Place-based risk factors for aggravated assault across police divisions in Little Rock, Arkansas

Drawve, Grant; Barnum, Jeremy D.

doi:10.1080/0735648x.2016.1270849

Cited by 26 publications

(19 citation statements)

References 38 publications

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“…So although RTM results in a single coefficient describing the effect of a particular crime generator, that effect size may vary in magnitude over the study area. Several applications of RTM have attempted to uncover such spatially varying effects by applying RTM for different areas of the city (Drawve and Barnum 2015;Piza et al 2017) or in different jurisdictions (Barnum et al 2017).…”

Section: Drawbacks Of Risk Terrain Modelingmentioning

confidence: 99%

Mapping the Risk Terrain for Crime Using Machine Learning

Wheeler

Steenbeek

2020

J Quant Criminol

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Objectives: We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the 'black box' of Random Forests, considerably improving their interpretability. Methods: We generate long-term crime forecasts for robberies in Dallas at 200 by 200 feet grid cells that allow spatially varying associations of crime generators and demographic factors across the study area. We then show how using interpretable model summaries facilitate understanding the model's inner workings. Results: We find that Random Forests greatly outperform Risk Terrain Models and Kernel Density Estimation in terms of forecasting future crimes using different measures of predictive accuracy, but only slightly outperform using prior counts of crime. We find different factors that predict crime are highly non-linear and vary over space. Conclusions: We show how using black-box machine learning models can provide accurate micro placed based crime predictions, but still be interpreted in a manner that fosters understanding of why a place is predicted to be risky.

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Section: Drawbacks Of Risk Terrain Modelingmentioning

confidence: 99%

Mapping the Risk Terrain for Crime Using Machine Learning

Wheeler

Steenbeek

2020

J Quant Criminol

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“…Six of the seven social and physical environmental measures in the RTM significantly predicted future gun crime locations: on-site consumption and off-site consumption alcohol establishments; fast food establishments; drug incidents; percentage of black residents and percentage of male residents. Drawve and Barnum (2018) applied RTM to aggravated assault and found bus stops and liquor stores to be consistently present as risk factors for different areas in Little Rock.…”

Section: Risk Terrain Modellingmentioning

confidence: 99%

“…Fortunately, risk terrain modelling (hereafter RTM) was developed in the study of urban crime to quantitatively assess the spatial influence of features of the urban landscape to identify areas where criminal activity is likely to emerge or persist. RTM has been applied to many different urban crimes including burglaries (Gale and Hollernan 2013;Moreto et al 2014), robberies (Kennedy and Gaziarifoglu 2011;Dugato 2013), shootings (Caplan et al 2011;Drawve et al 2016a), aggravated assaults (Piza et al 2011;Kennedy et al 2016;Anyinam 2015;Kocher and Leitner 2015), and assaults on police (Drawve and Barnum 2018). Because RTM includes contextual information relevant to the social and physical environment, it should be an appropriate approach to assessing terrorism risk.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors for Violent Dissident Republican Incidents in Belfast: A Comparison of Bombings and Bomb Hoaxes

2019

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Objectives To identify risk factors for bombings and bomb hoaxes committed by dissident Republicans in Belfast, Northern Ireland. Methods Risk terrain modelling (RTM) was applied to each type of incident to identify significant risk layers. Results Previous protests and riots [relative risk value (RRV) of 14.07; spatial influence (SI) of 100 m], punishment attacks (RRV 6.56; SI 300 m) and areas dense with pubs and bars (RRV 4.98; SI 200 m) were identified as risk factors for bombings. Punishment attacks (RRV 10.77; SI 100 m), police stations (RRV 8.76; SI of 200 m) and places dense with shops (RRV 6.94; SI 400 m) were identified as risk factors for bomb hoaxes. Descriptive statistics regarding predictive accuracy concluded that half of incidents for both types occurred in high or very high risk cells in a 3-year post-study period. Conclusions RTM could be a useful tool in guiding targeted responses to the dissident Republican threat in Belfast. The results suggest that there is some assessment of risk by the offenders, and that they are selecting targets rationally. Due to the differences in risk factors for the two types of events it can be proposed that there may be differences between targets relevant to ideology and realistic targets with increased chance of success.

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“…Each project used Risk Terrain Modeling (RTM: Caplan et al 2011;Caplan and Kennedy 2016) to diagnose the spatial attractors of criminal behavior and forecast the micro-level places at highest risk of hosting future crime incidents. These analyses were informed by the emerging body of research demonstrating RTM to be a valid crime forecasting tool across a wide variety of crime types, including aggravated assault (Drawve and Barnum 2017;Kennedy et al 2016), burglary (Caplan et al 2015;Moreto et al 2014), carjacking (Lersch 2017), gun violence Drawve et al 2016;Kennedy et al 2011), motor vehicle theft and recovery , robbery , and public drug selling (Barnum et al 2016). These projects helped to further develop this body of research by using RTM findings to design police interventions in a manner that directly targets spatial risk factors that contribute to the emergence and persistence of crime hot spots ) rather than simply identifying micro-places to serve as target areas of interventions.…”

Section: Introductionmentioning

confidence: 99%

Facilitators and Impediments to Designing, Implementing, and Evaluating Risk-Based Policing Strategies Using Risk Terrain Modeling: Insights from a Multi-City Evaluation in the United States

Piza

Kennedy

Caplan

2018

Eur J Crim Policy Res

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The contemporary policing literature contains numerous examples of partnerships between academic researchers and police agencies. Such efforts have greatly contributed to evidence-based policing by increasing the knowledge base on effective strategies. However, research has demonstrated that successful collaboration between researchers and practitioners can be a challenge, with various organizational and inter-agency factors presenting difficulties at various stages of the process. Additionally, applied research can oftentimes face implementation challenges when the time comes to convert research into practice. The current study contributes to the literature by discussing researcher/practitioner partnerships and program implementation in the context of a multi-city risk-based policing project in the United States. We conceptualize police interventions as contingent on four distinct phases: 1) problem analysis, 2) project design, 3) project implementation, and 4) project evaluation. In this project, the research partners were able to successfully complete each phase in certain cities while the project experienced difficulty at one or more phases in other cities. We discuss these disparate experiences, identifying factors that facilitate or impede successful completion of each step. Policy implications and recommendations for future risk-based policing interventions are discussed.

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Place-based risk factors for aggravated assault across police divisions in Little Rock, Arkansas

Cited by 26 publications

References 38 publications

Mapping the Risk Terrain for Crime Using Machine Learning

Mapping the Risk Terrain for Crime Using Machine Learning

Risk Factors for Violent Dissident Republican Incidents in Belfast: A Comparison of Bombings and Bomb Hoaxes

Facilitators and Impediments to Designing, Implementing, and Evaluating Risk-Based Policing Strategies Using Risk Terrain Modeling: Insights from a Multi-City Evaluation in the United States

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