A Spatial Analysis of Alcohol Outlet Density and Abandoned Properties on Violent Crime in Paterson New Jersey

Abstract

Alcohol outlet density (AOD) and abandoned or vacant properties in under-served urban communities has received increased attention and has been linked to community violence. While previous research has examined the AOD and violent crime association, less research has investigated the relationship between abandoned properties and violent crime. Those studies that are present examining the AOD-abandoned properties-violent crime link have been plagued by flaws that include statistical weaknesses and aggregated datasets that investigated larger units such as states or countries. The present study, using Geographic Information Systems (GIS) mapping, spatial analysis techniques, and a regression-based approach examines the association between AOD and abandoned properties on violent crime, controlling for demographic characteristics, in Paterson, New Jersey. Results provide some evidence on the association between AOD and abandoned properties on violent crime, drawing conclusions for policy and practice.

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Journal of Community Health
https://doi.org/10.1007/s10900-019-00772-0
ORIGINAL PAPER
A Spatial Analysis ofAlcohol Outlet Density andAbandoned
Properties onViolent Crime inPaterson New Jersey
DavidT.LardierJr.1,2 · RobertJ.Reid3· DanlinYu4· PaulineGarcia‑Reid3
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Alcohol outlet density (AOD) and abandoned or vacant properties in under-served urban communities has received increased
attention and has been linked to community violence. While previous research has examined the AOD and violent crime
association, less research has investigated the relationship between abandoned properties and violent crime. Those studies
that are present examining the AOD-abandoned properties-violent crime link have been plagued by flaws that include statis-
tical weaknesses and aggregated datasets that investigated larger units such as states or countries. The present study, using
Geographic Information Systems (GIS) mapping, spatial analysis techniques, and a regression-based approach examines
the association between AOD and abandoned properties on violent crime, controlling for demographic characteristics, in
Paterson, New Jersey. Results provide some evidence on the association between AOD and abandoned properties on violent
crime, drawing conclusions for policy and practice.
Keywords Alcohol outlet density· Abandoned or vacant properties· Violent crime· GIS mapping· Spatial filtering
techniques
Introduction
Community-based research and prevention scholarship has
shown a growing interest in examining the impact of the
ecological structure of neighborhoods on community vio-
lence. Traditionally, crime and community violence research
have focused on individual characteristics or environmental
conditions, which include sociodemographic variables (e.g.,
population density, poverty, racial inequality) [1]. Although
sociodemographic variables are often correlated predictors
of crime and violence [2], these are not necessarily active
contributors. Instead, determinants such as AOD [3–6] and
abandoned or vacant properties [7, 8], have been associated
with both poverty and ecological circumstances including
community violence. Specifically, AOD and abandoned
properties in under-served urban communities has received
increased attention with community violence.
Early investigations into the association between AOD,
abandoned properties, and violent crimes in urban com-
munities were plagued by flaws that include statistical
weaknesses and aggregated datasets that investigated
larger units such as states or countries [9]. While some
addressed these concerns by using city-level data [10–12],
more recent studies have used census track data as the
geographic unit of analysis, as well as employed spatial
analysis techniques, which have resulted in more rigorous
findings [6–8, 13]. These investigations have further sup-
ported the relationship between AOD and violent crime
[6, 14], as well as the connection between the density of
abandoned properties in a neighborhood and violent crime
[8, 15, 16]. Few have, however, considered the associa-
tion the density of abandoned or vacant properties have
with the “alcohol-crime” link [2, 17], and even less have
* David T. Lardier Jr.
dlardier@unm.edu; dalardier@salud.unm.edu
* Danlin Yu
yud@montclair.edu
1 Department ofIndividual, Family, andCommunity
Education, The University ofNew Mexico, Albuquerque,
NM87131, USA
2 Department ofPsychiatry andBehavioral Sciences,
University ofNew Mexico School ofMedicine, The
University ofNew Mexico, Albuquerque, NM87131, USA
3 Department ofFamily Science andHuman Development,
Montclair State University, Montclair, NJ07043, USA
4 Department ofEarth andEnvironmental Sciences, Montclair
State University, Montclair, NJ07043, USA

Journal of Community Health
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examined potential model misspecification or residual spa-
tial autocorrelation due to the existence of spatial effects
to examine these associations [9].
Alcohol outlet density (AOD) is defined as “the number
of physical location in which alcoholic beverages are avail-
able for purchase either per area or per population” [4, p.
556]. A high density of alcohol retailers has been associ-
ated with a variety of negative individual health outcomes
such as adolescent substance use [9, 18, 19], negative sub-
stance abuse treatment outcomes [20], as well as harmful
social outcomes including traffic accidents, drinking and
driving, and physical injuries [9]. Studies have also shown
that higher AOD are associated with violent crime [5, 14].
For example, Jennings etal. [5] identified that the addition
of liquor stores in a geographic area significantly increased
the probability of violent crime, overtime. Similarly, Zhang
etal. [14] showed that reductions in AOD between 1997 and
2007 reduced exposure to violent crime among community
members in Atlanta, Georgia.
A sizable corpus of literature has also indicated for over
70years the influence community stability and infrastruc-
ture have on crime and criminality. Shaw and McCay [21]
argued that neighborhood poverty and instability lead to
weak social bonds, resulting in the probability for increased
criminality, violence, and unhealthy behaviors. Recent stud-
ies support that vacant or abandoned properties are likely to
offer a location to those engaged in illegal activity and are
symbolic of community deterioration [22]. Some have even
described vacant or abandoned properties have been identi-
fied as “crime attracters” [16], influencing community well-
being [23] and provoking certain types of crimes including
drug dealing, robberies, and assaults [24]. Therefore, the
structural and built environmental circumstances may assist
in identifying and designing effective strategies to limit com-
munity violence, as well as reduce other negative outcomes
related to health and community well-being.
It is reasonable to conclude that there is a significant asso-
ciation between vacant or abandoned properties and crime,
and similarly AOD and community crime; yet, less under-
stood is the occurrences of violent crimes, when connected
with both AOD and the density of abandoned or vacant
properties from a spatial analysis standpoint. Therefore, the
present study extends previous research by assessing the
association between violent crime, AOD, and the density of
abandoned properties in an under-served urban community,
Paterson, New Jersey, using spatial analysis techniques at the
census tract-level. Given the emphasis on reducing violent
crime, preventing alcohol abuse, and creating safer com-
munities by eliminating abandoned properties that detrimen-
tally impact the well-being of a community, the objective is
to test the validity of the AOD-abandoned properties-crime
link using spatial analyses. For geographically referenced
data, incorporating spatial effects explicitly in the model
may help model the AOD-abandoned properties-crime link
more reliably [25–27].
Community Context
Paterson, New Jersey is the third largest city in New Jer-
sey (8.4 square miles) with a 2010 population of just over
146,000 [28]. As the third largest city, Paterson has a rich
and dynamic history of being one of the first industrial hubs
of industry; however, recent history has been shaped by
white-flight, urban decline, the loss of manufacturing jobs,
racial segregation, and poverty. Racially, the city is 60.7%
Hispanic or Latina/o, 27.8% Black/African American, and
30% White alone, with 37% of the city’s population foreign
born [28]. Nearly one-third (26.6%) of the population is
below 18years of age. Economically, the median household
income in 2017 dollars was $36,106, with 29% of the city’s
population living in poverty [28]. Only 10.6% of the popu-
lation 25years of age and older holds a bachelor’s degree
or higher. Social problems are entrenched in Paterson with
disproportionately high rates of substance abuse and HIV/
AIDS [29], particularly among the youth population between
13 and 18years of age. As of 2013, when data were col-
lected, there were 197 alcohol selling establishments and
6196 crimes committed, with 1554 violent crimes [30]. The
violent crime rate is 10 times higher than the nearest subur-
ban community [30].
Methods
To investigate the association between occurrences of vio-
lent crimes and sociodemographic factors, distribution of
alcohol selling facilities, and abandoned properties in Pat-
erson New Jersey, we collected cross-section data at the cen-
sus block group level from publicly available sources. Our
analysis specifically focused on 107 census block groups
in which demographics were recorded. Based on previous
research [3] examining both AOD and abandoned proper-
ties, three demographic factors were selected: (1) percentage
of African American residents; (2) percentage of Hispanic
residents; and (3) median household income. All measures
are available at census block group level and were obtained
from the 2010 census data. Map1 summarize the data tabu-
larly and spatially.
Violent Crime information, including both aggravated
assaults and armed robbery from 2013, were procured from
the Paterson Police Department (n =1554). These crime
offenses are two of the four indexes (e.g., rape, aggravated
assault, homicide/manslaughter, and armed robbery) of
violent crime in the Federal Bureau of Investigation’s Uni-
form Crime Report (UCR). Assaultive violence locations

Journal of Community Health
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were geocoded then integrated (via spatial join) to the cen-
sus block group. The count of violent crimes for each cen-
sus block group were produced and serve as our outcome
variable.
Abandoned property locations were obtained from Pat-
erson’s Housing Authority (n =769). Similar to assaultive
violence, locations were also geocoded then integrated.
Alcohol selling establishments (N = 197) present in the city
were obtained from the city’s ABC (Alcohol and Bever-
age Control) board, with actual addresses geocoded and
integrated (via spatial join in a GIS) to the census block.
The count information of the alcohol selling establishments
and abandoned properties is used in an attempt to establish
association between the presence of alcohol selling facili-
ties, abandoned properties and violent crimes, controlling
demographic factors.
To investigate the association between the occurrence
of violent crimes, AOD and the presence of abandoned or
vacant properties, a regression-based approach is employed.
This approach is the most frequently used statistical tech-
nique to investigate the relationship between a given obser-
vation and set of factors [31]. Since the dependent variable,
occurrence of violent crimes, is a count variable, our pre-
liminary data analyses suggest a negative binomial specifica-
tion produces good modeling results. In addition, due to data
being collected and organized in census block groups, spatial
effects were included in modeling strategy [25, 32]. Among
the various techniques that address spatial effects, we found
a semiparametric eigenfunction based spatial filtering tech-
nique provided the best results [33, 34].
With the spatial filtering technique, we assume that, as is
consistent with spatial data, the independence assumption
of the regression residual will not hold. Through building a
binary spatial link matrix in which the elements are either 1
or 0, with 1 identifying that two spatial units are neighbors
(i.e., sharing a border) and 0 otherwise, we are able to create
a quantitative spatial structure (C) in which the data is gener-
ated and collected. By centralizing the spatial link matrix C
to MCM, where M= I − 11′/N (I is the identity matrix; 1 is a
vector of 1s; and N is the number of spatial units). The spa-
tial filtering technique essentially decomposes this central-
ized spatial link matrix into N eigenvectors that represent N
potential map patterns that can be produced from the spatial
structure C. These eigenvectors are called spatial filters and
can be used as auxiliary covariates in a regression model to
effectively control for spatial effects (spatial autocorrelation
in the regression residuals) [35]. Demographic factors were
controlled for as covariates.
Since our models are negative binomial specification and
estimated via maximum likelihood approaches, the Akaike
Information Criterion (AIC) is used to compare nested
models. A lower AIC tends to indicate better model-to-
data fit [35]. Specifically, a reduction of three in the AIC
value is deemed an acceptable model improvement [35]. All
Map1 Counts of assaultive violence, alcohol outlets, and abandoned/vacant properties in Paterson, NJ,census block group. AssaultVio assault-
ive violence, Alcohol Outlets alcohol outlets, ABDProperty abandoned property

Journal of Community Health
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calculations were conducted using the SPDEP package in
the R environment. To this end, three models were proposed
to assess how alcohol selling establishment and abandoned
properties are related to the occurrence of violent crime at
census block group level.
Model 1 examined the relationship between occurrences of
violent crimes and sociodemographic factors alone (this is
our base model that does not consider the influence of alco-
hol selling establishments and abandoned properties);
Model 2 added alcohol-selling facilities in each block
group to assess whether locations of alcohol selling outlets
increased occurrences of violent crimes;
Model 3 included both the count of alcohol selling estab-
lishments and abandoned properties to assess whether these
predicted and increased occurrences of violent crimes.
Results
The results of the three models are reported in Table1.
Comparing the Null and Residual deviances of all three
models, we can see that the added variables reduce the
amount of deviance significantly with only marginal reduc-
tion of degrees of freedom. This suggests the variables
indeed explain the variation of violent crimes in Paterson,
NJ. The data fit statistic, AIC score suggests an increas-
ingly better fit from model 1 to model 3, which is expected
as more relevant exploratory variables are added to the
models. Demographic results (model 1) indicated similar
to previous studies [3] that occurrences of violent crimes
tends to concentrate in poorer regions with more ethnic-
racial minority groups. Furthermore, adding alcohol sell-
ing establishments in model 2 as an explanatory variable
increased the explanatory power of the model. The AIC
score drops from 680.39 to 647.2. This finding indicates
that alcohol selling establishments directly influenced
assaultive violence occurrences. Last, adding abandoned
properties count in model 3 was also found to significantly
predict violent occurrences. The AIC scored also dropped
from 647.20 to 643.57. As for the spatial filters, however,
except for the base model (model 1), the added spatial
filters for models 2 and 3 only show marginal significance.
One potential explanation for the marginally significant
spatial filters in models 2 and 3 might be that we included
the highly spatially auto-correlated alcohol selling outlets
and abandoned properties in the model specification. In
the base model, when these two variables are omitted, the
residuals captured the spatial autocorrelation, which leads
to a significant spatial filter.
Coefficient estimates for previously examined variables
stayed consistent and in the appropriate direction. Model 3
findings illustrate that the presence of abandoned proper-
ties is associated with violent crimes in the community.
Controlling for spatial effects in all three models helped
increase the explaining power present (the spatial filter
of all three models are significant/marginally significant).
Map1 illustrates that as assaultive violence rates increase
(designated by darker shading in census block groups),
there is an increase in the total of alcohol outlets as per-
cent of the total and an increase in abandoned proper-
ties. This is illustrated by the bars in the map wherein
greater percent total increase of both alcohol outlets and
abandoned properties indicates an increase of the percent
total increase of assaultive violence in census blocks with
greater assaultive violence, or darker shading of census
blocks. Overall, these findings show a direct link between
violent crime occurrences and sociodemographic factors,
concentration of alcohol selling facilities and abandoned
properties. Adding alcohol selling distribution and aban-
doned properties information improves model perfor-
mance, as evident by the decreasing AICs.
Table 1 Linear regression
models assessing the
relationship between alcohol
outlet density, abandoned
properties, and violent crime in
Paterson, New Jersey, 2013
AIC akaike-information criterion, SE standard error, df degrees of freedom
Model 1 Model 2 Model 3
Β (SE) z-score Β (SE) z-score Β (SE) z-score
Population .01*** (.001) 5.24 .01*** (.001) 5.86 .01*** (.001) 6.08
Percent Hispanic 1.92** (.59) 3.23 1.62** (.59) 3.06 1.77** * (.53) 3.38
Median household income .01*** (.001) − 6.79 .01*** (.001) − 4.85 .01*** (.001) − 4.47
Percent African American/Black 2.37*** (.51) 4.68 2.57*** (.51) 6.05 2.28*** (.43) 5.31
Alcohol outlet density .19*** (.03) 6.16 .16*** (.03) 5.09
Abandoned property count .01** (.02) 2.53
Spatial filter 1.84* (.71) 2.64 − 1.25* (.63) − 1.95 − 1.20* (.61) − 1.95
Null deviance (df) 237.22 (106) 332.87 (106) 355.81 (106)
Residual deviance (df) 116.77 (101) 120.45 (100) 121.80 (99)
AIC 680.39 647.2 643.56

Journal of Community Health
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Discussion
The link between AOD, abandoned properties, and vio-
lent crime is a critical public health concern, which is
why understanding the environmental and social contexts
that contribute to this relationship are so important. The
connection between AOD and violent crime in urban
communities is well documented in the extant substance
abuse and public health literature [5, 14, 36, 37]. The
common theme among these studies is the idea that AOD
contributes to community disadvantage and increases the
probability of violence in communities [6, 9]. There are
also investigations that document when alcohol outlets
are removed from a community, crime in these locations
reduces over-time [14], providing additional support on
the role the built-environment and businesses such as alco-
hol outlets have on a community.
Similarly, the research connecting abandoned proper-
ties with community violence, like AOD and community
violence is well established. These studies indicate that as
abandoned property density increases so does the prob-
ability for violent crime, as well as other social concerns
including substance use, the illicit sale of drugs, and
prostitution [1, 7, 8, 16, 22]. Despite such scholarship,
less research has examined the association AOD and the
density of abandoned or vacant properties have on violent
crime [2]. This is an important area of inquiry when we
consider the influence built environment has on commu-
nity well-being.
This investigation, using spatial analyses techniques,
investigated the relationship AOD and the density of aban-
doned or vacant properties have on violent crime, control-
ling for demographic variables. Our first model supports
the assertion that violent crime occurs at higher rates in
census blocks with populations that are more impover-
ished and have a larger number of persons of color. These
findings are in overall agreement with previous research
indicating that due to White flight, social and racial seg-
regation, and city-level neglect, minoritized persons of
color—i.e., largely Hispanic and African American—are
more likely to not only live in poverty but experience dis-
proportionate rates of violence [1, 22]. High-dense urban
communities with concentrated economic and social dis-
advantage produce negative outcomes for individuals,
often persons of color, living in these communities [38,
39]. These effects are often further augmented when we
consider structural concerns that negatively contribute to
community ecology, such as alcohol outlets [3, 9, 14] and
abandoned properties [22].
Our second model provides additional support in rela-
tion to the idea that selected place characteristics, such
as AOD, are related to violent crime rates. The density
of alcohol outlets in this study was positively associated
with rates of violence, independent of population char-
acteristics. Consistent with previous research, AOD are
often located in poor communities of color [3, 9, 13, 14],
which were controlled for as covariates in this study, thus
excluding as an explanation for effects related to AOD on
violent crime.
Our final model indicated that significant statistical
effects on violent crime were present in census blocks with
increased AOD and the density of abandoned or vacant
properties. These findings are supported by previous stud-
ies indicating that the presence of abandoned properties
not only increases rates of violent crime [7, 8], but that
AOD continues to also have a robust effect. This may point
toward the compounding influence of structural concerns in
impoverished urban communities. The combination of these
potentials, or structural issues, increases the probability of
violence [40]. The intersection of these community struc-
tures needs to be considered by both violence and substance
abuse prevention programs and regulatory efforts alike that
control not only the growth of alcohol outlets but consider
ways to reduce abandoned or vacant properties—although
not through gentrification.
Limitations
This study has several limitations. First, our analyses relied
on cross-sectional data, which does not allow us to make
claims of causality. Prior longitudinal studies have provided
some evidence on the association between AOD [5], the den-
sity of vacant or abandoned properties [16, 24], and violent
crime in a community, separately. However, as far as these
authors are aware there are no studies examining violent
crime effects of introducing both alcohol outlets and aban-
doned properties into a community over-time.
Other important limitations of the current study include:
(1) absence of controls for other local environmental char-
acteristics that may be associated with violence; and (2)
the focus on violence from one source of data (i.e., police
department). Other characteristics of the community and
population may be related to violence experienced by res-
idents of various communities. Among these may be the
presence of illegal drug markets and the association with
community violence [41]. Another point of interest may be
the presence and increase of recent foreclosures in a com-
munity, which contributes to the number of vacant proper-
ties [42].
In addition, the current study focused on a measure of
violence based on police-incidence reports. While this meas-
ure is supported by previous research and the definition put
forward by the UCR it may limit the accuracy of violent
crime counts in a community. Some evidence indicates that

Journal of Community Health
1 3
simple assaults that involve little or no physical contact are
either under-reported or not reported at all [6, 43]. Further,
communities of color tend to be distrustful of police and
may underreport certain crimes in their community [44, 45].
To address this limitation, studies elsewhere have utilized
data of victims of violent crime appearing in hospitals [46],
which in tandem with police-incidence reports may begin
to provide a more holistic understanding of violent crime in
a target community.
A final limitation concerns the politically defined geo-
graphic units used in this study—i.e., census blocks. These
geographic units vary in shape and size and may even influ-
ence the outcomes of studies [47]. Although there is some
consistency in findings across studies identifying the asso-
ciation between AOD, the density of vacant or abandoned
properties in a community, and violent crime, this limitation
should be considered when interpreting findings, as it may
have some unobserved biases on analyses.
Conclusion
The findings from this study may be important to the city
of Paterson, New Jersey, as well as those cities with simi-
lar demographic and socio-ecological features. Findings
generated from this study indicate that there may be a link
between the density of vacant or abandoned property, the
AOD, and violent crime. Building on the urban planning
literature, land-use policies may need to be explored, which
provide guidelines on the number of alcohol establishments
allowed in a designated community and through zoning
restrict access to alcohol outlets [48]. These authors are not
suggesting a ban on alcohol products but instead that both
state and local-level policymakers assess policies related to
AOD and how these existing policies may negatively con-
tribute to the health, well-being, and safety of the public.
Such a task may be particularly critical in communities
already experiencing additional socio-ecological concerns,
such as a high density of vacant properties [1, 22].
Similarly, partnerships need to be created and supported
between city policymakers, agencies, and community mem-
bers (e.g., a community coalition). Through these com-
munity partnerships, strategic plans can be developed that
address socio-ecological and public health concerns related
to both alcohol outlets and abandoned or vacant properties.
One such project for abandoned or vacant properties may be
vacant lot greening, wherein through city buyback programs
of abandoned properties, city policymakers place monies
toward greening vacant lands, which has been associated
with crime reductions [1]. Other community coalitions have
implemented demolition plans to address the problem of
abandoned or vacant properties and limit crime in already
impoverished and oppressed communities, as exemplified in
Camden, New Jersey [49].
Communities elsewhere have addressed AOD concerns
through the creation of ordinances that designate “hot-spots”
or locations where the association between AOD and violent
crime is the greatest. As designated “hot-spots”, community
coalitions and policymakers can develop place-based inter-
ventions that utilize community-based policing practices
[50] and simultaneously curb issues associated with AOD
without over policing communities that are often poor and
‘of-color’. It is however critical that community members,
coalitions, and policy-makers work collaboratively with
police to monitor and support such strategies to limit the
potential for over-policing and abuses by police on the pub-
lic [50]. Multisector and community-based initiatives have
the potential to stimulate robust community changes that
honor the voices and perceptions of community members
from various sociodemographic backgrounds.
Funding Drug Free Communities Grant (DFC) Initiative (Grant
#SPO22019-01). Funded through the Substance Abuse and Mental
Health Services Administration (SAMHSA).
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict of
interest.
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