A visual analytic framework for monitoring terror attacks: a case study of Mandera county, Kenya

Abstract

Terrorism remains a persistent and evolving threat in Kenya, particularly in border counties such as Mandera, which face heightened vulnerability due to their proximity to Somalia. The Country’s ongoing instability, weak governance structures, and the presence of terrorist groups like al-Shabaab (AS) create fertile ground for cross-border militant activity, posing significant challenges to both regional security and local stability. A critical gap in current counterterrorism efforts is the absence of localised, data-driven frameworks capable of monitoring and predicting terrorist activities in real time. This study addresses that gap by developing a Visual Analytics (VA) framework tailored to monitor, analyse, and predict terrorist incidents, using Mandera County as a case study. Beyond processing complex datasets, the framework offers strategic guidance to decision-makers, enhancing situational awareness and enabling proactive counterterrorism interventions. The core objective is to strengthen counterterrorism strategies through spatiotemporal data mining techniques, such as cluster analysis, association analysis, and outlier detection, to derive actionable insights from both structured and unstructured data sources. The methodology integrates data from social media, security reports, and established databases such as the Global Terrorism Database (GTD) and the Armed Conflict Location & Event Data Project (ACLED). Visuals in the framework are guided by Shneiderman’s Visual Information-Seeking Mantra: “overview first, zoom and filter, then details on demand.” Through this approach, the system identifies attack hotspots, temporal patterns, and early warning indicators, delivering a user-centric tool for informed decision-making. Findings demonstrate the framework’s effectiveness in predicting high-risk areas and attack types, with particular emphasis on armed assaults and improvised explosive device (IED) incidents, which constitute over 50% of recorded attacks in Mandera. The results show improved allocation of security resources, enhanced inter-agency coordination, and timely intervention capabilities. Key recommendations include deploying the framework in high-risk regions, integrating it with existing security infrastructure, and expanding its use to other terrorism-affected areas. The framework’s scalable and adaptable design positions it as a valuable tool for strengthening counterterrorism efforts across Kenya and similar contexts.