By Kaitlyn Sill and Paul A. Haskins.

This article originally appeared in Police Chief and is reposted here with permission from the International Association of Chiefs of Police.

Hate crimes harm whole communities. They are message crimes that tell all members of a group—not just the immediate victims—that they are unwelcome and at risk.

The damage that bias victimization causes multiplies when victims and justice agencies don’t recognize or report hate crimes as such. In addition, in cases for which law enforcement agencies fail to respond to or investigate hate crimes, relationships between law enforcement and affected communities can suffer, and public trust in police can erode.[1]

While it is known that hate crimes are underreported throughout the United States, there is not a clear understanding of exactly why reporting rates are low, to what extent, and what might be done to improve them. An even more elementary question, with no single answer, is: What constitutes a hate crime? Different state statutes and law enforcement agencies have different answers to that question, which further complicates the task of identifying hate crimes and harmonizing hate crime data collection and statistics.

By Michael Jendrykea, , Stephen C. McClure

From time to time the popular media draws attention to hate crimes and hate groups, evoking images of NaziGermany and the rise of fascism. The geographic association between hate groups and hate crimes is uncertain. In this research we ask whether hate crimes are co-located and correlated to the presence of hate groups to explore a potential association between these two phenomena. Publicly available point level data on hate crimes and hate groups collected by the Southern Poverty Law Center (SPLC) were aggregated to unitary framework of hexagonal grid cells of a Discrete Global Grid System (DGGS) at multiple scales for consistent analysis. We explore the effects of proximity by interpreting a co-location map, deploying a Geographically Weighted Regression (GWR) for count data, and apply a Spatial Lag Model (SLM) at multiple scales, to ascertain the effects of the size of the aggregation units on the relationship between hate groups and hate crimes. Controlled or uncontrolled for spatial dependence, at all scales, the Spatial Lag Model (SLM) shows that an average of 39.5% of the hate crimes was correlated with hate groups. These results are consistent with the existing research but show that in most instances spatial dependence was present, regardless of the size of the aggregation unit or the distance to neighboring cells. Our future research will consider additional racial, economic and social variables using a DGGS.

Applied Geography. Volume 111, October 2019, 102072

By Brian Levin, James Nolan, and Kiana Perst

When prejudice-related data are combined and analyzed over time, critical information is uncovered about overall trends, related intermittent spikes, and less common sharp inflectional shifts in aggression. These shifts impact social cohesion and grievously harm specific sub-groups when aggression escalates and is redirected or mainstreamed. These data, so critical to public policy formation, show that we are in such a historic inflection period now. Moreover, analysis of the latest, though partial Federal Bureau of Investigation hate crime data release, when overlaid with available data from excluded large jurisdictions, reveals hate crimes hit a record high in 2021 in the United States that previously went unreported. This Essay analyzes the most recent national data as well as various numerical and policy milestones that accompanied the historic, yet incomplete, implementation of hate crime data collection and related statutes over recent decades. This analysis of emerging trends in the United States is undertaken in the context of bigoted aggression broken down over time.

112 J. CRIM. L. & CRIMINOLOGY 749 (2023)