Methods for Evaluating the Association Between Alcohol Outlet Density and Violent Crime

Pamela J. Trangenstein, Frank C Curriero, Jacky Jennings, Daniel W Webster, Carl A Latkin, Raimee H. Eck, David H. Jernigan

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalAlcoholism: Clinical and Experimental Research
DOIs
StatePublished - Jan 1 2019

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Crime
Alcohols
Censuses
Baltimore
Distance measurement
Rape
Homicide
Police
Law enforcement
Linear regression
Linear Models
Research Personnel

Keywords

  • Alcohol
  • Alcohol Outlet Density
  • Spatial Access Measures
  • Violent Crime

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Toxicology
  • Psychiatry and Mental health

Cite this

@article{3d466f5979e04963b667ff6a092064fd,
title = "Methods for Evaluating the Association Between Alcohol Outlet Density and Violent Crime",
abstract = "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.",
keywords = "Alcohol, Alcohol Outlet Density, Spatial Access Measures, Violent Crime",
author = "Trangenstein, {Pamela J.} and Curriero, {Frank C} and Jacky Jennings and Webster, {Daniel W} and Latkin, {Carl A} and Eck, {Raimee H.} and Jernigan, {David H.}",
year = "2019",
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language = "English (US)",
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T1 - Methods for Evaluating the Association Between Alcohol Outlet Density and Violent Crime

AU - Trangenstein, Pamela J.

AU - Curriero, Frank C

AU - Jennings, Jacky

AU - Webster, Daniel W

AU - Latkin, Carl A

AU - Eck, Raimee H.

AU - Jernigan, David H.

PY - 2019/1/1

Y1 - 2019/1/1

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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