This study investigates the severity dynamics of terrorism in Northern Nigeria through the lens of the Anarchical Coefficient of Terrorism (ACT), utilizing Regularization Regression Models (RRMs), specifically Lasso and Ridge regression techniques. The primary objective is to identify significant predictors of terrorism severity, such as the number of perpetrators per incident, casualties per incident, and incidents per city, while providing a predictive framework for policymakers and security agencies, and also quantifying the chaotic characteristics inherent in the region's terrorism landscape. Analyzing a terrormographic dataset from 1991 to 2024, the research seeks to discern the patterns and relationships among key variables influencing terror incidents. Methodologically, rigorous data validation tests, including assessments for multicollinearity, autocorrelation, and normality, are conducted to address challenges in high-dimensional data before model fitting. The results demonstrate strong model performance, with R² values indicating substantial explanatory power for the variance in incident variables. Key findings reveal that high-casualty incidents are strongly associated with future attacks, indicating a cycle of violence, while enhanced security in high-casualty cities effectively reduces the likelihood of subsequent incidents. Furthermore, the study underscores the role of organized terrorist groups with high incident-perpetrator ratios in sustaining violence. This highlights the importance of monitoring organized groups with high incident-perpetrator ratios, indicating their potential to execute multiple attacks. These insights emphasize the necessity of proactive counter-terrorism strategies, including intelligence gathering, community engagement, and tailored interventions based on the region's anarchical dynamics. The contributes valuable knowledge to the understanding of terrorism severity dynamics, addressing critical gaps in existing literature and informing strategic responses to mitigate the impact of terrorism in Northern Nigeria. However, it acknowledges limitations related to data constraints and model applicability. Future research directions suggest the need for longitudinal studies and comparative analyses to enhance the understanding of terrorism severity dynamics in diverse regions.
Terrorism, Severity Dynamics of Terrorism, Regularization Regression Models, Anarchical Coefficient of Terrorism, Predictive Framework
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