Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection

Caplan, Joel M.; Kennedy, Leslie W.; Piza, Eric L.; Barnum, Jeremy D.

doi:10.1007/s12061-019-09294-7

Cited by 22 publications

(15 citation statements)

References 48 publications

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“…Gun violence is clustered in both time and space, and the highest risk identified for gun violence is identified at open drug scenes in vulnerable neighbourhoods in the weeks following an initial shooting event. This is in line with findings that crime prediction works best when combining geographical risk factors with crime data, rather than just using one of the two (Caplan, Kennedy, Piza, & Barnum, 2019).…”

Section: Discussionsupporting

confidence: 89%

Open drug markets, vulnerable neighbourhoods and gun violence in two Swedish cities

Gerell

Sturup

Magnusson

et al. 2021

Journal of Policing, Intelligence and Counter Terrorism

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Gun violence is a serious issue in many countries across the globe. It has been shown that there is an elevated risk for a further shooting nearby within a short time span of a shooting incident, so-called near-repeat patterning. The present study presents new evidence on near-repeat patterning in Sweden, with a focus on neighbourhoods which the police have labelled as 'vulnerable'deprived neighbourhoods where criminal networks have a large impact on local communities. Such neighbourhoods tend to have open drug markets, and to have high levels of gun violence. The present paper analyses the association of open drug markets and vulnerable neighbourhoods with gun violence and near-repeat patterning of gun violence in two Swedish cities. Our findings suggest that gun violence is strongly concentrated on open drug markets in vulnerable neighbourhoods, and that those locations in addition exhibit high risks for repeat shootings after an initial shooting event. We propose that the police can use this knowledge to improve practices to prevent or disrupt gun violence.

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Section: Discussionsupporting

confidence: 89%

Open drug markets, vulnerable neighbourhoods and gun violence in two Swedish cities

Gerell

Sturup

Magnusson

et al. 2021

Journal of Policing, Intelligence and Counter Terrorism

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show abstract

“…Here we combine the four different models into one risk surface, and illustrated that it was quite effective and predicting future homeless related crimes. We suspect that incorporating all of the models increased predictive accuracy, as prior work has ensembled different predictions with RTM and illustrated increases in predictive accuracy (Caplan et al, 2019;Drawve et al, 2019). Although future work may further examine whether specific offending or victimization is better predicted in isolation, or whether other models will provide more accurate predictions in space and time.…”

Section: Discussionmentioning

confidence: 97%

Using risk terrain modeling to predict homeless related crime in Los Angeles, California

Yoo

Wheeler

2019

Applied Geography

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We apply Risk Terrain Modeling (RTM) to identify the factors that predict homeless related crime at micro grid cells in Los Angeles, CA. We find that place based factors predicting whether homeless individuals are victimized or the offender being homeless are largely consistent with one another. Out of 26 different crime attractors and generators, prior drug arrests, homeless shelters, and bus stops are the three biggest factors in predicting homeless related crime. We show how the RTM model can effectively forecast future homeless related crimes as well. This suggests that targeted spatial strategies can reduce both homeless offending and victimization risk. Given that the majority of homeless individuals are only intermittently homeless, place based strategies may be a more an effective way to limit risk than strategies that focus on individuals.

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“…This included a National Institute of Justice multicity study which found that policing strategies targeted at high risk places resulted in as much as 35% fewer gun crimes, 33% fewer motor vehicle thefts, and 42% fewer robberies, compared to control areas (Kennedy, et al 2018;Nolette, 2016). Other research has demonstrated the capabilities of RTM for actionable crime analysis and resource allocation, and the strong place-based predictive validity that it can attain (e.g., Caplan, Kennedy, Piza and Barnum, 2019;Garnier, Caplan, and Kennedy, 2018;Drawve et al, 2017;Ohyama and Amemiya, 2018). RTM was used by ACPD to assess the spatial nature of robbery incidents as they related specifically to Atlantic City's environmental backcloth (Barnum et al, 2017).…”

Section: Risk Terrain Modeling Analysismentioning

confidence: 99%

“…A risk terrain model to inform the intervention strategy (that was slated to begin February 1, 2017) was produced to diagnose the most significant attractors/generators of robbery incidents that had occurred within the recent past two months (from December 2016 through January 2017; N=51). This procedure for analyzing two months of data with RTM to make next-month predictions is recommended by Caplan and Kennedy (2016) and has been demonstrated in several research studies to be valid and reliable (e.g., Caplan, Kennedy, Piza and Barnum, 2019;Ohyama and Amemiya, 2018). The average street block length in Atlantic City is 308 feet; places equal to 154 feet by 154 feet were the units of analysis.…”

Section: Risk Terrain Modeling Analysismentioning

confidence: 99%

Data-Informed Crime Prevention at Convenience Stores in Atlantic City

Kennedy

Caplan

Drawve

2021

Preprint

Self Cite

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The Atlantic City Police Department intervened to reduce robberies with an evidencebased approach. Informed by risk terrain modeling and hot spot analysis, police commanders implemented a place-based intervention focused around convenience stores. Target areas throughout the city were reprioritized each month to create a dynamic deployment strategy that efficiently allocated resources to the most vulnerable places. Risk reduction actions, such as business checks, were favored over law enforcement against people. Robberies significantly decreased by over 37% within four months. There was a significant spatial diffusion of benefits and there were fewer arrests, as should be expected with fewer crimes and a tactical place-based, not person-oriented, approach. Implications for policy and practice are discussed within the contexts of rapid evidence-based police responses to urgent crime problems, police culture, and data analytics.

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Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection

Cited by 22 publications

References 48 publications

Open drug markets, vulnerable neighbourhoods and gun violence in two Swedish cities

Open drug markets, vulnerable neighbourhoods and gun violence in two Swedish cities

Using risk terrain modeling to predict homeless related crime in Los Angeles, California

Data-Informed Crime Prevention at Convenience Stores in Atlantic City

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