pages 1099-1128, November.
    6. Qian Wang & Mengmeng Hao & David Helman & Fangyu Ding & Dong Jiang & Xiaolan Xie & Shuai Chen & Tian Ma, 2023. "Quantifying the influence of climate variability on armed conflict in Africa, 2000–2015," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9289-9306, September.
    7. Mengmeng Hao & Fangyu Ding & Xiaolan Xie & Jingying Fu & Yushu Qian & Tobias Ide & Jean-François Maystadt & Shuai Chen & Quansheng Ge & Dong Jiang, 2022. "Varying climatic-social-geographical patterns shape the conflict risk at regional and global scales," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.

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