Abstract
Security and intelligence agencies around the world invest considerable resources in preventing terrorist attacks, as these may cause strategic damage, national demoralization, infringement of sovereignty, and government instability. Recently, data-mining techniques have evolved to allow identification of patterns and associations in criminal data that were not apparent using traditional analysis. The aim of this paper is to illustrate how to use interpretable classification algorithms to identify subgroups (“patterns”) of terrorist incidents that share common characteristics and that result in mass fatalities. This approach can produce insights far beyond those of conventional macro-level studies that use hypothesis-testing and regression models. In addition to this methodological contribution, from a practical perspective, exploring the characteristics identified in the “patterns” can lead to prevention strategies, such as alteration of the physical or systemic environment. This is in line with situational crime prevention (SCP) theory. We apply our methodology to the Global Terrorism Database (GTD). We present three examples in which terror attacks that are described by a particular pattern (set of characteristics) resulted in a high probability of mass casualties, while attacks that differ in just one of these characteristics (i.e., month of attack, geographical area targeted, or type of attack) resulted in far fewer casualties. We propose exploration of the differentiating characteristic as a means of reducing the probability of mass-fatality terrorist incidents.
Highlights
In an effort to better understand the logic of terrorists’ actions and to develop more efficient prevention strategies, criminologists suggest different typologies of terror attack based on various dimensions
The aim of this study is to demonstrate the utility of applying data-mining algorithms to identify subgroups of terrorist incidents that share common characteristics and that result in mass fatalities
This curve was created by plotting the true positive rate (TPR), i.e., the proportion of positive events that were identified as such by the classifier, against the false positive rate (FPR), i.e., the proportion of negative events that were wrongly categorized as positive
Summary
In an effort to better understand the logic of terrorists’ actions and to develop more efficient prevention strategies, criminologists suggest different typologies of terror attack based on various dimensions. Terrorism and Singer and Golan Crime Sci (2019) 8:14 threats to national security are documented to have impacts on tourism (Araña and León 2008). Psychological effects, such as public fear and stress, are a further consequence. Terrorism can affect political tolerance (Peffley et al 2014) and can cause infringement of sovereignty, potentially leading to military confrontations. These devastating effects motivate governments to develop tools to assess and prevent large-scale terrorist attacks (e.g., LaFree and Bersani 2014)
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