Redrawing Hot Spots of Crime in Dallas, Texas

• DOI: 10.1177/1098611120957948
• Author: Sydney Reuter,Andrew P. Wheeler
• Published date: 01 June 2021
• Date: 01 June 2021
• Subject Matter: Articles

Article

Redrawing Hot Spots of

Crime in Dallas, Texas

Andrew P. Wheeler

1

and

Sydney Reuter

2

Abstract

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.

Keywords

hot-spots, clustering, prediction, cost-benefit-analysis

Introduction

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

1

HMS, Irving, TX, United States

2

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.

Email: apwheele@gmail.com

Police Quarterly

2021, Vol. 24(2) 159–184

!The Author(s) 2020

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DOI: 10.1177/1098611120957948

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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)