Investigating Crime Pattern Stability at Micro-Temporal Intervals: Implications for Crime Analysis and Hotspot Policing Strategies

DOI10.1177/0734016821996785
Date01 June 2021
Published date01 June 2021
AuthorTimothy C. Hart
Subject MatterArticles
Article
Investigating Crime Pattern
Stability at Micro-Temporal
Intervals: Implications for
Crime Analysis and Hotspot
Policing Strategies
Timothy C. Hart
1
Abstract
Studies of crime hotspot forecasts use various metrics to describe different characteristics of
prediction patterns. However, few investigations consider how the stability of crime hotspot,
estimated at relatively short temporal intervals, can impact hotspot policing efforts. In response,
using address-level incident location data that were collected from six law enforcement agencies in
the United States, the current study examines the daily stability of crime hotspots that were esti-
mated over a 1-year period. Results suggest that micro-temporal stability patterns in crime hotspot
forecasts are dependent on crime type, jurisdiction, and the interaction between these two factors.
Implications for crime analysis and future research are discussed.
Keywords
crime and place, micro-temporal analysis, crime forecasting
Hotspot policin g is an effective crime red uction and prevention strat egy that relies on analytic meth ods
designed to identify nonrandom patterns within crimelocation information. Many of these techniques
involve forecasting spatiotemporal crime patterns, based on patterns that have been observed in the
past. The effectiveness of hotspot policing is largely dependent on the results of these estimation and
prediction methods, and an increasing number of metrics designed to evaluate the performance and
characteristics of hotspot mapping results have emerged from the literature as a result.
One way in which the characteristics of crimehotspot patterns can be described is by measuring the
stability of crimeforecasts over time. For example, Adepejuand colleagues (2016) recently examined
multiple prediction methods using a Dynamic Variability Index (DVI) and found that kernel density
estimation(KDE) produceshighly stable forecastsacross consecutiveprediction intervals.In describing
1
Department of Criminology and Criminal Justice, University of Tampa, FL, USA
Corresponding Author:
Timothy C. Hart, Department of Criminology and Criminal Justice, University of Tampa, 401 W Kennedy Blvd, Tampa,
FL 33606, USA.
Email: thart@ut.edu
Criminal Justice Review
2021, Vol. 46(2) 173-189
ª2021 Georgia State University
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DOI: 10.1177/0734016821996785
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their findings, researchers noted that evaluating hotspot stability is an important aspect of crime pre-
diction because subtle changes in hotspot patterns across estimation intervals may adversely impact
hotspot policingefforts. However, extanthotspot research focuses primarilyon the predictive accuracy
of various analytic techniques, rather thancrime pattern stability.
Nevertheless, research into crime pattern stability has produced empirical knowledge that has
been used to improve hotspot policing strategies. For example, Johnson et al. (2008) analyzed the
stability of crime patterns produced from KDE maps over multiple interpolation periods. They found
that hotspots with the highest concentrations of crime were not always the most stable. Based on
these results, they offered recommendations for how simple KDE methods could be modified so that
they could produce crime forecasts that could better inform strategic crime-reduction efforts.
Although actionable hotspot policing information was derived from this investigation, crime loca-
tion data were aggregated to a 6-month
1
interval, which could have masked important variation in
crime pattern stability that could have occurred within the interval.
Finally, many scholars have studied micro-temporal patterns in crime data, but few have exam-
ined the micro-temporal stability of crime hotspots (Adepeju et al., 2016, is a noteworthy exception).
For example, researchers have investigated changes in intraday crime risk (Lemieux & Felson,
2012) and the ebb and flow of the ambient population throughout the day and its correlation to
when and where crime occurs (Andresen, 2011). Analytic techniques used to detect micro-temporal
patterns in crime data have also been developed, such as Ratcliffe’s (2002) aoristic analysis.
2
However, additional research into crime pattern stability across micro-temporal intervals is needed
because understanding crime hotspot stability at a micro-temporal level can help improve our ability
to proactively combat crime and make our communities safer. The current study was undertaken in
response to this need for additional research.
The remainder of this article is organized in the following manner. First, a review of the relevant
empirical literature is provided, focusing on crime hotspot mapping techniques and the metrics
commonly used to assess them, with particular attention paid to the time intervals commonly used
in crime hotspot mapping. Next, the data and methods employed in the current study are described,
followed by a presentation of the results. The article concludes with a discussion of the current
findings and their implications for hotspot analysis. Limitations of the current investigation are also
presented, and recommendations for future research are offered.
Literature Review
The law of crime concentration represents decades of empirically based knowledge about crime and
place, summarized in a single statement: “[F]or a defined measure of crime at a specific microgeo-
graphic unit, the concentration of crime will fall within a narrow bandwidth of percentages for a
defined cumulative proportion of crime” (Weisburd, 2015, p. 133). Criminological theories that
attempt to explain these crime patterns argue that they are linked to the criminal opportunit ies
created by the built environment (Clarke, 1995; Jeffery, 1971; Newman, 1972), to the daily routine
activity patterns of potential offenders and victims as they intersect in space and time (Cohen &
Felson, 1979; Felson & Eckert, 2019), and to places within the environmental backcloth that gen-
erate and attract crime (Brantingham & Brantingham, 1984, 1991; Brantingham et al., 2016). These
insights play an important role in supporting law enforcement efforts to reduce and prevent crime
through hotspot policing.
Identifying Crime Patterns
Crime analysis is a fundamental component of most hotspot policing strategies, with identifying
higher-than-normal concentrations of crime being one of the analysts’ most important objectives
174 Criminal Justice Review 46(2)

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