Risky Business: Examining the 80-20 Rule in Relation to a RTM Framework

Date01 March 2021
Published date01 March 2021
Subject MatterArticles
Risky Business: Examining the
80-20 Rule in Relation to a
RTM Framework
Hannah Steinman
, Grant Drawve
Jyotishka Datta
, Casey T. Harris
and Shaun A. Thomas
The spatial elements of crime occurrence and the identification of crime generators/attractors have
remained a prominent area of research. We focus on the utility of the 80-20 rule and the labeling of
risky facilities in crime forecasting models with risk terrain modeling (RTM). We first examine
whether the rule holds across types of crime generating places including liquor stores, department
stores, hotels/motels, restaurants/bars, and apartment complexes. Next, we use our findings to test
whether conducting preliminary analyses to identify risky facilities increases the predictive power of
RTM versus using all possible facilities. When restricting the RTM approach to only risky facilities,
results were more accurate than a traditional RTM approach. Findings and implications are nested in
the utilization of the wider body of environmental criminology research to increase our understanding
of where crime is likely to occur.
ecology and crime/spatial analysis, crime/delinquency theory, property crime, other, violent
Traditionally, research has overwhelmingly focused on understanding who commits a crime, leading
to the identification of chronic offending by Wolfgang and colleagues. Their landmark finding was
that 6.3%of delinquent youth accounted for 52%of the recorded police contact. In other words, most
youths are not delinquent, but a subset of youth is best described as chronic offenders. More contem-
porary research continues to support this general finding that a small percentage of individuals account
for a disproportionate amount of any particular crime outcome (e.g., Vaughn & DeLisi, 2008).
Although there are relatively few chronic offenders, considerable attention is afforded to those
offenders due to the disproportionate amount of crime that is attributed to them (McGloin & Stickle,
Department of Sociology & Criminology, University of Arkansas, Fayetteville, AR, USA
Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR, USA
Corresponding Author:
Grant Drawve, Department of Sociology & Criminology, University of Arkansas, Fayetteville, AR 72701, USA.
Email: drawve@uark.edu
Criminal Justice Review
2021, Vol. 46(1) 20-39
ª2020 Georgia State University
Article reuse guidelines:
DOI: 10.1177/0734016820938859
2011). With a focus on who commits the crime, where crime occurs warrants the same focus.
Certainly, research continues to indicate crime is not random in space and time, leading to a small
percentage of places accounting for a disproportionate share of crime occurrence.
As a growing analytical tool, risk terrain modeling (RTM) has been utilized to identify attributes
from the environment spatially associated with crime occurrence (RTM; see Caplan et al., 2011;
Caplan & Kennedy, 2016; Kennedy etal., 2018). RTM has a foundation in environmental criminology,
focusing primarily on the spatial attributes of crime with temporal scales of secondary priority,
including the influence of crime generators and attractors (CGAs; see Brantingham & Brantingham,
1995). These features of the environment such as bars, restaurants, rental housing units, public transit
stops, and so on, attract or generate crime based on their presence. Yet while advancements in RTM
applications have furthered crime and place research, the risk terrain approach overlooks variability in
crime occurrence at any one facility type. That is, RTM can identify bars as crime risk generators or
attractors, but ignores important issues raised by environmental criminology broadly, and the “80-20
rule” specifically (Clarke & Eck, 2003; Eck et al., 2007). In short, and expanded on later, the 80-20 rule
stipulates that a small subset of establishments of one type of facility (20%) usually accounts for a
majority of crime (80%), identifying certain risky facilities.
The current study builds on these observations to test the predictive accuracy of RTM using a model
limited to only risky facilities. To do so, we compare risk terrain models for all facilities against a risky
facility model using two crime types—aggravated assaults and theft from a motor vehicle—for the
calendar year 2018 to forecast January through September 2019 crime occurrence in Little Rock, AR.
These models are compared using the updated predictive accuracy index (PAI) outlined by Drawve and
Wooditch (2019), building from Chainey et al.’s (2008) original metric. In addition, we compare the
predictive accuracy of the two RTM analyses to a more simplistic approach, risky facility buffers.
Review of Literature
Extant literature dating back to Guerry (1833) and Quetelet (1831) demonstrate spatial patterning in
crime occurrence. Subsequently, Sherman et al. (1989) brought this argument to prominence in more
contemporary research at the microlevel with emphasis on the hotspots of crime. In comparison to
studies of individual criminal offending, spatial research has found that the concentration of crime
within relatively few places is more extreme than the disproportionate offending of high-crime indi-
viduals (Spelman & Eck, 1989). Naturally, research has sought to leverage theory to understand why
crime occurs in specific places, including through the lenses of routine activities theory (Cohen &
Felson, 1979) and crime pattern theory (P . J. Brantingham & Brantingham, 1984) lead ing to the
development of the law of crime concentration (Weisburd, 2015). In turn, this has led to an increased
effort to forecast where crime is going to occur, often down to the street level, in hopes of being able to
implement place-based efforts to reduce offending or prevent crime from occurring at all.
Environmental Criminology Foundation
Environmental criminology often relies on three theoretical perspectives as the foundation to explain
why crime occursin specific spaces at particulartimes: routine activitiestheory, rational choicetheory,
and crimepattern theory. Routineactivities theoryargues criminalopportunities are presentduring daily
routine activities such as going to school, work, and running errands when a likely target, motivated
offender,and lack or absenceof capable guardianshipconverge intime and space. When multiplepeople
havesimilar or overlappingroutine activities,this leads to the concentrationsof criminal opportunitiesat
certain places. Although there areopportunities for crime, offenders still have to makethe decision to
act. Presentedwith criminal opportunities, the decisionto act follows a rationalchoice model where the
offender seeks to maximize their rewards whileminimizing the risks involved (see Cornish& Clarke,
Steinman et al. 21

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