FINDING SAFETY IN NUMBERS: EXPLORING THE POSSIBILITIES OF TERRORISM CONSEQUENCE MODELS AS PUBLIC SAFETY PLANNING TOOLS

Abstract

Special events and mass gatherings present significant vulnerabilities across the United States and remain attractive targets for violent extremists. This study contends that a more realistic understanding of their human consequences (i.e., civilians killed or wounded) can help inform the deployment of limited resources and personnel to mitigate the potential consequences. Specifically, this thesis investigates how predictive analysis models could be developed, vetted, and implemented as public safety planning tools. The study examines the relationship between the crowd size and the killed or wounded rates of common terrorism attack types. When a relationship exists, the research explores how that relationship can be expressed as a mathematical model that predicts future lethality. The relationship between the crowd size, killed or wounded, and attack type is examined using regression analysis. Of the five attack types reviewed, only two yielded enough data for this level of statistical analysis. The results show a mostly weak relationship, which suggests that overall crowd size holds little influence over the total casualty rate. Instead, the weapon’s effective range, crowd density, and ease of egress are recommended as future areas of continued study. This thesis also suggests continued research and development of probabilistic consequence models while identifying options for collaboration and implementation.