Description of original award (Fiscal Year 2019, $955,475)
This project will expand on the U.S. Terrorist Incidents and Plots (TIPS) database to include attempted and completed mass shooting plots. Specifically, the research team will collect data on mass shootings plots, including plots foiled in advance or on-scene of the attack, from both open sources and from previously unreleased, official records from law enforcement agencies to identify promising indicators of potential mass shootings plots, models for prioritizing investigations, process and policy barriers to foiling plots along with solutions, and factors contributing to plot lethality along with ways to defend against them. By employing analytical methods used in terrorism research, the team will identify false positive cases for use as a control group enabling a comparison of credible vs. suspected mass shooting plotters. To identify characteristics associated with plot lethality, the research team will collect data on perpetrator, bystander, and first responder behaviors and tactics to identify ways to strengthen security in public soft target locations. Also, data on threat prioritization will be collected to enable advanced statistical and machine learning analyses comparing the four types of threat assessment outcomes: true negatives, true positives, false positives, and false negatives, as well as using mathematical computations to enable police agencies to prioritize incoming tips based on the findings. This project will answer three primary research questions: 1) What are the threat indicators and investigative procedures that better identify potential active shooters while also reducing the burden on police agencies and those identified incorrectly? 2) What are the barriers to discovering and halting active shooting plots, and how are those barriers mitigated? 3) What are the key factors impacting the likely casualties of attempted mass shootings? The team will also develop an online toolkit and a curriculum for use by law enforcement officers based on actionable lessons learned on how law enforcement, facilities security staff, and the public can collectively stop mass shootings plots in advance and stop casualties from plots that reach execution.
"Note: This project contains a research and/or development component, as defined in applicable law," and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14). CA/NCF
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