4.5 Article

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

出版社

MDPI
DOI: 10.3390/ijgi10080547

关键词

assessment; terrorist attack types; I-MLKNN; multi-source factors

资金

  1. National Natural Science Foundation of China [42001238]
  2. Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan [CITTCD201904037]
  3. R&D Program of Beijing Municipal Education Commission [KM202010011012]
  4. Postgraduate Research Capacity Improvement Program from Beijing Technology and Business University in 2021

向作者/读者索取更多资源

Studying the patterns of terrorist attacks and developing an assessment model is crucial for maintaining international security and global counter-terrorism efforts. The I-MLKNN multi-label classification algorithm proves effective in assessing the spatial distribution of terrorist attacks and revealing the patterns of different types of attacks, such as Armed Attacks, Bombing/Explosions, and Facility/Infrastructure Attacks, in Southeast Asia.
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.

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