Breaking the Chain: How Arrests Reduce the Probability of Near Repeat Crimes

AuthorAndrew P. Wheeler,Cory P. Haberman,Jordan R. Riddell
Published date01 June 2021
Date01 June 2021
Subject MatterArticles
Breaking the Chain: How
Arrests Reduce the
Probability of Near
Repeat Crimes
Andrew P. Wheeler
, Jordan R. Riddell
, and Cory P. Haberman
Objectives: Near repeat patterns have been identified for a host of different crimes, but effective
strategies to reduce near repeats have had more variable results. This study identifies near repeat
crime patterns in Dallas, TX, and examines the effects of an arrest on reducing the probability of
future crime. Method: Using open-source crime data from the Dallas Police Department from July
2014 through June 2018, we identified near repeat patterns for shootings, interpersonal robberies,
residential burglaries, and thefts from motor vehicles. Logistic regression models were used to test
the effect of an arrest on reducing near repeat crimes; controls for geographic, demographic, and
temporal factors were included in each model. Results: Near repeat calculations suggest violent
crime clustered closely in time and space, with property crime dispersed over larger spatial and
temporal dimensions. Across all four crime types, findings suggest arrests resulted in 20%–40%
reductions in a near repeat follow-up crime. Conclusions: In line with past research on shootings,
arrests reduced the likelihood of subsequent crimes. This suggests policing strategies to increase
arrests may be a fruitful way to reduce near repeat crime patterns.
near repeat, arrests, spatial analysis, crime analysis
Near repeat crime patterns are the concentration of crime incidents in both space and time (Townsley
et al., 2003). Drawing from epidemiology, the idea is that the occurrence of a crime creates a
heightened risk of a subsequent crime in nearby areas (e.g., a street block or two) for a short time
(e.g., a few days to weeks; Knox, 1964). In sum, near repeat crime patterns are a well-established
criminological finding for many differen t crime types (S. D. Johnson et al., 2007; Rat cliffe &
Criminology and Criminal Justice Program, School of Economic, Political, and Policy Sciences, The University of Texas at
Dallas, Richardson, TX, USA
School of Criminal Justice, University of Cincinnati, OH, USA
Corresponding Author:
Andrew P. Wheeler, HMS, 5615 High Point Dr #100, Irving, TX 75038, USA.
Criminal Justice Review
2021, Vol. 46(2) 236-258
ª2021 Georgia State University
Article reuse guidelines:
DOI: 10.1177/0734016821999707
Rengert, 2008; Youstin et al., 2011). But, studies evaluating policing strategies to address near
repeat crime patterns have shown mixed results (Elffers et al., 2018; Groff & Taniguchi, 2019; R.
B. Santos & Santos, 2016; Stokes & Clare, 2019). Here, we focus on whether arresting individuals
reduces the probability of a near repeat crime.
We build on prior work in two particular ways. Prior analyses of shooting data have established
that arrests reduce the probability of near repeat patterns (Wyant et al., 2012). In this work, we
establish whether this relationship also holds true for not only shootings in another sample but also
for three instrumental crimes: residential burglaries, interpersonal robberies, and thefts from motor
vehicles. If near repeat patterns are largely a function of the same individuals committing multiple
offenses in a short spree (S. D. Johnson et al., 2009), it may be the incapacitation or deterrent effects
of an arrest are larger for instrumental crimes than for shootings, which are theoretically driven by
retaliatory violence (Loftin, 1986; Ratcliffe & Rengert, 2008).
Given that macro-level evidence of the effectiveness of arrests in reducing future crime are more
equivocal (Bursik et al., 1990; Chamlin et al., 1992; Levitt, 1998), examining very small space and
time windows provides a more direct test of the efficacy of arrest-based strategies that are less
influenced by potential endogeneity problems. If arrests provide subseq uent reductions in near
repeat offending, it can provide the foundation for assessing the benefits of increasing arrest rates
in reference to reduced crime rates.
Second, a variety of more recent work has established different predictive factors that
correlate with the probability of a near repeat crime (Garnier et al., 2018; Moreto et al.,
2014; Piza & Carter, 2018). In addition to formulating a regression mod el incorporating these
demographic and facility factors to account for additional confounds not present in prior work,
we include a factor for the historical density of crimes. Given that hot spots of crime tend to be
temporally stable (Curman et al., 2014; Weisburd et al., 2004; Wheeler et al., 2016), not
accounting for the underlying density can result in confounds in predicting which crimes are
likely to result in a near repeat offense.
A large sample of 4 years of open data on crime incidents and arrests from Dallas, TX, was used
for this study. Compared to prior work on shootings, we find that arrests tended to have similar
effectiveness in reducing the probability of a near repeat offense for all the crimes types examined
(Wyant et al., 2012), typically ranging from a reduction of 20%–40%near repeats across different
space and time windows. Given the low arrest rates for many of the crime types under stud y,
prioritizing events that have a higher probability of a near repeat crime may be a reasonable strategy
for police departments to tackle near repeat crimes.
Literature Review
Overview of Near Repeat Crime Patterns
Near repeat crime patterns occur when two or more crimes occur in a short spatial–temporal window
(Townsley et al., 2003). The basic idea is that a crime incident, often called the originator/initiator
event, spurs one or more subsequent crime incidents, often called the near repeat events, in the
immediate spatial area and in a short time frame of the originator event. The premise is adapted from
epidemiology and the transmission of infectious diseases (Knox, 1964). For example, an originator
house is burglarized on a Monday, and the next-door neighbor becomes a near repeat house when it
is burglarized the following Wednesday. While most work on near repeat crime patterns has been
focused on burglary (S. D. Johnson et al., 2007; Townsley et al., 2003), it has been shown to extend
to a variety of different crime types: robbery, shootings, theft from motor vehicles, thefts of motor
vehicles, arson, assault, economic crimes, piracy, and terroristic events (Behlendorf et al., 2012;
Block & Fujita, 2013; Braithwaite & Johnson, 2012; Haberman & Ratcliffe, 2012; Lockwood, 2012;
Wheeler et al. 237

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