Stable and Fluid Hotspots of Crime: Differentiation and Identification

Johnson, Shane D.; Lab, Steven P.; Bowers, Kate

doi:10.2148/benv.34.1.32

Cited by 55 publications

(53 citation statements)

References 20 publications

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“…This way, near-repeats occur at locations where street robberies generally tend to occur without especially considering the weekday. Johnson et al [31] suggest that, due to the stability of hotspots, it is likely to require complex crime prevention action such as a greater allocation of police resources or a redesign of the physical environment.…”

Section: Discussionmentioning

confidence: 99%

“…These near-repeat patterns will finally be compared to hotspots of the total dataset of street robberies. While stable hotspots are more likely to need complex crime prevention actions, such as a greater allocation of police resources or a redesign of the physical environment, temporally unstable hot spots indicate short-term crime outbreaks that have an impact on the temporal length for the allocation of police resources [31]. In our discussion, we will focus on how this information can be useful for law enforcement agencies to predict future victimizations and optimize crime prevention strategies of police resources.…”

Section: Research Objectivesmentioning

confidence: 99%

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Evaluating the Impact the Weekday Has on Near-Repeat Victimization: A Spatio-Temporal Analysis of Street Robberies in the City of Vienna, Austria

Glasner

Leitner

2016

IJGI

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Abstract:The near-repeat phenomenon refers to the increased risk of repeat victimization not only at the same location but at nearby locations up to a certain distance and for a certain time period. In recent research, near-repeat victimization has been repeatedly confirmed for different crime types such as burglaries or shootings. In this article the near-repeat phenomenon is analyzed for each day of the week separately. That is, the near-repeat pattern is evaluated for all consecutive Mondays, Tuesdays, Wednesdays, etc. included in the dataset. These consecutive weekdays represent the fictive set of consecutive dates to allow for spatial and temporal analysis of crime patterns. Using these principles, it is hypothesized that street robberies cluster in space and time and by the same day of the week. This research analyzes street robberies from 2009 to 2013 in Vienna, Austria. The overall research goal investigates whether near-repeat patterns of robberies exist by weekdays and in an additional step by time of day, and whether these near-repeat patterns differ from each other and from purely spatial patterns. The results of this research confirm the existence of near-repeat patterns by weekday and especially by time of day. Distinctive locations have been identified that differ greatly per weekday and time of day. Based on this information, law enforcement agencies in Austria can optimize strategic planning of police resources in combating robberies.

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Section: Discussionmentioning

confidence: 99%

Section: Research Objectivesmentioning

confidence: 99%

Evaluating the Impact the Weekday Has on Near-Repeat Victimization: A Spatio-Temporal Analysis of Street Robberies in the City of Vienna, Austria

Glasner

Leitner

2016

IJGI

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“…Compared to the relatively permanent built environment modifications that result from land use planning and urban design, law enforcement can change the spatial and temporal distribution of resources in anticipation of recurring seasonal crime trends (Johnson et al, 2008).…”

Section: Spatio-temporal Crime Patterns and Time-varying Regression Cmentioning

confidence: 99%

Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends

Quick

Law

2017

Environment and Planning B: Urban Analytics and City Science

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“…Bus stops were then ranked by the frequency of DIRs, whereas some bus stops were removed from the list (prior to random assignment) if they did not meet the criteria below: (a) Bus stops must not be spatially auto-correlated. K nearest Local Indicators of Spatial Association (LISA) were used (K = 2); (b) the coefficient of variation (CoV) must be less than or equal to 0.5 (Johnson et al 2008); A CoV close to zero indicates a temporally stable pattern of DIRs between 2012 and 2013; and (c) Police assistance must have been required in at least 33.3 % of the DIRs at each bus stop. These criteria ensured that high frequency bus stops were not too close to each other, were temporally stable and indicated the presence of police recorded crime.…”

Section: Bus Stops As Hotspotsmentioning

confidence: 99%

“…The stability of the evidence has led Weisburd and his colleagues to refer to this phenomenon as the 'law of concentration of crime in place' Weisburd 2015). Notably, with these spatial concentrations there are also temporal concentrations in terms of times of the day, days of week and certain months of the year (Farrell and Pease 1994;Ratcliffe 2004;Johnson et al 2008;Townsley 2008)-which means that the more precise characterization of the phenomenon should be the 'law of concentration of crime in place and time. ' Certain attributes of the places of crime are correlated with higher frequencies of incidents.…”

Section: Introduction Crime and Placementioning

confidence: 99%

Predictable Policing: Measuring the Crime Control Benefits of Hotspots Policing at Bus Stops

Ariel

Partridge

2016

J Quant Criminol

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Objectives A fairly robust body of evidence suggests that hotspots policing is an effective crime prevention strategy. In this paper, we present contradictory evidence of a backfiring effect.Methods In a randomized controlled trial, aimed at reducing crime and disorder, London's 'hottest' 102 bus-stops were targeted. Double patrol teams of Metropolitan Police Service uniformed officers visited the stops three times per shift (12:00-20:00), 5-times per week, for a duration of 15 min, over a 6 month period. Crucially, officers arrived and departed the bus stop on a bus, with significantly less time spent outside the bus stop setting. Outcomes were measured in terms of victim-generated crimes reported to the police and bus driver incident reports (DIRs), within targeted and catchment areas. We used adjusted Poissonregression models to compare differences in pre-and post-treatment measures of outcomes and estimated-marginal-means to illustrate the treatment effect.Results DIRs went down significantly by 37 % (p = 0.07) in the near vicinity of the bus stops (50 m), by 40 % in the 100 m catchment area (p = 0.04) and marginally and nonsignificantly in the farthest catchment (10 %; p = 0.66), compared to control conditions. However, victim-generated crimes-the primary outcome measured in previous experiments-increased by 25 % (p = 0.10) in the near vicinity, by 23 % (p = 0.08) and 11 % (p B 0.001) within the 100-150 m catchment areas, respectively. Conclusions These findings illustrate the role of bounded-rationality in everyday policing: reductions in crime are predicated on an elevated perceived risk-of-apprehension. Previous studies focused on clusters of addresses or public facilities, with police moving freely and unpredictably within the boundaries of the hotspot, but the patrol areas of officers in this experiment were limited to bus stops so offenders could anticipate their movements. Hotspots policing therefore backfires when offenders can systematically and accurately predict the temporal and spatial pattern of long-term targeting at a single location.

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Stable and Fluid Hotspots of Crime: Differentiation and Identification

Cited by 55 publications

References 20 publications

Evaluating the Impact the Weekday Has on Near-Repeat Victimization: A Spatio-Temporal Analysis of Street Robberies in the City of Vienna, Austria

Evaluating the Impact the Weekday Has on Near-Repeat Victimization: A Spatio-Temporal Analysis of Street Robberies in the City of Vienna, Austria

Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends

Predictable Policing: Measuring the Crime Control Benefits of Hotspots Policing at Bus Stops

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