Abstract
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.
Highlights
To determine the best method to effectively reduce the dimension of the features while retaining the main information of the features as much as possible, scholars conducted a series of research studies and proposed a number of algorithms, such as the Genetic Algorithm (GA) and Principal Component Analysis (PCA)
The dimension reduction algorithm Locally Linear Embedding (LLE) was used to reduce the dimensions of the features of the types of terrorist attacks,LLE
The dimension reduction algorithm to reduce the dimenfeatures and after reduction was obtained by the Maximal Information Coefficient (MIC).between sions ofbefore the features of the the dimension types of terrorist attacks, and the correlation the feamulti-label classification algorithm was reduction integrated with the by inverse distance tures before and after the dimension was obtained the MIC
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Unarmed Assault: An attack whose primary objective is to cause physical harm or death directly to human beings by any means other than explosive, firearm, incendiary, or sharp instrument (knife, etc.). These are assessed by the types of terrorist attacks that occurred in the same location and are used to predict the types of terrorism where no attack occurred. This involves a typical multi-label classification problem [15]. It can effectively evaluate the risk of different types of terrorist attacks and reveal the patterns between the types of terrorist attacks, providing support for relevant decision-makers
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