4.3 Article

Quantifying the Exposure of Street Segments to Drinking Places Nearby

Journal

JOURNAL OF QUANTITATIVE CRIMINOLOGY
Volume 30, Issue 3, Pages 527-548

Publisher

SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10940-013-9213-2

Keywords

Drinking places; Facilities; Street segment; Exposure; Inverse distance weighting

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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. 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. 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. 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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