Quantitative Analysis of Global Terrorist Attacks Based on the Global Terrorism Database

Li, Zhongbei; Li, Xiangchun; Dong, Chaoyu; Guo, Fengwei; Zhang, Fan; Zhang, Qi

doi:10.3390/su13147598

Cited by 16 publications

(10 citation statements)

References 34 publications

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“…According to GTD's data attribution criteria, there are 22 categories of targets/victims in global terrorist attacks, with names corresponding to the codes: business (1), government (2), police (3), military (4), abortion-related (5), airport & aircraft (6), government diplomacy (7), educational institution (8), food or water supply (9), journalist & media (10), maritime (11), NGO (12), other (13) Government (general): This target type mainly includes politicians, former politicians, and political venues. Terrorist attacks are generally targeted at politicians, e.g., in response to their election campaigns or political measures taken.…”

Section: Quantitative Statistical Analysis Of Terrorist Attack Target...mentioning

confidence: 99%

“…The model was validated using scenario analysis and sensitivity analysis, and the influence of key factors was analyzed. Li et al [8] used GTD data to quantify past terrorist attacks and used hierarchical analysis (AHP) to classify the level of damage. Luo et al [9] proposed a RF-RFE method based on a random forest kernel for identifying key indicators from 28 initial indicators to predict the risk.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based target prediction for terrorist attacks

Pan,

Zhang

2023

J. Phys.: Conf. Ser.

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To address the problem that terrorist attacks are frequent and attack targets are widely difficult to predict, we use multidimensional features of terrorist attacks to predict terrorist targets based on a quantitative statistical analysis of data in the global terrorism database (GTD) from 1970 to 2019. In this paper, a machine learning-based prediction model (i.e., a classifier framework) is proposed. The model pre-processes GTD data and uses the OneHotEncoder and KBinsDiscretizer methods for data category transformation, classifies terrorist targets by four algorithms—SVM, decision trees, KNN and DNN. Each algorithm was trained with their parameters optimised, and the performance of the proposed model was tested and evaluated. The experimental results show that the prediction model achieved good performance in predicting the attack targets. The neural network had the best prediction performance in predicting twenty-two attack targets with 100% accuracy, indicating that the proposed model is accurate and effective.

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Section: Quantitative Statistical Analysis Of Terrorist Attack Target...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning-based target prediction for terrorist attacks

Pan,

Zhang

2023

J. Phys.: Conf. Ser.

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“…Additionally, n is the number of grids in which the terrorist attack type l has occurred [58]. The impact of grid i on the types of terrorist attacks of its neighbors is shown in Figure 4, where " " represents the terrorist attack type l. The possibility of a terrorist attack of type l on grid i is affected by the average influence of the surrounding grids, which can be calculated by Equation (13).…”

Section: Inverse Distance Weightingmentioning

confidence: 99%

“…According to the GTD, currently the types of terrorist attacks are divided into eight categories, including Assassination, Armed Assault, Bombing/Explosion, Hijacking, Hostage Taking (Barricade Incident), Hostage Taking (Kidnapping), Facility/Infrastructure Attack and Unarmed Assault. The details of the attack information are as follows [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Xie

Zhang

et al. 2021

IJGI

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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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“…Li et al. (2021) used k ‐means clustering, an unsupervised ML approach, to classify terrorist activities by region, attack type, target type and weapon type (Li et al., 2021). Overall, there was a scarcity of studies that utilized data science methods to identify characteristics or attributes of terrorist attack locations.…”

Section: Introductionmentioning

confidence: 99%

Identifying factors associated with terrorist attack locations by data mining and machine learning

Bridgelall

2023

Int Social Sci J

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While studies typically investigate the socio‐economic factors of perpetrators to comprehend terrorism motivations, there was less emphasis placed on factors related to terrorist attack locations. Addressing this knowledge gap, this study conducts a multivariate analysis to determine attributes that are more associated with terrorist attacked locations than others. To tackle the challenge of identifying pertinent attributes, the methodology merges a Global Terrorism Database with relevant socio‐economic attributes from the literature. The workflow then trains 11 machine learning (ML) models on the combined data set. Among the 75 attributes assessed, 10 improved the predictability of targeted locations, with population and public transportation infrastructure being key factors. After optimizing hyperparameters, a multi‐layer perceptron—a type of artificial neural network—exhibited superior predictive performance, achieving an area under the curve score of 89.3%, classification accuracy of 88.1% and a harmonically balanced precision and recall score of 87.3%. In contrast, support vector machines demonstrated the poorest performance. The study also revealed that race, age, gender, marital status, income level and home values did not improve predictive performance. The ML workflow developed can aid policymakers in quantifying risks and making objective decisions regarding resource allocation to safeguard public health.

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Quantitative Analysis of Global Terrorist Attacks Based on the Global Terrorism Database

Cited by 16 publications

References 34 publications

Machine learning-based target prediction for terrorist attacks

Machine learning-based target prediction for terrorist attacks

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

Identifying factors associated with terrorist attack locations by data mining and machine learning

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