Premium
Identifying high‐risk firearm owners to prevent mass violence
Author(s) -
Laqueur Hannah S.,
Wintemute Garen J.
Publication year - 2020
Publication title -
criminology and public policy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.6
H-Index - 33
eISSN - 1745-9133
pISSN - 1538-6473
DOI - 10.1111/1745-9133.12477
Subject(s) - law enforcement , enforcement , business , database transaction , intervention (counseling) , scope (computer science) , computer security , gun violence , actuarial science , poison control , injury prevention , political science , law , medical emergency , medicine , computer science , database , psychiatry , programming language
Research Summary In this article, we detail recent efforts in California to identify and target high‐risk firearm owners to help prevent firearm violence, including mass shootings. We begin by describing gun violence restraining orders, also known as extreme risk protection orders, which provide a judicial mechanism for firearm recovery and a time‐limited prohibition on firearm purchases. Next, we discuss California's Armed and Prohibited Persons (APPS) database and enforcement system. APPS is used to identify newly prohibited persons among legal firearm owners and to help law enforcement recover those firearms. Finally, we highlight early research in which machine learning for rare event detection is employed to forecast individual risk using California's decades worth of firearm transaction records and other readily available administrative data. Policy Implications The approaches described range in scale, scope, and strategy, but all three allow for targeted intervention at times of heightened risk. In so doing, they offer the potential to provide outsized benefits to efforts to prevent mass violence.