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Enhancing Data-Driven Law Enforcement Efforts: Exploring how Risk Terrain Modeling and Conjunctive Analysis Fit in a Crime and Traffic Safety Framework

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Abstract

Law enforcement’s examination of vehicle crashes is often nested in the Data-Driven Approaches to Crime and Traffic Safety (DDACTS) framework which highlights the importance of hot spot analysis. To assist law enforcement efforts, this study explores how two additional spatial techniques, namely risk terrain modeling (RTM) and conjunctive analysis of case configurations (CACC), could be incorporated within the DDACTS framework. RTM was utilized to identify how the built, physical environment contributed to the risk of traffic incidents. RTM identified 6 risk factors related to the occurrence of vehicle crashes, and high-risk places were compared to hot spots on predictive accuracy. CACC was used to explore configurations likely to result in traffic incidents for the priority places. Our findings support the Theory of Risky Places and fit within a vulnerability-exposure framework, providing law enforcement with guidance for identifying places where vehicle crashes are likely to occur in the future. In addition to providing insight for law enforcement, we discuss how law enforcement can develop working partnerships with stakeholders capable of preventing and/or reducing traffic incidents, which is in line with the general DDACTS framework.

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Notes

  1. Gas and convenience stores also included liquor and tobacco stores. There were overlap between many of the descriptions so we aggregated to one factor.

  2. A spatial lag option is not currently available in the RTMDx software (see Heffner, 2013).

  3. Parameters in the PAI formula include: n, which represents the number of crimes in areas where crimes are predicted to occur; N, which is the number of crimes in the study area; a, which represents the area of areas where crimes are predicted to occur, and A, which is the area of the study area.

  4. Miethe et al. (2008) also provide syntax for other statistical packages. Additionally, Barnum (2016) developed a tutorial for the utilization of conjunctive analysis with RTM, referred to as Conjunctive Analysis of Risk Factor Configurations (CARFC).

  5. It is possible to do this manually, but RTMDx does not currently output the combination configurations.

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Authors and Affiliations

  1. Department of Sociology and Criminology, University of Arkansas, 211 Old Main, Fayetteville, AR, 72701, USA

    Grant Drawve & Hannah Steinman

  2. Department of Criminal Justice and Criminology, Georgia Southern University, Statesboro, GA, USA

    Jonathan Grubb

  3. Green Bay Police Department, Green Bay, WI, USA

    Michelle Belongie

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  1. Grant Drawve
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  2. Jonathan Grubb
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  4. Michelle Belongie
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Correspondence to Grant Drawve.

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Drawve, G., Grubb, J., Steinman, H. et al. Enhancing Data-Driven Law Enforcement Efforts: Exploring how Risk Terrain Modeling and Conjunctive Analysis Fit in a Crime and Traffic Safety Framework. Am J Crim Just 44, 106–124 (2019). https://doi.org/10.1007/s12103-018-9449-3

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