Enhancing Near-Repeat Shooting Analysis with Crime Gun Intelligence

• In Event: Tracking the Trade: Gun Trafficking, Markets, and Regulatory Gaps

Abstract

The National Integrated Ballistic Information Network (NIBIN), administered by the Bureau of Alcohol, Tobacco, Firearms, and Explosives, enables law enforcement agencies to analyze ballistic evidence to develop the criminal histories of crime guns and connect shooting incidents across space and time. Despite its potential, the crime gun intelligence gleaned from NIBIN has not yet been integrated into analyses of near-repeat shooting patterns, which could provide new insights into how shooting incidents are connected. This study addresses this gap by examining near-repeat shooting patterns in Detroit, Michigan, through a NIBIN lens.
Using a near-repeat analysis framework, we identify spatial and temporal clustering of shooting incidents, leveraging NIBIN intelligence to identify connected incidents and explore the criminal histories of associated firearms. Additionally, we use logistic regression to estimate the likelihood that a shooting incident belongs to a near-repeat chain. For shooting incidents within a near-repeat chain, we use multinomial logistic regression to analyze incident characteristics across chains of varying intensity. By integrating NIBIN intelligence into our assessments, this study provides a more comprehensive understanding of how firearm-related violence is distributed across space and time, providing actionable insights for law enforcement and violence prevention efforts.

Authors

• Alaina De Biasi, Wayne State University

• Jeffrey Rojek, Michigan State University

• Ed McGarrell, Michigan State University