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Vulnerability and Exposure to Crime: Applying Risk Terrain Modeling to the Study of Assault in Chicago

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Abstract

Prior research has applied risk assessment and spatial analysis techniques to the study of violence. This paper builds on those results, tying the practical outcomes of spatial risk analysis methods to broader spatial issues on the articulation of risky places for aggravated assault. We begin by conceptualizing key relationships, addressing the effects of environmental factors on creating distinct, identifiable areas that are conducive to crime. Propositions of the theory of risky places are posed and then empirically tested using a GIS based program, RTMDx, on aggravated assault data in an urban area. Given the current thinking about crime vulnerability based on concentration and spatial influence of features and events, this paper offers an analytical strategy to model risky places that combines the conceptual insights of crime emergence and persistence, advances in geo-spatial analytical techniques, and micro-level data.

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Notes

  1. The layers were not mutually exclusive. However, spatial influence of the convenience store gas stations may be different from the overall dataset, so it made sense to test them on their own.

  2. 14 potential risk factors were tested for spatial influences of both distance and/or density, up to three blocks, whole increments. Nine potential risk factors were tested for spatial influences only as a function of distance. [See page 28 of Caplan et al. (2013b) for a detailed explanation as to why these parameters were chosen.]

  3. (14 factors * 2 operationalizations * 3 blocks) + (9 factors * 1 operationalization * 3 blocks) = 111 variables.

  4. Created by rescaling the grid cell values between the minimum and maximum values.

  5. Results from a Poisson goodness-of-fit test confirmed that the 2012 aggravated assault incident locations (the dependent variable) follow a negative binomial distribution (Pearson gof = 53187.49, p < 0.001).

  6. Moran’s I was 0.24 and was significant at p < 0.001. The Moran’s I analysis was conducted within the Geoda spatial analysis software. Geoda was also used to generate the spatial lag variable (first order Queen Continuity).

  7. The IRR compares to a baseline of zero, in RTM, risk scores have a baseline of 1. So, we subtracted 1 from the mean prior to calculating the interpretation statistics.

  8. For example: Given the expected occurrence of near repeat incidents in Chicago within a certain distance (i.e., 426 ft) and period of time (i.e. one week) from new crime incidents, an analytical strategy for prioritizing new crime incidents could be to evaluate each new crime incident for its propensity to become an instigator for near repeats – based upon the proportion of high-risk places within the expected near repeat bandwidth. Imagine, for instance, a crime analyst plotting a new crime incident on a risk terrain map. She then draws a buffer around it equal to the expected near repeat bandwidth to isolate all the micro-level places within the buffer. Then she identifies all the places within the buffer that are exceptionally high risk, based on the risk terrain model. Now she can advise commanders about the proportion of high-risk places within the buffer (e.g., 87 out of 237 = 37 %). This can inform decisions about whether to allocate resources to that crime’s buffer area given the vulnerable places within it for near repeat crimes over the next 7-day period. This resource allocation decision could be made in consideration of all other recent crime incidents that have occurred in the jurisdiction so that priority can be given to those areas with the greatest propensity for new crimes to emerge in the near future. Although a risk terrain may be time-stable once it is produced (unless actions are taken to affect spatial vulnerabilities), places within the terrain can be very dynamic as each new crime incident creates exposures that subsequently alter the spatial dynamics of crime.

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Kennedy, L.W., Caplan, J.M., Piza, E.L. et al. Vulnerability and Exposure to Crime: Applying Risk Terrain Modeling to the Study of Assault in Chicago. Appl. Spatial Analysis 9, 529–548 (2016). https://doi.org/10.1007/s12061-015-9165-z

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