TY - JOUR
T1 - Methods for Evaluating the Association Between Alcohol Outlet Density and Violent Crime
AU - Trangenstein, Pamela J.
AU - Curriero, Frank C.
AU - Jennings, Jacky M.
AU - Webster, Daniel
AU - Latkin, Carl
AU - Eck, Raimee H.
AU - Jernigan, David
N1 - Funding Information:
This report was supported by Cooperative Agreement Number 5U48DP005045 from the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the Department of Health and Human Services. The project described was supported by Award Numbers T32AA007240, Graduate Research Training in Alcohol Problems: Alcohol-related Disparities and P50AA005595, Epidemiology of Alcohol Problems: Alcohol-Related Disparities from the National Institute on Alcohol Abuse and Alcoholism. The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.
Publisher Copyright:
© 2019 by the Research Society on Alcoholism
PY - 2019
Y1 - 2019
N2 - Background: The objective of this analysis was to compare measurement methods—counts, proximity, mean distance, and spatial access—of calculating alcohol outlet density and violent crime using data from Baltimore, Maryland. Methods: Violent crime data (n = 11,815) were obtained from the Baltimore City Police Department and included homicides, aggravated assaults, rapes, and robberies in 2016. We calculated alcohol outlet density and violent crime at the census block (CB) level (n = 13,016). We then weighted these CB-level measures to the census tract level (n = 197) and conducted a series of regressions. Negative binomial regression was used for count outcomes and linear regression for proximity and spatial access outcomes. Choropleth maps, partial R2, Akaike's Information Criterion, and root mean squared error guided determination of which models yielded lower error and better fit. Results: The inference depended on the measurement methods used. Eight models that used a count of alcohol outlets and/or violent crimes failed to detect an association between outlets and crime, and 3 other count-based models detected an association in the opposite direction. Proximity, mean distance, and spatial access methods consistently detected an association between outlets and crime and produced comparable model fits. Conclusions: Proximity, mean distance, and spatial access methods yielded the best model fits and had the lowest levels of error in this urban setting. Spatial access methods may offer conceptual strengths over proximity and mean distance. Conflicting findings in the field may be in part due to error in the way that researchers measure alcohol outlet density.
AB - Background: The objective of this analysis was to compare measurement methods—counts, proximity, mean distance, and spatial access—of calculating alcohol outlet density and violent crime using data from Baltimore, Maryland. Methods: Violent crime data (n = 11,815) were obtained from the Baltimore City Police Department and included homicides, aggravated assaults, rapes, and robberies in 2016. We calculated alcohol outlet density and violent crime at the census block (CB) level (n = 13,016). We then weighted these CB-level measures to the census tract level (n = 197) and conducted a series of regressions. Negative binomial regression was used for count outcomes and linear regression for proximity and spatial access outcomes. Choropleth maps, partial R2, Akaike's Information Criterion, and root mean squared error guided determination of which models yielded lower error and better fit. Results: The inference depended on the measurement methods used. Eight models that used a count of alcohol outlets and/or violent crimes failed to detect an association between outlets and crime, and 3 other count-based models detected an association in the opposite direction. Proximity, mean distance, and spatial access methods consistently detected an association between outlets and crime and produced comparable model fits. Conclusions: Proximity, mean distance, and spatial access methods yielded the best model fits and had the lowest levels of error in this urban setting. Spatial access methods may offer conceptual strengths over proximity and mean distance. Conflicting findings in the field may be in part due to error in the way that researchers measure alcohol outlet density.
KW - Alcohol
KW - Alcohol Outlet Density
KW - Spatial Access Measures
KW - Violent Crime
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U2 - 10.1111/acer.14119
DO - 10.1111/acer.14119
M3 - Article
C2 - 31157919
AN - SCOPUS:85068074894
SN - 0145-6008
VL - 43
SP - 1714
EP - 1726
JO - Alcoholism: Clinical and Experimental Research
JF - Alcoholism: Clinical and Experimental Research
IS - 8
ER -