Examining the Spatial Distribution of Crime by Victim Characteristics

ABSTRACT

It is well known that victims do not consistently report crimes. More problematically, victims in disadvantaged neighborhoods with high percentages of minority residents are not likely to report at high rates. Crime mapping is often considered at an aggregate level; however, the present study examines crime from a victim-perspective to better understand how crime is distributed by victim characteristics. Results reveal interesting similarities and differences in the geographical distribution of crime when disaggregated by victim race. Implications and directions for future research are also discussed.

Acknowledgement

An earlier version of this manuscript was presented at the Southern Criminal Justice Association’s Virtual Research Showcase in September 2020. We very much appreciate the comments and feedback from the participants in the panel session. Our manuscript has been improved because of their efforts. We also appreciate the fantastic set of comments provided by the blind reviewers. Their input has greatly strengthened our revised manuscript.

Notes

¹ Demographic data were retrieved from the U.S. Census Bureau’s American Community Survey for 2018. The comparison between 2010 and 2018 is referencing the differences in reported demographics from the 2010 Decennial Census and the 2018 ACS, as the 2010 Census Data were the most recent available in the ArcMap online tools for mapping purposes. Data were retrieved from https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/.

² The recent publication of 20 years of reported offenses to the FBI’s Uniform Crime Reports shows that this distribution is regularly encountered over time. Property crime occurs at much higher rates than violent crimes, averaging rates that are approximately 14 times higher than the violent crime rate between 2000 and 2019.

³ The global S-Index is calculated using the equation $S=\frac{{\sum }_{i=1}^n{s}_i}{n}$, where n is the number of areas in the study area (in this paper, the number of Census tracts in Dallas, Texas), and $s_i$ varies between 0 and 1, depending on whether the two point patterns are similar or dissimilar in each Census tract. Generally speaking, it is an average of the number of 1s.

Additional information

Notes on contributors

Wesley G. Jennings

Wesley G. Jennings, PhD, is Chair and Professor in the Department of Criminal Justice & Legal Studies at the University of Mississippi. He has over 250 publications, his h-index is 55 (i-index of 158), and he has over 10,000 citations to his published work.

Danielle M. Fenimore

Danielle M. Fenimore, Ph.D., is a postdoctoral researcher in the Public Safety Institute at the University of Memphis.

Nicholas M. Perez

Nicholas Perez, Ph.D. is an Assistant Professor in the School of Criminology, Criminal Justice, and Emergency Management at California State University, Long Beach (CSULB).

Stephen A. Bishopp

Stephen A. Bishopp, Ph.D. is a Lieutenant at the Dallas Police Department in Dallas, Texas.