Alcohol outlets and community levels of interpersonal violence:
Spatial density, type of outlet, and seriousness of assault
William Alex Pridemore
Indiana University
Department of Criminal Justice
302 Sycamore Hall
Bloomington, Indiana 47405
812-856-2220
wpridemo@indiana.edu
and
Tony H. Grubesic
Indiana University
Department of Geography
Student Building 120
Bloomington, Indiana 47405
812-855-7971
tgrubesi@indiana.edu
The authors shared equally in the creation of this manuscript. This research was partially
supported by Indiana University’s Faculty Research Support Program and administered by the
Office of the Vice Provost for Research. Both authors are members of the Research Group on
Injuries, Violence, and Victimization at Indiana University.
1
Alcohol outlets and community levels of interpersonal violence:
Spatial density, type of outlet, and seriousness of assault
Abstract
This study examined the association between alcohol outlet density and assault density. This
association recently has received increased attention in several other disciplines. Yet despite the
renewed popularity in criminology of ecological explanations of the variation of crime rates,
community characteristics like the density of alcohol outlets have received little attention in our
discipline. Employing Cincinnati block groups as our units of analysis and controlling for several
structural characteristics often found to be associated with violence rates, we estimated spatially
lagged regression models to determine how the variation in spatial density of alcohol outlets was
related to the variation in the spatial density of assaults. We took into account varying levels of
harm by estimating separate models for simple and aggravated assaults. We also estimated
separate models for off-premise outlets, bars, and restaurants to see if there were differences in
this association by type of outlet. The results revealed a consistent positive and significant
association between outlet density and assault density. This association held for overall, simple,
and aggravated assaults, and for total outlet density and the density of each type of outlet. Further
tests showed the outlet-violence association to be significantly stronger for off-premise outlets
relative to the effects of bars and restaurants, and estimation of attributable fractions showed that
off-premise outlets appear to be responsible for approximately one-quarter and one-third of
simple and aggravated assaults, respectively. We contextualize these findings in the larger
theoretical and empirical literature, suggest how they can be translated into actionable policy,
and discuss the many ways criminologists could contribute to a better understanding of this
phenomenon.
2
Introduction
This study examined the association between alcohol outlets and assault, focusing on the
spatial density of each and on the effects of different types of outlets. While the alcohol-violence
association has long been the subject of criminological research (Wolfgang, 1958), both older
and more recent criminological studies tend to focus on individuals (Felson et al., 2008) or
criminal events (Felson, Burchfield, & Teasdale, 2007; Pridemore & Eckhardt, 2008). With a
few exceptions (e.g., Block & Block, 1995; Fagan, 1993; Nielsen & Martinez, 2003; Parker,
1995; Roncek & Maier, 1991), ecological studies of alcohol and violence are largely absent from
the criminological literature. In recent years, however, such studies have increasingly appeared
in the journals of other disciplines like epidemiology, public health, and geography (e.g., Britt et
al., 2005; Gruenewald et al., 2006; Lipton & Gruenewald, 2002; Lipton et al., 2003: Livingston,
2008; Pridemore, 2004, 2002; Pridemore & Snowden, 2009; Zhu, Gorman, & Horel, 2004).
The near absence in the criminological literature of ecological studies of the alcoholviolence association, especially at the community-level, is intriguing given the reinvigoration of
ecological theory and analysis over the last two decades. While these macro-level studies have
focused considerable effort on better understanding how neighborhood characteristics and
context influence crime, they have largely centered on testing hypotheses generated from social
disorganization (Bursik, 1988; Sampson & Groves, 1989) and related theories like social
cohesion (Nieuwbeerta et al., 2008), social capital (Rosenfeld, Messner, & Baumer, 2001), and
collective efficacy (Sampson, Raudenbush, & Earls, 1997). As a result, other neighborhood
characteristics have received less attention. We believe that alcohol outlets, as a source of
community-level variation in levels of interpersonal violence, deserve greater attention in the
criminological literature. A higher density of outlets in an area means closer proximity and easier
3
availability to an intoxicating substance for residents. Perhaps just as importantly, alcohol outlets
provide a greater number of potentially deviant places. Convenience stores licensed to sell
alcohol may be especially troublesome in this regard, as they often serve not only as sources of
alcohol but also as local gathering places with little formal social control.
A secondary goal of the present paper is thus to stimulate greater interest among
criminologists in the ecological association between alcohol and violence, and in doing so to
reveal the advantages of employing a spatial analytical approach to the study of this
phenomenon. Our analysis contributes to the existing literature in several ways. First, we take
into account differing levels of harm by employing both simple and aggravated assaults as
dependent variables. Second, we recognize that the strength of any association may vary based
upon the type of alcohol outlet under consideration (i.e., off-premise, bar, restaurant). Third, we
use block groups as our units of analysis, providing us with a lower level of aggregation and thus
greater detail and less chance of aggregation bias than studies employing much larger units like
ZIP codes, cities, and states. Fourth, we use a different and more spatially appropriate indicator
of outlet density compared to many prior studies. Fifth, we include in our models several
important socioeconomic and demographic variables that may confound the outlet-violence
association, some of which are missing from prior studies. Sixth, we estimate spatially lagged
regression models in order to control for spatial autocorrelation. Seventh, we estimate the
attributable fraction (AF) for assault due to alcohol outlet density. The AF is the proportion of (in
our case) assaults that would be avoided were the exposure (in our case, alcohol outlets)
eliminated. Finally, we carry out our analyses on a different sample than those used previously,
thus allowing us to examine the generalizeability of prior results. We stress that while there are
reasons to believe that alcohol may exert important individual-level effects on violent behavior,
4
our concern is with the spatial density of alcohol outlets within communities, and thus with how
the variation in the density of alcohol outlets influences the variation among communities in their
density of assaultive violence.
Literature Review
There are several reasons to suspect that the density of alcohol outlets may influence
levels of community violence. We briefly review this literature here and then present findings
from analyses of the relationship from other studies, nearly all of which have appeared in the
journals of other disciplines.
The first reason to expect higher levels of crime and violence where alcohol outlets are
concentrated is largely at the individual-level: greater availability of alcohol means more
consumption by more people, which will lead to more disputes. This is a compositional
argument, for the most part, not a structural one. Whether pharmacological or social, the
disinhibiting effects of alcohol on aggression cannot be ignored, nor can the fact that biology and
social context interact to affect the likelihood that alcohol consumption will lead to violence
(Giancola, Saucier, & Gussler-Burkhardt, 2003; Miczek et al., 1992; Pihl, Lau, & Assaad, 1997).
Studies of violent victims (e.g., Goodman et al., 1986) and offenders (e.g., Fendrich et al., 1995)
have shown that a high percentage of both are under the influence of alcohol at the time of the
violent event. Pernanen (1991) provides detailed potential conceptual frameworks and theoretical
pathways for this type of relationship, and Parker and Rebhun (1995) outline how alcohol likely
fits into several individual-level criminological theories in a way that will increase violence. As
it relates to our study, it is generally accepted that greater alcohol availability in an area is
associated with greater levels of consumption (Blose & Holder, 1987; Parker & Rebhun, 1995).
5
Therefore, a compositional argument can be made about the association between alcohol outlet
density and crime rates.
While this compositional argument should not be ignored, we are more interested in the
ecological or spatial effects of the location and density of alcohol outlets on area violence rates.
At the event and structural levels, ideas from selective disinhibition, routine activities, and social
disorganization overlap in this regard. Just as social disorganization theory leads us to expect
higher rates of crime in certain neighborhoods due to community characteristics, the same can be
said for even smaller and specific locations within these neighborhoods that provide high-risk
areas for crime and violence. Among others, these include street intersections, public
transportation nodes, and alcohol outlets (Block & Block, 1995; Sherman et al., 1989; Stark,
1987). As evidence by Weisburd and his colleagues suggests (Braga et al., 1999; Weisburd et al.,
2006), there is something unique about these spots, as focused efforts of crime control do not
simply displace crime to other areas.
Both bars and off-premise outlets serve as a place where motivated offenders and
potential victims converge (Cohen & Felson, 1979), especially young males who are already at
higher risk for both offending and victimization. These outlets also provide permissive settings
where norms are ambiguous or even supportive of deviant behavior, especially in the context of
relaxed inhibitions that can occur when drinking. Similarly, unkempt areas in and around offpremise alcohol outlets can be a sign that anti-social behavior is condoned in the area, if not
acceptable. Many view these places as time-out periods or moral holidays where the normal rules
governing interpersonal behavior are weakened (Collins, 2008; MacAndrew & Edgerton, 1969).
According to Parker and Rebhun (1995), these weakened normative constraints can interact with
consumption level to overcome active constraint over one’s behavior, thereby resulting in
6
selective disinhibition and increasing the likelihood of deviant and even violent behavior (see
also Pernanen, 1991). The context itself may be enough to overcome any constraint in the
absence of alcohol, but alcohol’s disinhibiting effect can increase the risk. Thus the greater the
concentration of outlets, the greater the likelihood of places where norms governing everyday
behavior might be relaxed.
The empirical literature from other disciplines has established bars and clubs as high-risk
drinking settings (Macdonald et al., 2005; Stockwell et al., 1993), and off-premise outlets can
also be sources of disorder. Of course all alcohol outlets are not the same, and careful research
on bars has shown that a wide range of characteristics can affect significantly the variation in the
amount and seriousness of aggression (Graham, Bernards, et al., 2006; Graham, Tremblay et al.,
2006; Graham et al., 2005; Quigley et al., 2003). Aside from the characteristics mentioned
above, the areas immediately surrounding bars are often places where patrons and others hang
out (and increasingly go to smoke, given the popularity of anti-smoking laws), and social control
can be especially problematic when several bars in the same area close down at the same time for
the night. Similarly, many off-premise outlets are not simply places where people go to buy
alcohol and then leave to take it home with them. Especially in urban areas these outlets are often
convenience stores where people congregate for social interaction. The convenience stores
themselves or the places immediately surrounding them might even serve as a de facto tavern of
sort (Block & Block, 1995).
While it may be that the concentration of alcohol outlets in an area is partially the result
of socially disorganized neighborhoods, these outlets can themselves contribute to
disorganization and decreased social cohesion. Problematic bars and off-premise outlets can
create disorder and reduce collective efficacy in parts of neighborhoods. Just as a larger
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community might be stigmatized, so might a smaller part of it, including very specific areas and
establishments. According to Stark (1987), we can expect more lenient law enforcement in these
types of places, which can in turn make them more attractive for those drawn to crime and
deviance. For all these reasons, community-level variation in the location and density of alcohol
outlets can be expected to be associated with the variation in violence rates among these
communities.
Early studies of the association between alcohol outlet density and violence provided
inconsistent results. Two studies by Gorman and colleagues (Gorman, Labouvie et al., 1998;
Gorman, Speer et al., 1998), for example, found no association with assault or domestic violence
in New Jersey. Block and Block (1995) also found no relationship in Chicago, leading them to
conclude that “[t]he high levels of crime at these locations reflect the crime levels of the
surrounding neighborhood more than they reflect a concentration of tavern and liquor store
crime” (p. 174). In a follow up to the New Jersey studies, however, researchers focused on a
single city (Newark) and did find the expected association (Speer et al., 1998). Several other
studies since that time have shown an association between outlet density and violence. For
example, using block group data from Washington, D.C., Roman et al. (2008) found that onpremise outlets were significantly associated with assaults. Although they did not find a
relationship between off-premise outlets and violence, they did find an association between
disorderly conduct and both types of outlets. An examination of census tract data in Austin and
San Antonio by Zhu et al. (2004) found an association between total outlet density and a wide
range of violent crimes. An analysis by Britt et al. (2005) of Minneapolis neighborhoods also
revealed an association between total outlet density and several violent crimes. In a longitudinal
study employing postal codes in Melbourne, Australia, and estimating fixed effects models,
8
Livingston (2008) found an association between changes in outlet density and changes in assault
levels. Finally, in one of the very few recent analyses appearing in a criminology journal, Nielsen
and Martinez (2003) found an association between total outlet rate and aggravated assault using
census tracts in Miami.
Summary of hypotheses
In our study, we tested the following hypotheses.
H1
The density of all alcohol outlets and of each type of outlet (i.e., off-premise, bar,
restaurant) will be positively associated with the density of total assaults.
H2
The positive effect of outlet density on assault density will be stronger for offpremise outlets relative to bars and restaurants.
H3
The positive effect of outlet density for each type of outlet will be stronger for
simple assaults relative to aggravated assaults.
Data and Method
Study area and data
Cincinnati, Ohio, was the study area for this analysis. Cincinnati has a population of
about 332,000 people. Its violent crime rate of 1,079 per 100,000 residents in 2007 (Federal
Bureau of Investigation, 2007) was similar to that of other large cities across the nation,
including Pittsburgh (1,107), Sacramento (1,113), Dallas (1,069), Tampa (1,060) and Las Vegas
(1,017). In addition to having a representative violent crime rate for a major U.S. city, Cincinnati
is an appealing study area for several other reasons. First, the city is demographically diverse.
Although it lacks a large Asian or Hispanic population, there is a fairly even split between the
black and white populations, which comprise 47% and 52% respectively of the city’s total
population. Second, Census estimates for 2005 indicate that Cincinnati is also socioeconomically
9
diverse. For example, although the median household income is $29,493, 27.6% of Cincinnati
households earn more than $50,000, yet 43.1% earn less than $35,000, and approximately 22%
of Cincinnati’s residents are below the 2005 poverty line (Health and Human Services, 2005).
Third, detailed crime data are available directly from the Cincinnati Police Department. Finally,
the authors are intimately familiar with the study area, having done extensive fieldwork
throughout the city.
Unit of analysis. The unit of analysis in this study was the block group. Block groups are
subdivisions of census tracts and represent the most disaggregate unit available for analysis from
the Census Bureau that still includes socioeconomic information such as household income,
unemployment, and poverty. Superior in resolution to tracts or ZIP codes (Grubesic & Matisziw,
2006; Matisziw, Grubesic, & Wei, 2008), both of which have been used in prior studies of outlet
density and violence (Gruenewald et al., 2006; Livingston, 2008; Zhu et al., 2004), block groups
provide a more detailed local snapshot of demographic and socioeconomic structure and are
often used for other types of spatial analyses. In addition, there is less potential for aggregation
bias when using smaller spatial units like block groups, an aspect that is particularly appealing
when examining the nature of the effects of outlet density and alcohol availability (Parker &
Wolz, 1979) and when conducting spatial statistical analysis (e.g., see Wooldredge, 2002, using
Cincinnati data). Nationally, block groups average about 40 census blocks and contain between
600 and 3000 people. There are 302 block groups in our analysis, representing a complete
enumeration for Cincinnati. The mean population is 1,038.
Dependent variables. The dependent variables in this study were simple assault density
and serious assault density per square mile. (See the next section for a discussion of the
advantages of using a measure of area density relative to rates within block groups.) The
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Cincinnati Police Department provided us with data on all 19,402 crimes reported to the police
between January and June of 2008. Each record included the Uniform Crime Report (UCR)
code, time and date of the offense, address, and description of the location (e.g., street, single
family house, etc.). From these data, we selected simple assaults (UCR 810; n = 2,298) and
serious assaults (UCR 400, 401; n = 479) for further analysis.
Assault data were geocoded using the Centrus geocoding engine from Group 1 software
(Group 1, 2008). While the limitations associated with geocoding crime data are well known
(Ratcliffe, 2001; Zandbergen, 2008), care was taken to ensure that the assigned coordinates were
accurate. In addition, only those events assigned a street-level match were utilized for analysis.
Again, this represents an improvement over previous studies of alcohol outlets and assaults that
were forced to utilize pre-aggregated data for census tracts (Zhu et al., 2004) or data geocoded to
ZIP codes (Gruenewald et al., 2006). The assault density in our sample of block groups ranged
from 0 (n = 23) to 867 per square mile, with a mean of 69. Figure 1a provides a map of
Cincinnati block groups with the location of all assaults, and Figure 1b provides a map with
block groups shaded by assault density. Table 1 provides descriptive statistics for all dependent,
independent, and control variables employed in the study.
[Figures 1a and 1b about here]
[Table 1 about here]
Independent variables. The main independent variable in this study was alcohol outlet
density. We disaggregated this variable into all outlets, outlets that may only sell beer and wine
for carryout, outlets that sell some combination of beer, wine, and spirits for on-premise
consumption, and outlets that sell beer and wine for on-premise consumption. We label these offpremise, bars, and restaurants, respectively. In our analyses, carryout and liquor stores consisted
11
of outlets with C1, C2, and C2X liquor licenses, restaurants were outlets with D1, D2, and D2X
licenses, and bars were outlets with D3, D5, and D6 licenses.1
Alcohol outlet data were retrieved from the Ohio Division of Liquor Control for
Hamilton County, Ohio (ODLC, 2008). Records were sorted by city, and all outlets with a
Cincinnati address were entered into our database. These data include liquor permit types that
allowed us to disaggregate by outlet type. Appendix A provides a list of different types of liquor
licenses and the number of each in Cincinnati. In total, there were 683 unique outlets in
Cincinnati during the summer of 2008, and it is not uncommon for a single outlet to operate
under more than one permit (e.g., C1 and C2). Each of these alcohol outlets were geocoded using
the same process as crime events described above and aggregated to block groups. The alcohol
outlet density in our sample of block groups ranged from 0 (n = 86) to 545 per square mile, with
a mean of 20. Figure 2 provides a map of Cincinnati block groups with the location of the
different types of alcohol outlets, with block groups shaded by overall outlet density.
[Figure 2 about here]
It is important to note that we utilize a true measure of spatial density for alcohol outlets
and assault, versus the more traditional and commonly used per capita based measurements. That
is, the number of outlets and assaults are standardized by the total area of the block groups (i.e.,
square miles), rather than the number of people living in the unit. We suspect that prior use of
per capita measures in some studies may be the source of some of the inconsistent findings to
date. There are two compelling reasons, both with theoretical and methodological underpinnings,
why this is a stronger framework for spatial-analytical studies of outlet density and violence.
Each rests on the fact that when using density measures based on per capita rates one assumes
12
the distributions of outlets and assaults are functions only of the number of residents in the study
area.
The first problem is related to the independent variable and is an issue of spatial
exposure. For example, consider Figure 3, which displays two different block groups of equal
population (1,000) but dramatically different sizes. Unit A is 0.10 square miles and Unit B is 2.5
square miles. Each unit contains 12 alcohol outlets. When using the per capita measurement
adjusted to reflect the density of outlets per 100 residents, each unit receives a score of 1.2.
While this captures a basic measure of population exposure to outlets, it does not reflect the
complexities of spatial proximity to outlets for unit residents. In contrast, when using a true
measure of spatial density, the scores for each unit are dramatically different, with a value of 120
outlets per square mile for Unit A and 4.8 for Unit B. We note that in this context it is important
not to interpret these differences in spatial density necessarily as higher levels of accessibility to
alcohol. This is because accessibility is also highly dependent on local street networks,
population and household distributions within the unit, and the actual location of each outlet with
respect to street networks and households. Instead, it can be argued that the spatial density is a
rough estimate of residential proximity to alcohol outlets. In other words, by utilizing spatial
density we are placing more emphasis on the proximity of people to dense distributions of
alcohol outlets as the primary factor for generating alcohol-related violence.
[Figure 3 about here]
The second problem with using rate-based measures of density is related to the dependent
variable and stems from the construction of the numerator and denominator. With a traditional
rate, the denominator would be based upon the population living in a particular block group. As a
result, the rate-based density metric makes an implicit assumption that both the victims and
13
offenders of all reported assaults are part of the local block group population (Gibbs & Erickson,
1976). This is not a reasonable assumption, nor is it reasonable that all block groups contribute
relatively equally to the victims and offenders in nearby block groups. The numerator in a
traditional rate-based measure partially consists of people not included in the denominator. This
is especially true of bars located in entertainment districts. In short, urban residents become
offenders and victims of assault, and patronize liquor stores and bars, in block groups in which
they do not reside, and thus the resident population cannot be equated with the population at risk.
This is no small matter when the mean block group population in our sample is only about 1,000
residents. Our spatial measure of assault density overcomes this problem and provides for more
accurate comparisons of assaults between block groups, and thus a better gauge of any
association between alcohol outlets and assaultive violence.
Control variables. We included several commonly employed structural characteristics of
communities in order to control for any potentially confounding effects on assault. The inclusion
of these controls is critical in our analysis, as some may argue that a high density of alcohol
outlets is simply an indicator of social disorganization within an area. By controlling for these
community characteristics we can better isolate the effects of outlet density on violence. Our
controls included the proportion of the population living below the poverty line, the proportion
of the population aged 16 and older that is unemployed, the proportion of all households in the
block group headed by a female and with a child under the age of 18 years old, the proportion of
the population aged 15-24, and the proportion of the population that is African-American. We
also include a control for population density given the construction of our main independent and
dependent variables. The data on these measures for our sample of Cincinnati block groups were
obtained from the Caliper Corporation (2005) and come from the 2000 census.
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Modeling Framework
Two different modeling approaches, ordinary least squares (not reported) and spatially
lagged regression, were used to examine the effects of alcohol outlets on violence. To facilitate
discussion and highlight the major differences between the statistical modeling approaches,
consider the following notation:

X



z
W1 and W2
is a k x 1vector of parameters
is a n x k matrix of exogenous variables
coefficient of spatially lagged dependent variable y
coefficient in an error term with a spatial autoregressive structure
is a normally distributed error term with a diagonal covariance matrix, 
the diagonal elements of the covariance matrix which are a function of p +1
exogenous variables, including the constant term
n x n row standardized weights matrices
Both the general linear regression model and a spatially lagged regression model can be
derived from the following general expression (Anselin, 1988):
y  W1 y  X  
(1)
where
  W2  
and
 ~ N (0, ),
 ii  hi ( z )
hi  0
In the event of homoskedasticity (   0 ) and no spatial dependence in values of the
dependent variable (   0 ) or in the error term (   0 ), equation (1) simplifies to a standard
linear regression model which may be estimated with ordinary least squares (Anselin, 1988):
y  X  
(2)
15
If spatial autocorrelation is present in values of the dependent variable but the errors in
the variance-covariance matrix are homoskedastic (   0 ) and there is no additional spatial
autocorrelation in the error term (   0 ), equation (1) becomes a spatial lag model:
y  W1 y  X   ,
(3)
where
 ~ N (0, )
The value of  in this model provides an estimate of the amount of spatial dependence in the
dependent variable values.2
Although OLS is widely used for capturing the relationship between outlet density and
violence while controlling for demographic and socioeconomic variables (Gorman, Speer et al.,
1998; Speer et al., 1998), it often fails to account for the influence of spatially related data. As
noted by Loftin (1972; see also Loftin & Ward, 1983), Cliff and Ord (1981), and others,
geographic units are not always independent of one another. In other words, events or processes
in one unit often have an impact on events or processes in neighboring or spatially proximal
units. Therefore, the choice of statistical models for examining the impact of outlet density on
violence should include the ability to model spatial dependence between units (Britt et al., 2005;
Millar and Gruenewald, 1997; Zhu, Gorman, & Horel, 2004). If one fails to account for spatial
dependence, the resulting analysis can yield false positives (e.g., suggesting outlet densities are
related to violence when in fact they are not) or an over/underestimation of the effect size of
other explanatory factors (e.g., local demographic or socioeconomic characteristics).
Results
Basic descriptive statistics for block groups and their associated levels of assault and
outlet densities in Cincinnati are reported in Table 1. Also reported are the demographic and
16
socioeconomic covariates utilized for regression analysis. Table 2 provides the results of two
global, spatial regression models, both of which use a queen’s contiguity spatial weight matrix
for analysis. The first model explores the relationship between all simple assaults, alcohol outlet
density and the control variables (Model 1), while the second provides an identical analysis for
all aggravated assaults (Model 2). The R-squared value of 0.75 suggests that Model 1 explains
about 75% of the variation in simple assaults by block group for Cincinnati. In each case, alcohol
outlet density is positively and significantly associated with assault density in this model, as are
population density, and poverty. From a spatial perspective, it is also important to note that the
spatial lag (Rho) associated with assault density is a positive and significant contributor to the
model. Simply put, rho measures the average influence of neighboring observations on each
individual observation. In this instance, a value of 0.454 confirms the importance of spatial
proximity for capturing the relationships between assaultive violence and outlet density. While
the overall explanatory power for Model 2 is somewhat reduced in comparison (R-squared =
0.52), the inferences drawn for outlet density, poverty and population density remain the same.
Given the results of these global models and the statistically significant relationship
between assaults and alcohol outlets, Models 3-8 are estimated to provide a more disaggregate
and nuanced analysis of the relationship between simple and aggravated assaults and alcohol
outlet types.
Table 3 displays the results when the spatial density of simple assault is regressed on offpremise outlets (Model 3), restaurants (Model 4), and bars (Model 5). Similar to the global
models, the density of each type of outlet is positively and significantly associated with simple
assault density, as are population density, poverty, percent of population aged between 15 and 24
years and the spatial lag variable. From an interpretive standpoint, Model 3 suggests that an
17
increase in one off-premise outlet per square mile is associated with 2.3 more simple assaults.
Similarly, Models 4 and 5 suggest similar effect sizes for restaurants (+ 1.15) and bars (+1.35) on
simple assault density. In all three cases, approximately 70% of the variance in simple assault
density is explained by alcohol outlets and the control variables.
Table 4 shows the results when aggravated assault density is regressed on the three
different types of outlets. The density of each type of outlet is positively and significantly
associated with aggravated assault density, as are population density, poverty, percent of
population aged between 15 and 24 years, and the spatial lag variable. The overall explanatory
power of the models for aggravated assault are somewhat diminished when compared to simple
assault, only explaining about 52% of the variance. However, it is important to note that once
again, off-premise establishments that are selling some combination of beer and wine for
carryout (C1, C2 and C2X) have the most notable impact on increasing aggravated assault.
Specifically, an increase in one off-premise outlet per square mile is associated with 0.6 more
aggravated assaults per square mile.
In an effort to disentangle the relationships between outlet types and assault density we
fitted a second suite of spatial regression models controlling for multiple outlet types. In part,
this helps address a portion of our second hypothesis. For example, while we expected to see a
positive effect of outlet density on assault density, we hypothesized that relationship would be
stronger for off-premise outlets relative to bars and restaurants. Table 5 displays an abbreviated
summary of coefficient sign and significance for the regression models examining aggravated
assault when more than one outlet type was controlled for. Indeed, these results seem to suggest
that off-premise carryout alcohol outlets are more strongly associated with aggravated assaults
18
than either bars or restaurants. Further, when only restaurants and bars are included in the same
model, these outlet types have no statistically significant impact on assault density.
Finally, our third hypothesis stated our expectation that the positive effect of outlet
density for each type of outlet will be stronger for simple assaults relative to aggravated assaults.
We can test this hypothesis for each type of outlet by testing for the equality of regression
coefficients between models (see p. 1276 in Clogg, Petcova, & Haritou, 1995; see also
Paternoster et al., 1998). This straightforward comparison is tested via a z-test for the difference
between slopes. The equation is as follows:
Obtaining the corresponding slope coefficients and standard errors for each type of outlet
for simple and aggravated assaults, we find that z is significant in all cases, meaning we reject
the null hypothesis of equality. In other words, the effect of the density of each type of outlet –
off-premise, bars, and restaurants – is stronger for simple assaults than for aggravated assaults.
Discussion
In both criminology and other disciplines, research on the alcohol-violence association
has focused on individuals and, to a lesser extent, the criminal event. In recent years, however,
increased attention has been given to ecological models that have examined the influence of
alcohol availability and alcohol outlets on area crime rates. With a few exceptions (see Block &
Block, 1995; Nielsen & Martinez, 2003; Parker, 1995; Roncek & Maier, 1991), though, this
growing ecological literature has appeared in the journals of other disciplines and been largely
ignored by macro criminologists. Now that more general ecological models again have a strong
foothold in criminology, we believe it is important to look at specific characteristics of
19
neighborhoods and communities that go beyond general levels of social organization, community
cohesion, and collective efficacy. This should include alcohol outlets and their location and
density.
While there are conflicting findings about the association between alcohol outlet density
and neighborhood levels of interpersonal violence, the empirical literature is beginning to
converge. On one hand, there are studies that reveal no association between alcohol outlets and
crime. For example, Gorman and colleagues (Gorman, Labouvie et al., 1998; Gorman, Speer et
al., 1998) found no association with assault or domestic violence in New Jersey municipalities,
nor did Block and Block (1995) find the expected association with crime in sections of Chicago.
On the other hand, a growing number of increasingly sophisticated analyses do find an
association between alcohol outlet density and violence (Alaniz et al., 1998; Britt et al., 2005;
Gorman et al., 2001; Gruenewald et al., 2006; Gruenewald & Remer, 2006; Livingston, 2008;
Nielsen & Martinez, 2003; Parker, 1995; Parker et al., 2007; Reid et al., 2003; Scribner et al.,
1999, 1995; Speer et al., 1998; Zhu et al., 2004). Improving upon several limitations of some of
these prior analyses, including controlling for spatial autocorrelation and using a more spatially
appropriate measure of outlet and assault density, our results reveal a positive and significant
association between the density of all outlets and the density of all assaults in our sample of
Cincinnati block groups. This association holds for each type of outlet (i.e., off-premise, bar,
restaurant) individually, and these findings are consistent with the expectations of our first
hypothesis. We believe it important to stress that this finding does not say alcohol outlets are
problematic per se, but that their concentration in an area is associated with higher levels of both
simple and aggravated assault.
20
The results also provide supporting evidence for our second hypothesis. That is, the
strength of the outlet-assault association varies by type of outlet. Specifically, the density of offpremise outlets is more strongly related to both simple and aggravated assaults than the density
of bars and restaurants. This finding is not dissimilar from the findings of Gruenewald et al.
(2006), who found a stronger association between off-premise outlet density and serious assault
(as measured by hospital data), though in their study they found no association with assault for
bars or restaurants. While bars especially provide a place for people to congregate and drink,
they also provide a certain level of informal social control via other patrons and semi-formal
control by bar bouncers and staff (Graham et al., 2005), which can serve to limit the amount and
escalation of violent encounters. This could very well provide the reasoning behind our most
tenuous finding. That is, while our results suggest a positive association between bar density and
aggravated assault, the p-value does not allow for strong conclusions. The greater informal and
semi-formal control provided in these settings could be at work here, especially since there is a
clear association between bar density and simple assault. Off-premise outlets, on the other hand,
not only often provide a place for locals to socialize inside or outside the store, but largely lack
informal and formal social control, especially since most of these outlets are convenience stores
or gas stations. This can result in the greater likelihood of a violent event taking place and more
easily allows for the escalation of violence once it begins. If the outlet is, in fact, a place for
community members to socialize, it may also serve as the place where ongoing disputes and
grievances are resolved, regardless of any role of alcohol.
The findings from our analyses also support the third hypothesis. That is, the strength of
the outlet-violence association is stronger for simple compared to aggravated assaults. This
finding holds for all three types of outlets. This may have as much to do with the nature of
21
aggravated assaults as it does the alcohol-violence association. That is, simple assault is a broad
category that contains a wide variety of (usually) relatively low level assaultive behavior. The
risk of many such behaviors likely increases with the consumption of alcohol by one or more of
the people involved in the event. On the other hand, while there is evidence of a dose-response
relationship between alcohol consumption and violence (Corrao et al., 1999; Macdonald et al.,
2005; Pridemore & Eckhardt, 2008; Wells & Graham, 2003; Wells et al., 2000), the different
reasons for and relative rarity of serious violence may mean that these events, on average, may
not require alcohol to prime them but instead may more often result from other causes.
Although we are unable to test the ideas directly, the results presented here are consistent
with the spatio-temporal notions that some alcohol outlets may be perceived as “deviant places”
where patrons expect a “time out” from the normal rules governing interpersonal behavior,
including violence. From crowded dance clubs filled with young people in an upscale
entertainment district to a convenience store parking lot in a rundown area littered with trash,
beer cans, and broken bottles, these are settings where the limits on behavior can be ambiguous
(Nielsen & Martinez, 2003). In such situations, intoxication may be more likely to selectively
disinhibit people’s choices (Parker & Rebhun, 1995).
Of course, one may return to the argument that outlet density is confounded with other
neighborhood characteristics that increase violence (Block & Block, 1995). Our analysis and
those of others, however, controlled for several structural characteristics commonly found to be
associated with higher levels of violence. One may also argue that there is nothing special about
alcohol outlets, as they are simply like any other retail outlet that might provide food, clothing,
or some service and thus provide place where a greater concentration of motivated offenders and
potential victims come into contact. Yet recent work by Gruenewald et al. (2006) controlled for
22
the density of non-alcohol retail, food, and service establishments and found no association with
assault, though the association between outlet density and serious violence remained.
A final consideration is the size of the effect. It might be argued that the magnitude of the
effect of alcohol outlets on assault in our study, while statistically significant, is small. An
increase of one off-premise outlet per square mile, for example, is associated with 2.3 more
simple assaults and 0.6 more aggravated assaults per square. However, as seen in Table 1, the
mean density of off-premise outlets in these block groups is only 8 per square mile and the mean
number of simple and aggravated assaults is 69 and 15 per square mile, respectively. To get a
more precise estimate of the role of outlets in violence, one may estimate an attributable fraction
(AF). In our case, the AF would be the expected percent decrease in assaults if all alcohol outlets
were removed or, in other words, the fraction of assaults attributable to alcohol outlets. While
commonly estimated in ecological and community-level studies in epidemiology, AFs are rarely
if ever computed by macro criminologists. This is probably due to the fact that the traditional
method of deriving an AF is dependent upon information about relative risk (RR) computed from
individual-level data. However, deriving AFs from aggregate ecological data is a straightforward
exercise due to the relationships between the relative risk, attributable fraction, and regression
coefficient. For details on both theory and method, the reader is urged to consult Norström
(1988a, 1988b). In short, the macro AF is computed as
where b is the effect parameter, X is the risk factor (in our case, alcohol outlet density), and Y is
the outcome (assault density). Using the effect sizes from Tables 3 and 4 and the means for outlet
and assault density in Table 1 for simple and aggravated assaults, respectively, the AFs for offpremise outlets are 26% and 30%. In other words, we could expect a reduction of about one23
quarter in simple assaults and nearly one-third in aggravated assaults in our sample of Cincinnati
block groups were alcohol outlets removed entirely. For simple assaults, the corresponding
attributable fractions are 11.9% and 10.2 % for bars and restaurants, respectively. For serious
assaults, the AFs are 4.3% and 8.1% for bars and restaurants, respectively. The AFs for offpremise outlets especially represent substantial reductions and clearly reveal the impact of
alcohol outlet density on assault density in our sample.3 More generally, while the AF is an
extrapolation from the data and must be interpreted with some caution, computing and reporting
attributable fractions aids in (1) the accumulation of evidence via the comparison of results from
different studies and (2) assessing the plausibility of an estimated macro-level effect.4
Limitations of current study
Our study contains limitations that must be considered when interpreting our results. Two
main limitations are associated with the dependent variable. First, we have no way of knowing
which assaults included in our measure were alcohol-related (see Livingston, 2008) or occurred
at or near an alcohol outlet (see Block & Block, 1995). Information on these details are
sometimes recorded as a part of the police record of an event, but the absence of such
information does not necessarily mean the event was not alcohol- and/or outlet-associated.
Second, our assault data depends on police records and thus the traditional limitations associated
with them. Nevertheless, if reporting and recording procedures are similar across the units in our
analysis, which is likely to be the case for serious violent crimes, then this measurement issue
becomes less troublesome. In fact, using National Crime Victimization Survey data, Baumer
(2002) has shown that neighborhood disadvantage is not significantly associated with reporting
aggravated assault. Baumer did find an association between neighborhood disadvantage and
reporting simple assault, however, which must be kept in mind when interpreting our results.
24
There may also be limitations associated with the set of independent variables included in
our models. Most importantly, we were unable to control for actual alcohol sales or alcohol
consumption within block groups.5 Similarly, we did not control for other area characteristics
that might be associated with heightened levels of violence, such as major intersections, public
transportation nodes, illegal drug trade, and nighttime business centers (Block & Block, 1995;
Gruenewald et al., 2006; Roncek & Maier, 1991). Of course, our study shares these limitations
with prior studies of alcohol outlets and crime.
In spite of these limitations, we have taken care to improve upon several aspects of prior
analyses of alcohol outlet density and violence. Further, it is our goal to encourage more research
by criminologists on the nature of these limitations, the alcohol outlet-violence association more
generally, and several substantive aspects of the association that we outline below.
General policy implications
Elements of ecological theories like collective efficacy, social disorganization, and social
cohesion have consistently been found to be associated with area rates of homicide. However,
most elements of these theories – such as poverty, ethnic heterogeneity, residential mobility,
anonymity of community members, willingness to intervene on others’ behalf, etc. – are
notoriously difficult to remedy via policy or other social mechanisms. Alcohol outlet density, on
the other hand, is much more amenable to policy changes. For example, the concentration of
alcohol outlets can be reduced by generally limiting liquor permits, setting thresholds on their
density, requiring new outlets to be outside some minimum distance from existing outlets,
refusing to issue a new license when a former alcohol outlet has gone out of business (especially
if the area already possesses problematic outlets or a high density of outlets), and limiting outlets
in high risk areas such as socially disorganized communities and neighborhoods with a high
25
concentration of college students. Liquor licensing boards can threaten (and follow through on)
closures for outlets that have proven to be a public nuisance via repeatedly violating liquor laws
or being a hot spot for crime. These agencies can also regulate opening and closing hours, and
local police can increase patrols immediately before, during, and immediately after closing hours
in areas with high concentrations of bars.
Unlike other negative neighborhood characteristics that often seem intractable, regulating
the density of outlets, and to some extent their management, can be readily addressed with a
mixture of policies by liquor licensing boards, the police, and government agencies that regulate
land use. These and other alcohol policies can promote responsible drinking and improve the
quality of life in communities by limiting deviant places and reducing violence and other
alcohol-related problems within neighborhoods. By doing so, such actions could even help
promote local levels of social organization, social cohesion, and collective efficacy.
Avenues for future research by criminologists on the outlet-violence association
Wolfgang’s (1958) discussion of the role of alcohol in violence brought considerable
attention to the topic. In recent years, however, the alcohol-violence association has not been a
consistent theme in the criminological literature, and the research that has appeared has focused
mostly on the individual-level association (e.g., Felson et al., 2008). Yet our results, together
with those of other criminologists (Nielsen & Martinez, 2003; Parker et al., 2007) and those from
public health and epidemiology (e.g., Gorman et al., 1998; Gruenewald et al., 2006; Livingston,
2008) suggest a prominent role for alcohol in the study of the social ecology of crime.
First, we must answer in relation to alcohol outlets a more general question posed by
Sherman et al. (1989, p. 46) about hot spots of criminality. That is, do alcohol outlets “vary in
their capacity to help cause crime, or merely in their frequency of hosting crime that was going
26
to occur some place inevitably, regardless of the specific place?” Block and Block (1995)
suggested that alcohol outlets played a minimal role in crime and violence, and that areas of high
outlet density are areas of more general social disorganization, and thus higher crime rates in
these areas only reflects the higher crime rate expected of disorganized areas. Is this true, or
might outlets cause crime and thus result in a higher rate than would exist were they not there? If
it is the latter, then one avenue of further research is to go beyond current analyses of outlet
density that treat all outlets the same. Even given similar outlet type (e.g., off-premise, bar),
outlets vary on a number of characteristics. Do hours of opening and closing play a role in
violence? What about the amount of alcohol sales or the number of patrons? Some off-premise
outlets are liquor stores that are devoted to liquor sales, while others are smaller convenience
stores where alcohol is just another product on the shelves and accounts for a small proportion of
sales. Does this matter? In short, we need to know more about the specific characteristics of
alcohol outlets that might contribute to their association with violent behavior.
Second, we agree with Graham (2006, p. 620) that “[w]e need a better understanding of
what actually occurs in and around off-premise alcohol outlets that might account for the
relationship with violence.” In other words, in addition to some of the aspects of outlets just
mentioned, there are another set of characteristics of outlets that are even more likely to be
important in any association with violence. Who are the clientele of these outlets? Is it a
convenience store or a more traditional liquor store? Is it a place where people come to buy
liquor and leave, or does it serve as a social gathering spot? Is it located on a major road or
intersection, or perhaps in a strip mall, or in a storefront on an urban block? How are the
premises and local environment kept up physically, both inside and out? Is it clean, well lit, and
well-cared for, or run down and dirty, with trash or empty bottles and cans around the building or
27
in the gutter? Outlets will vary on these physical, environmental, and situational characteristics,
which might play a role in any association with violence. Answering these questions goes
beyond merely examining density, but requires more systematic social observation of public
spaces (Sampson & Raudenbush, 1999) inside and out, including the type (though obviously
adapted to fit off-premise outlets) of detailed and careful observation of the bar milieu carried
out by Graham and colleagues (e.g., Graham, Bernards et al., 2006; Graham, Osgood et al.,
2006; Graham, Tremblay et al., 2006; Graham et al., 2005; see also Homel & Clark, 1994).
Third, some research on alcohol outlets and crime should employ longitudinal analysis.
While cross-sectional studies such as ours are informative, it is important to understand how
changes in outlet density influence changes in local rates of violence. This can be done via the
use of change scores for both or, if data are available, by employing a panel design (see
Gruenewald & Remer, 2006; Livingston, 2008). Similarly, criminologists can employ interrupted
time series analysis to take advantage of natural experiments. If a new outlet opens in a
neighborhood, does this increase violence? Does violence decrease if an outlet closes? What
happens when a new alcohol policy is implemented that alters the conditions of alcohol sales
(Pridemore & Snowden, 2009)?
Fourth, it is vital that we better understand the role of neighborhood characteristics in the
outlet-violence association. It is clear from the literature that many scholars assume that a greater
concentration of outlets just reflects greater neighborhood disorganization. Is the relationship
really this simple? For example, does a high concentration of bars and clubs in an entertainment
district result from disorganization or organization? Are all alcohol outlets unwanted and viewed
with derision by a majority of the community? It is likely that these associations are more
complex, and equally likely that the effects of outlet density on violence vary by place and by
28
type of neighborhood. It could be, for example, that alcohol outlets partially mediate the
association between disorganization and violence. Interaction effects may also play a role
(Gruenewald et al., 2006; Peterson, Krivo, & Harris, 2000; Zhu et al., 2004). Might high levels
of organization or collective efficacy buffer the effect of high outlet density on violence?
Similarly, disorganized communities may be more susceptible to the negative effects of alcohol
outlets, even if the latter are not highly concentrated in the area. Interaction effects with land use
are also important to examine. Is the outlet-violence association moderated by the prevailing land
use in the area? Or by being located near vacant parcels, public transportation nodes, or a
manufacturing or warehouse district? It is difficult to imagine that the spatial effects of alcohol
outlets on violence do not vary by location, requiring that we examine more closely any
contextual effects represented by neighborhood and land use characteristics.
Finally, there are important methodological questions to consider when examining the
effects of alcohol outlets on violence from a spatial perspective. For example, if one is interested
in exploring the role of land use in the outlet-violence association, how is spatial proximity best
captured? Murray and Roncek (2008) utilize both a queen’s contiguity measure and a ‘radial’
distance (i.e., Euclidean) buffer of 500 feet for capturing adjacency. The radial Euclidean
distance measure is a relatively poor surrogate for spatial proximity because both offenders and
victims traverse urban environments along a street network. As a result, when using Euclidean
distance to capture spatial proximity, one can potentially overestimate these proximity
relationships, including that of land use and outlet-violence association.
Conclusion
There has been a resurgence during the last two decades in the theoretical and empirical
literature on the association between social structure and community crime rates. This work,
29
however, has focused largely on social disorganization theory and its variants. While these
theories have received considerable support, the criminological literature on social ecology and
crime rates has paid surprisingly little attention to other community characteristics that may
influence rates of violence.
One characteristic that has been the topic of recent research in the epidemiology, public
health, and geography literatures, however, is alcohol outlet density within a community and its
effect on violence rates. The major goal of this paper was to bring this idea to the criminology
literature. In doing so, we employ a more spatially appropriate measure of community alcohol
outlet density, use smaller units of analysis that allow for greater resolution of community
characteristics, include important control variables, control for spatial autocorrelation, examine
the effects of different types of alcohol outlets to determine their differential effects on
community violence, and compute and report attributable fractions, or the fraction of assault
density due to outlet density. Using Cincinnati block groups as our unit of analysis, and simple
and aggravated assaults as our dependent variables, we found a general pattern of association
between outlet density and violence. These associations held after controlling for important
demographic and socioeconomic characteristics of these communities and for spatial
autocorrelation. We also found that while the associations held for both simple and aggravated
assaults, the association with simple assaults was stronger. Finally, for both simple and
aggravated assaults we found that the strength of the association with violence was significantly
greater for off-premise outlets than for bars and for restaurants.
In short, the density of alcohol outlets appears to play an important role in the variation of
community rates of violence, and the strength of this association varies by outlet type. While
alcohol consumption may have individual-level effects on violent offending and victimization,
30
our results provide evidence of ecological effects. We suggest these effects are due both to the
spatial proximity of community residents to alcohol itself, and thus an intoxicating substance,
and to deviant places. Both bars and off-premise outlets serve as social gathering places with
access to alcohol, and the latter especially provide little or no social control. These results
provide clear policy implications and reveal the promise of the application of GIS and spatial
analysis to the study of alcohol and violence. The nature of our findings also should encourage
further investigation of the nature of the ecological association between alcohol, violence, and
other negative outcomes within communities.
31
Endnotes
1. In Ohio, liquor and grocery stores that sell spirits do not have a Number-Letter type license
like other alcohol outlets. Instead, the Ohio Division of Liquor Control refers to these as “liquor
agencies,” and these agencies must go through a different application process. There are only 27
of these in Hamilton County and 14 in Cincinnati proper. For the purposes of our analysis, we
included these in the “off-premise” category. As part of our sensitivity analysis, however, we
removed this group and reestimated the effects of off-premise outlets on total and felonious
assaults. While the effect size was slightly changed, the inferences remained the same.
2. A third option, the spatial error model, is also available but not implemented for this study.
3. Whereas selection bias is a serious concern when computing relative risk, the analog for the
macro attributable fraction is omitted variable bias.
4. In undertaking sensitivity analysis on one’s own work, for example, it would be wise to
compute an AF to ensure the parameter is not implausibly large (or small). Similarly, one can
usually compute an AF from the information reported in published articles to do the same thing.
See Norström (1988b, p. 340) for a clear example of the latter.
5. We note, however, that a prior study (Stevenson et al., 1999) found that a significant
association between off-premise outlets and violence remained after controlling for alcohol sales.
32
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38
Table 1. Descriptive statistics for Cincinnati block groups (n = 302)
Minimum
Maximum
Mean
Standard
Deviation
Total Assault Density
Serious Assault Density
0.00
0.00
866.66
333.33
69.28
14.99
121.79
35.21
Total Outlet Density
Restaurant Density (D1, D2, D2X)
Bar Density (D3, D5, D6)
Carryout and Liquor Store Density (C1, C2,
C2X)
0.00
0.00
0.00
545.45
166.66
272.72
20.20
6.12
6.06
48.30
18.00
23.78
0.00
105.26
7.79
14.63
Population Density
Percent Black Population
Percent Population Aged 15-24
Percent Population Below Poverty Line
Percent 16+ Unemployed
Percent Female Headed Households
0.00
0.09
1.08
0.00
0.00
0.00
30394.73
98.78
98.52
81.48
58.60
69.86
6600.68
42.72
16.23
21.48
8.82
19.91
4352.82
33.90
11.17
17.35
9.36
11.72
39
Table 2. Spatial lag models for all alcohol outlets and assault density
Model 1: Simple assaults
Model 2: Aggravated assaults
Coefficient
t-value
P-value
Coefficient
t-value
P-value
-50.987
-5.569
0.000
-12.222
-3.336
0.000
Rho – Simple Assault
0.454
9.430
0.000
-----
-----
-----
Rho – Aggravated Assault
-----
-----
-----
0.480
8.202
0.000
Total Outlet Density
0.844
9.659
0.000
0.114
3.390
0.000
Population Density
0.005
5.990
0.000
0.001
3.811
0.000
Poverty
1.680
4.739
0.000
0.395
2.831
0.004
Unemployment
0.653
1.168
0.242
0.337
1.527
0.126
% Black
0.021
0.133
0.893
-0.027
-0.402
0.673
% 15-24
-0.548
-1.659
0.097
-0.252
-1.909
0.056
Female-Headed Households
0.067
0.127
0.898
0.122
0.591
0.553
Constant
Adjusted R-Squared
0.751
0.526
Rho
0.454
0.480
Standard Error
60.55
24.21
Breusch-Pagan
473.08 (p = 0.00)
589.87 (p = 0.00)
40
41
Table 3. Spatial lag models for simple assault by outlet type
Model 3: Off-premise outlets
Model 4: Restaurants
Model 5: Bars
b
t-value
P-value
b
t-value
P-value
b
t-value
P-value
-35.730
-3.742
0.000
-42.842
-4.222
0.000
-46.324
-4.893
0.000
Rho - Assault Density
0.435
8.547
0.000
0.511
10.313
0.000
0.477
9.846
0.000
Off-Premise Outlet Density
2.341
7.429
0.000
-----
-----
-----
-----
-----
-----
Restaurant Density
-----
-----
-----
1.152
4.730
0.000
-----
-----
-----
Bar Density
-----
-----
-----
-----
-----
-----
1.357
8.012
0.000
Population Density
0.005
4.961
0.000
0.006
6.400
0.000
0.007
7.356
0.000
Poverty
2.215
5.912
0.000
2.200
5.710
0.000
1.895
5.180
0.000
Unemployment
-0.129
-0.216
0.828
0.425
0.695
0.486
0.988
1.692
0.090
% Black
0.142
0.827
0.408
0.067
0.376
0.706
-0.010
-0.060
0.952
% 15-24
-0.655
-1.858
0.063
-0.857
-2.362
0.018
-0.784
-2.302
0.021
Female-Headed Households
-1.010
-1.871
0.061
-0.719
-1.247
0.212
-0.426
-0.790
0.429
Constant
Adjusted R-Squared
0.719
0.698
0.731
Rho
0.435
0.511
0.477
Standard Error
64.43
66.79
62.960
Breusch-Pagan
472.43 (p = 0.00)
1177.33 (p = 0.00)
475.09 (p = 0.00)
42
Table 4. Spatial lag models for aggravated assault by outlet type
Model 6: Off-premise outlets
Model 7: Restaurants
Model 8: Bars
b
t-value
P-value
b
t-value
P-value
b
t-value
P-value
-10.687
-3.020
0.000
-11.407
-3.081
0.002
-10.729
-2.905
0.003
Rho - Assault Density
0.432
7.170
0.000
0.504
8.736
0.000
0.493
8.458
0.000
Off-Premise Outlet Density
0.581
5.020
0.000
-----
-----
-----
-----
-----
-----
Restaurant Density
-----
-----
-----
0.199
2.261
0.023
-----
-----
-----
Bar Density
-----
-----
-----
-----
-----
-----
0.104
1.623
0.104
Population Density
0.001
2.777
0.005
0.001
4.075
0.000
0.001
4.540
0.000
Poverty
0.430
3.176
0.000
0.458
3.316
0.000
0.468
3.336
0.000
Unemployment
0.167
0.759
0.447
0.313
1.410
0.158
0.354
1.577
0.114
% Black
-0.004
-0.067
0.946
-0.020
-0.319
0.749
-0.024
-0.380
0.703
% 15-24
-0.218
-1.680
0.092
-0.286
-2.165
0.030
-0.302
-2.279
0.022
Female-Headed Households
0.045
0.229
0.818
0.038
0.187
0.851
-0.019
-0.092
0.926
Constant
Adjusted R-Squared
0.539
0.518
0.513
Rho
0.432
0.504
0.493
Standard Error
23.85
24.38
24.52
Breusch-Pagan
486.31 (p = 0.00)
612.60 (p = 0.00)
298.65 (p = 0.00)
43
Table 5. Spatial lag models for aggravated assault and multiple types of outlet density
Sign
Significant
Off-premise density
Restaurant density
+
+
Yes
No
Off-premise density
Bar density
+
+
Yes
No
Restaurant density
Bar density
+
+
No
No
44
45
46
Unit 1
0.10 square miles
Population = 1,000
Outlets = 12
Per-capita Density = (12/1000)*100 = 1.2
Spatial Density = (12/0.10) = 120
Unit 2
2.50 square miles
Population = 1,000
Outlets = 12
Per-capita Density = (12/1000)*100 = 1.2
Spatial Density = (12/2.50) = 4.8
Figure 3: AComparison of Rate-BasedDensity Measures with Spatial Density Measures
47
Appendix A. Number and type of alcohol outlets in Cincinnati.
Permit Type
Description
Count
A1
Manufacturer of beer, ale, stout and other malt liquors
6
A2
Manufacturer of wine
4
A3
Manufacture, import and sell alcohol and spirituous liquor
2
B1
Distributor of beer, ale, stout and other malt liquor
3
B3
Distributor of sacramental wine
2
B5
Distributor and importer and bottler of wine
2
C1
Beer only in original sealed container for carryout
259
C2
Wine and certain prepackaged mixed drinks in sealed
container for carryout
274
C2X
Beer in original sealed container for carryout
27
D1
Beer only for on premises consumption or in sealed containers
for carryout
164
D2
Wine and certain prepackaged mixed drinks in sealed
container for consumption on premises or carryout
175
D2X
Beer for on premise consumption
18
D3
Spirituous liquor for on premises consumption until 1:00am
10
D4
Beer and any intoxicating liquor to members for on premise
consumption
17
D5
Spirituous liquor, beer and wine for on premise consumption
until 2:30am
153
D6
Sale of intoxicating liquor on Sunday from 1pm to midnight
171
MISCa
Other, specialty permits
22
a
Miscellaneous category created by authors.
48