A Typology of Civilians Shot and Killed by US Police: a Latent Class Analysis of Firearm Legal Intervention Homicide in the 2014-2015 National Violent Death Reporting System.

Abstract

Approximately 1000 people are killed by police acting in the line of duty each year. Historically, research on these deaths, known as legal intervention homicides (LIH), has been limited by data that is either contextually rich but narrow in scope and not readily available to the public (e.g., police department reports from a single city), or detail-poor but geographically broad, large, and readily available (and maintained by federal agencies) (e.g., vital statistics and supplemental homicide reports). Over the past 5years, however, researchers have turned to the National Violent Death Reporting System (NVDRS), which captures nearly all lethal police shootings in participating states while providing detailed incident and victim information. The current study extends prior work on police-involved lethal shootings in three important ways. First, we use latent class analysis to construct a data-driven, exhaustive, mutually exclusive typology of these events, using NVDRS data 2014-2015. Second, rather than fitting some, but not all cases into predefined sub-types, every case is assigned membership to a particular emergent class. Third, we use a validated case identification process in NVDRS to identify incidents of lethal police-involved shootings. Seven classes emerge. Classes differ across important incident and victim characteristics such as the event that brought the victim and law enforcement together, the highest level of force used by the victim against law enforcement, and the kind of weapon, if any, used by the victim during the incident. Demographic variables do not distribute uniformly across classes (e.g., the latent class in which the victim appeared to pose minimal threat to law enforcement was the only class in which the plurality of victims was a non-white race). Our approach to generating these typologies illustrates how data-driven techniques can complement subjective classification schemes and lay the groundwork for analogous analyses using police encounter data that include fatal and non-fatal outcomes.