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

Did the association between alcohol outlet density and crime change during COVID shelter-in-place orders?

INTRODUCTION

We investigated whether greater concentrations of on- and off-sale alcohol outlets were associated with crime and whether this association was moderated by COVID-19 shelter-in-place orders (SIP) that restricted on-premises consumption of alcohol.

METHODS

Crimes (2019-2020) and addresses of licenced alcohol outlets in a medium-sized California city were geocoded within census block groups (N = 61). On- and off-sale alcohol outlet density was calculated as licenced outlets/2.59 km2 (1 square mile). Multilevel negative binomial regression analyses were conducted to examine associations between alcohol outlet density and crime, and possible moderating effects of SIP, controlling for block group demographic characteristics and density of other retail businesses.

RESULTS

On-sale outlet density was positively associated with total crimes and Part 2 crimes, while off-sale outlet density was inversely associated with total crime and Part 2 crimes. Overall, SIP was not significantly associated with crime, but moderated the associations of on-sale density with total crime and Part 1 crimes such that reductions in crime during SIP were observed in higher density areas. The association of off-sale outlets with crime was not moderated by SIP policies.

DISCUSSION AND CONCLUSION

On-sale outlet density, but not off-sale density, appears to be associated with increased crime. The results further indicate that restrictions in hours and service imposed by SIP policies reduced crime in high on-sale outlet density areas. These findings reinforce the importance of regulating alcohol outlet density and hours of service, especially for on-sale outlets, as a crime reduction strategy.

Validation of a neighborhood sentiment and safety index derived from existing data repositories

BACKGROUND

The communities we live in are central to our health. Neighborhood disadvantage is associated with worse physical and mental health and even early mortality, while resident sense of safety and positive neighborhood sentiment has been repeatedly linked to better physical and mental health outcomes. Therefore, understanding where negative neighborhood sentiment and safety are salient concerns can help inform public health interventions and as a result, improve health outcomes. To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys.

OBJECTIVE

The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories.

METHODS

The NSSI was created using Principal Component Analysis. Mineigen and minimum loading values were 1 and 0.3, respectively. Throughout the step-wise PCA process, variables were excluded if their loading value was below 0.3 or if variables loaded into multiple components.

RESULTS

The novel index was validated against standardized survey items from a longitudinal cohort study in the Northeastern United States characterizing experiences of (1) Neighborhood Characteristics with a Pearson correlation of -0.34 (p < 0.001) and, (2) Neighborhood Behavior Impact with a Pearson correlation of -0.33 (p < 0.001). It also accurately predicted the Share Care Community Well Being Index (Spearman correlation = 0.46) and the neighborhood deprivation index (NDI) (Spearman correlation = -0.75).

SIGNIFICANCE

Our NSSI can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies.

IMPACT STATEMENT

To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. The NSSI was validated against four separate measures and can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies.

How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis

Urban crimes are a severe threat to livable and sustainable urban environments. Many studies have investigated the patterns, causes, and strategies for curbing the occurrence of urban crimes. It is found that neighborhood socioeconomic status, physical environment, and ethnic composition all might play a role in the occurrence of urban crimes. Inspired by the recent interest in exploring urban crime patterns with spatial data analysis techniques and the development of Bayesian hierarchical analytical approaches, we attempt to explore the inherently intricate relationships between urban assaultive violent crimes and the neighborhood socioeconomic status, physical environment, and ethnic composition in Paterson, NJ, using census data of the American Community Survey, alcohol and tobacco sales outlet data, and abandoned property listing data from 2013. Analyses are set at the census block group level. Urban crime data are obtained from the Paterson Police Department. Instead of examining relationships at a global level with both non-spatial and spatial analyses, we examine in depth the potential locally varying relationships at the local level through a Bayesian hierarchical spatially varying coefficient model. At both the global and local analysis levels, it is found that median household income is decisively negatively related to urban crime occurrence. Percentage of African Americans and Hispanics, number of tobacco sales outlets, and number of abandoned properties are all positively related with urban crimes. At the local level of analysis, however, the different factors have varying influence on crime occurrence throughout the city of Paterson, with median household income having the broadest influence across the city. The practice of applying a Bayesian hierarchical spatial analysis framework to understand urban crime occurrence and urban neighborhood characteristics enables urban planners, stakeholders, and public safety officials to engage in more active and targeted crime-reduction strategies.

Logistic Regression Model of Demographic Predictors and Confounders of Binge Alcohol Use Among Adults with Major Depression

Identifying sociodemographic populations with a major depressive episode (MDE) who are at increased risk for binge alcohol use (BAU) is critical for developing focused prevention programs to meet the needs of each population. Thus, the goal of the current exploratory retrospective study is to examine if sex, race, age, education, marital status, and income can significantly predict the risk for BAU among adults with MDE and if the association between MDE and BAU changes after adjusting for demographic variables in question while holding sex, race, and age as constant variables. Data from the Substance Abuse and Mental Health Services Administration's 2018 National Survey for Drug Use and Health were extracted and analyzed to achieve the study goal. The unweighted sample included 6,999 adults representing a weighted population size of 33,900,452.122 in the USA. Results revealed that age and marital status significantly predicted BAU in the past month among adults with MDE. Adults under the age of 50, with a college degree, never married, divorced/separated, and with a high-middle income level or higher were at higher risk for BAU. The study's clinical implications are that understanding demographics of individuals with MDE at increased risk for BAU can inform improved tailored assessment and treatment of alcohol abuse and MDE among at-risk populations. Future research should consider examining additional potential risk factors for BAU among adults with MDE.

A Spatial Analysis of Alcohol Outlet Density Type, Abandoned Properties, and Police Calls on Aggravated Assault Rates in a Northeastern U.S. City

Background: Community-based research and prevention scholarship has shown a growing interest in examining the impact of the ecological structure of neighborhoods on community violence. Ecological structures such as higher numbers of alcohol outlets and abandoned/vacant properties in geographically dense, poor, and socially isolated communities are critically important to consider. Further, disadvantaged urban communities are burdened by greater police presence with limited or no abatement in crime or violence. Purpose: Using Geographic Information Systems (GIS) mapping, spatial analysis techniques, and a negative binomial regression analyses, this study investigated the relationships between alcohol outlet density consisting of license C (i.e., combined on and off premises establishments) and license D (i.e., off premises establishments such as liquor stores), abandoned properties, and police calls to service on aggravated assault rates in a Northeastern United States urban city. Results: Negative binomial regression analysis results showed that license C alcohol outlet density count (IRR = 1.36, 95% CI = 1.08, 2.11), license D alcohol outlet density count (IRR = 1.13, 95% CI = 1.01, 1.30), abandoned property count (IRR = 1.28, 95% CI = 1.01, 2.01), and police call to service counts (IRR = 1.24, 95% CI = 1.01, 1.50) were positively associated with aggravated assault rates. Analyses controlled for census data characteristics. Conclusions: The link between alcohol outlet density and violent crime has been established in public health research. However, this study represents an important contribution in recognizing the unique relationships between license C and license D alcohol outlet density, abandoned properties, and police calls to service with aggravated assaults in an urban neighborhood. Conclusions are drawn for policy and practice.

"Bullets Have no Names": A Qualitative Exploration of Community Trauma Among Black and Latinx Youth

Repeated exposure to violent crime and drug use among youth of color can have negative health and behavioral outcomes. Using a community trauma theoretical framework, this qualitative study examines the lived experiences of Black and Latinx youth living in an under-resourced community with high levels of violent crime. Data were collected through eleven focus groups comprising of (N = 59) youth. Common themes arose including daily occurrences of witnessing gun violence, feelings of hopelessness, and lack of opportunities among youth. Implications highlight the importance of youth serving community-based organizations to nurture resilience and community healing within trauma-exposed neighborhoods.