Redrawing Hot Spots of Crime in Dallas, Texas

Published date01 June 2021
Date01 June 2021
Subject MatterArticles
Redrawing Hot Spots of
Crime in Dallas, Texas
Andrew P. Wheeler
Sydney Reuter
In this work we evaluate the predictive capability of identifying long term, micro
place hot spots in Dallas, Texas. We create hot spots using a clustering algorithm,
using law enforcement cost of responding to crime estimates as weights. Relative to
the much larger current hot spot areas defined by the Dallas Police Department, our
identified hot spots are much smaller (under 3 square miles), and capture crime cost
at a higher density. We also show that the clustering algorithm captures a wide array
of hot spot types; some one or two addresses, some street segments, and others an
agglomeration of larger areas. This suggests identifying hot spots based on a specific
unit of aggregation (e.g. addresses, street segments), may be less efficient than using a
clustering technique in practice.
hot-spots, clustering, prediction, cost-benefit-analysis
While targeting police resources at micro place hot spots has been one of the
most successful policing interventions to reduce crime (Braga et al., 2019), it is
still an open question as to how to construct those hot spots. Using open crime
HMS, Irving, TX, United States
Program in Criminology & Criminal Justice, School of Economic, Political, and Policy Sciences,University
of Texas at Dallas, Richardson, TX, United States
Corresponding Author:
Andrew P. Wheeler, HMS, Irving, TX, United States.
Police Quarterly
2021, Vol. 24(2) 159–184
!The Author(s) 2020
Article reuse guidelines:
DOI: 10.1177/1098611120957948
data from Dallas, Texas, this article provides an example of constructing long
term micro place hot spots. Using historical data to identify hot spots of crime,
we show how our identif‌ied clusters capture a higher density of crime costs than
current Dallas hot spot areas on future crimes.
While there are a plethora of prior articles examining the predictive ability of
different hot spot methods (Chainey et al., 2008; Drawve, 2016; Levine, 2008;
Van Patten et al., 2009), there are three novel contributions of this work. First,
we illustrate the use of a hierarchical clustering technique, DBSCAN (Campello
et al., 2013), to formulate different contiguous hot spot areas. A point of con-
tention in prior work is the correct spatial unit of analysis to conduct analysis
on. For example, several scholars often suggest street segments (Rosser et al.,
2017; Weisburd et al., 2004), some suggest street segments and intersections
(Braga et al., 2010; Wheeler et al., 2016), and others have suggested targeting
specif‌ic addresses (Eck et al., 2007; Lee & Eck, 2019; Sherman et al., 1989).
Using a hierarchical clustering technique allows one to avoid needing to specify
such a spatial unit of analysis up front, and can be used on address based crime
data to identify a reasonable resolution for a particular hot spot. In the micro
place hot spots we identify, we subsequently show some are one or two
addresses clustered together, others are a street segment, and others are an
agglomeration of several nearby street segments.
The second novel contribution of this work is to construct hot spots using law
enforcement cost of responding to crime estimates, which is related to prior
research creating hot spots using crime harm scores (Macbeth & Ariel, 2019;
Ignatans & Pease, 2016; Ratcliffe, 2015; Sherman et al., 2016). Crime harm has
been characterized in prior research by either asking survey respondents to rank
different crimes (Wolfgang, 1985), or by translating sentencing decisions to
create harm weights (Ratcliffe, 2015), with the ultimate goal that policing
resources are better allocated relative to the harm a particular crime impacts
on the community, as opposed to counting all crime equal (Sherman &
Cambridge University Associates, 2020).
In place of crime harm scores however, here we use average Uniform Crime
Report (UCR) cost of crime estimates (per law enforcement) for Texas as
weights in the DBSCAN algorithm (Hunt et al., 2019). This provides more
interpretable hot spot summaries in terms of direct cost of crime estimates rel-
evant to police departments. These cost of crime estimates can be used to pro-
vide more actionable information in terms of cost-benef‌it analysis for police
departments when planning a hot spots policing strategy.
The third novel contribution of the study is to evaluate the predictive accu-
racy of our identif‌ied cost of crime weighted hot spots using historical data to
predict future crimes. While prior work has mapped harm spots (Curtis-Ham &
Walton, 2017; Fenimore, 2019; Norton et al., 2018; Weinborn et al., 2017), the
majority of that work has simply focused on crime concentration, and has not
assessed the predictive accuracy of a technique to create such harmspots (for an
160 Police Quarterly 24(2)

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT