Neighborhood Level Spatial Analysis of the Relationship Between Alcohol Outlet Density and Criminal Violence |
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Authors: | Heather?R Britt Email author" target="_blank">Bradley?P?CarlinEmail author Traci?L?Toomey Alexander?C?Wagenaar |
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Institution: | (1) Minnesota Department of Education, Safe and Healthy Learners Unit, Roseville, Minnesota, USA;(2) School of Public Health, Division of Biostatistics, University of Minnesota, MMC 303, Minneapolis, Minnesota 55455-0392, USA;(3) School of Public Health, Division of Epidemiology, University of Minnesota, Minneapolis, Minnesota, USA;(4) College of Medicine, Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida, USA |
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Abstract: | Misuse of alcohol is a significant public health problem, potentially resulting in unintentional injuries, motor vehicle crashes,
drownings, and, perhaps of greatest concern, serious acts of violence, including assaults, rapes, suicides, and homicides.
Although previous research establishes a link between alcohol consumption increased levels of violence, studies relating the
density of alcohol outlets (e.g., restaurants, bars, liquor stores) and the likelihood of violent crime have been less common.
In this paper we test for such a relationship at the small area level, using data from 79 neighborhoods in the city of Minneapolis,
Minnesota. We adopt a fully Bayesian point of view using Markov chain Monte Carlo (MCMC) computational methods as available
in the popular and freely available WinBUGS language. Our models control for important covariates (e.g., neighborhood racial
heterogeneity, age heterogeneity) and also account for spatial association in unexplained variability using conditionally
autoregressive (CAR) random effects. Our results indicate a significant positive relationship between alcohol outlet density
and violent crime, while also permitting easy mapping of neighborhood-level predicted and residual values, the former useful
for intervention in the most at-risk neighborhoods and the latter potentially useful in identifying covariates still missing
from the fixed effects portion of the model. |
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Keywords: | Bayesian Markov chain Monte Carlo alcohol criminal violence neighborhood |
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