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Quantifying the Exposure of Street Segments to Drinking Places Nearby

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Abstract

Objectives

Introduce and test the relative efficacy of two methods for modeling the impact of cumulative ‘exposure’ to drinking facilities on violent crime at street segments.

Methods

One method, simple count, sums the number of drinking places within a distance threshold. The other method, inverse distance weighted count, weights each drinking place within a threshold based on its distance from the street segment. Closer places are weighted higher than more distant places. Distance is measured as the street length from a street segment to a drinking place along the street network. Seven distance thresholds of 400, 800, 1,200, 1,600, 2,000, 2,400 and 2,800 feet are tested. A negative binomial regression model controlling for socio-economic characteristics, opportunity factors and spatial autocorrelation is used to evaluate which of the measure/threshold combinations produce a better fit as compared to a model with no exposure measures.

Results

Exposure measured as an inverse distance weighted count produces the best fitting model and is significantly related to violent crime at longer distances than simple count (from 400 to 2,800 feet). Exposure to drinking places using a simple count is significantly related to violent crime up to 2,000 feet. Both models indicate the influence of drinking places is highest at shorter distance thresholds.

Conclusions

Both researchers and practitioners can more precisely quantify the influence of drinking places in multivariate models of street segment level violent crime by incorporating proximity in the development of a cumulative exposure measure. The efficacy of using exposure measures to quantify the influence of other types of facilities on crime patterns across street segments should be explored.

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Notes

  1. For similar arguments for the importance of considering the proximity of facilities, please see Bernasco and Block (2011).

  2. Thirty-eight studies have been conducted linking alcohol and a variety of crime types at geographic levels of zip code and smaller. Only the earliest and most robust studies are cited here.

  3. These studies used different bandwidths with the goal of developing the best representation of a continuous surface of event density. The term threshold distance is most often used with vector analysis while bandwidth has received attention as it relates to the generation of kernel density maps using raster data analysis (Bailey and Gatrell 1995; McLafferty et al. 2000). Here, the goal of a threshold distance is to define the distance of likely spatial interaction between places.

  4. Ratcliffe (2011, 2012) has pursued statistical tests to quantify the exact geographic extent of a facilities criminogenic influence. Since this research primarily interested in quantifying the cumulative effect of nearby drinking on street segments the exact distances are measured within set thresholds.

  5. Groff (2013) used street segments as the unit of analysis and measured the effect of the presence of drinking places on the total amount of crime at that street segment. However, the study did not control for socio-economic characteristics.

  6. There are several additional methods that could be used to weight distances to reflect distance decay (e.g., IDW Squared, Quartic, and Exponential). Extant research offers limited guidance on the most appropriate choice. This research takes a systematic approach and tests the most straightforward option, inverse distance weighted (IDW).

  7. McCord and Ratcliffe (2009) developed a facility-focused measure to more accurately quantify the amount of crime associated with particular land uses and facilities. This is a variation of kernel density they termed ‘intensity value analysis’. The drawback to this technique for comparisons of different bandwidths is that the value assigned to a facility changes as the threshold distance changes. For example, a facility 1,000 feet from a place would be assigned an inverse distance weight of .167 under a 1,200 foot bandwidth and .643 under a 2,800 foot bandwidth. As a consequence, the intensity values would not be suitable for comparison across bandwidths.

  8. The mean length of streets in Seattle was 387 feet and was calculated based on street blocks defined as both sides of a street between two intersections (limited access highways and highway ramps were excluded from the analysis). The figure was rounded to 400 feet for convenience.

  9. The larger study was entitled “Understanding Developmental Crime Trajectories at Places: Social Disorganization and Opportunity Perspectives at Micro Units of Geography” and was funded by the National Institute of Justice under award number 2005-IJ-CX-0006. Data are available through the Interuniversity Consortium for Political and Social Research (ICPSR).

  10. All geocoding was done in ArcGIS 9.1 using a geocoding locator service with an alias file of common place names to improve the hit rate. The geocoding locater used the following parameters: spelling sensitivity = 80, minimum candidate score = 30, minimum match score = 85, side offset = 0, end offset 3 percent, and Match if candidates tie = no. Manual geocoding was done on unmatched records in ArcGIS 9.1 and then in ArcView 3.x using the ‘MatchAddressToPoint’ tool (which allowed the operator to click on the map to indicate where an address was located) to improve the overall match rate. Research has suggested hit rates above 85 % are reliable (Ratcliffe 2004). The final geocoding percentage for crime incidents was 97.3 %.

  11. Following Weisburd et al. (2011) all events for which a report was taken are included except those: (1) which occur at an intersection, (2) whose location was given as a police precinct or police headquarters; and (3) those which occur on the University of Washington campus. Unfortunately, data on crime from the University of Washington campus were not geocoded and provided to the Seattle Police Department after 2001. Efforts to obtain data directly from the University of Washington were unsuccessful.

  12. Data detailing all businesses in the zip codes containing Seattle were purchased from InfoUSA and geocoded by the researcher. The zip codes incorporated land outside the city limits of Seattle so the geocoding hit rate was impossible to calculate accurately.

  13. For more details regarding how the variables were constructed and the theoretical arguments for their use please see the original study (Weisburd et al. 2012).

  14. The total sales and employment variables were used as proxies for the non-resident population who use a place but see Andresen (2006) for an alternative method of calculating populations at specific places.

  15. Both measures of exposure were created using the Network Analyst’s Origin and Destination Cost Matrix in ArcGIS 9.3©. The geoprocessing model was built using ESRI’s Model Builder©. A technical description of the geoprocessing model used is available in Groff (2013). All measures were converted to miles prior to calculation using the base formula [1 – Sqr (distance/5,280)].

  16. The overall model regression results are not discussed but are available upon request from the author.

  17. When using a zero-inflated negative binomial regression, BIC is not computed for the prediction of places with no crime. Thus, only the street segments with at least one violent crime are included in the comparisons.

  18. The original version of this paper included a distance weighted activity (DWA) measure using annual sales. Results indicated both the IDW and the Simple count produced better fitting models than the DWA at all distances. This finding lacked adequate explanation so the model was not included in the paper.

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Acknowledgments

The author is grateful to Alex Piquero, Cathy Spatz Widom and the three anonymous reviewers for helpful comments on previous drafts as well as to Lauren Holt for editorial assistance. Data were collected under the “Understanding Developmental Crime Trajectories at Places: Social Disorganization and Opportunity Perspectives at Micro Units of Geography” led by David Weisburd, Elizabeth Groff and Sue-Ming Yang and funded by the National Institute of Justice (2005-IJ-CX-0006).

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  1. Department of Criminal Justice, Temple University, 531 Gladfelter Hall, 1115 Polett Walk, Philadelphia, PA, 19122, USA

    Elizabeth R. Groff

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Correspondence to Elizabeth R. Groff.

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Groff, E.R. Quantifying the Exposure of Street Segments to Drinking Places Nearby. J Quant Criminol 30, 527–548 (2014). https://doi.org/10.1007/s10940-013-9213-2

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