![]() ![]() Conflicting findings in the field may be in part due to error in the way that researchers measure alcohol outlet density. Spatial access methods may offer conceptual strengths over proximity and mean distance. Proximity, mean distance, and spatial access methods yielded the best model fits and had the lowest levels of error in this urban setting. Proximity, mean distance, and spatial access methods consistently detected an association between outlets and crime and produced comparable model fits. Eight models that used a count of alcohol outlets and/or violent crimes failed to detect an association between outlets and crime, and three other count‐based models detected an association in the opposite direction. The inference depended on the measurement methods used. Choropleth maps, partial R², Akaike's Information Criterion, and root mean squared error guided determination of which models yielded lower error and better fit. Negative binomial regression was used for count outcomes and linear regression for proximity and spatial access outcomes. #DESTROY THE BARRICAD IN DELTA FORCE XTREME 2 SERIES#We then weighted these CB‐level measures to the census tract level (n=197) and conducted a series of regressions. calculated alcohol outlet density and violent crime at the census block (CB) level (n=13,016). ![]() Violent crime data (n=11,815) were obtained from the Baltimore City Police Department and included homicides, aggravated assaults, rapes, and robberies in 2016. ![]() The objective of this analysis is to compare measurement methods – counts, proximity, mean distance, and spatial access – of calculating alcohol outlet density and violent crime using data from Baltimore, Maryland. ![]()
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