An innovative color-coding scheme for terrorism threat advisory system. Issue 1 (March 2023)

• Record Type: Journal Article
• Authors: Singh, Prabal Pratap; Philip, Deepu
• Is Part Of: Methodological innovations. Volume 16:Issue 1(2023)
• Page Start: 38
• Page End: 56
• Publication Date: 2023-03
• Subjects: Threat advisory system -- warning systems -- color code -- caterpillar diagram -- global terrorism database -- global terrorism index -- Markov chains; Research -- Methodology -- Periodicals; Social sciences -- Methodology -- Periodicals; 300.72
• DOI: 10.1177/20597991221144577
• Languages: English
• ISSNs: 2059-7991

Abstract

This research develops an innovative terror threat advisory system capable of visually communicating variations in the terrorism levels to policymakers or the public and forecasts future levels. Earlier attempts to create similar advisory systems by policymakers were either discontinued or lost their relevance due to a trust deficit in the system. We propose a novel approach for creating a color scheme and utilize it to develop an intuitive caterpillar diagram summarizing various stages of terrorism. It incorporates Global Terrorism Impact Scores for a nation or region using the Global Terrorism Database (GTD). Further, color transitions in the caterpillar diagram between consecutive periods mimic a Markovian process, thereby enabling us to develop the forecasting model. We successfully demonstrated the effectiveness of the proposed caterpillar diagram and forecasting model for India, Iraq, and their respective regions. The forecasting model suggests that the aggressive terrorism stage depicted by red color and transient stages of ascent (yellow) and descent (cyan) are the most probable in these nations and their regions. The proposed caterpillar diagram is an innovative visualization approach to identify terrorism patterns, from which a Markovian forecasting model is developed to aid policymakers. Our approach applies to any event-based database like GTD. Finally, the caterpillar diagram is a domain-independent framework that can visualize variations in any univariate data series, thereby assisting in system monitoring.