Community vibrancy and its relationship with safety in Philadelphia

To what extent can the strength of a local urban community impact neighborhood safety? We construct measures of community vibrancy based on a unique dataset of block party permit approvals from the City of Philadelphia. Our first measure captures the overall volume of block party events in a neighborhood whereas our second measure captures differences in the type (regular versus spontaneous) of block party activities. We use both regression modeling and propensity score matching to control for the economic, demographic and land use characteristics of the surrounding neighborhood when examining the relationship between crime and our two measures of community vibrancy. We conduct our analysis on aggregate levels of crime and community vibrancy from 2006 to 2015 as well as the trends in community vibrancy and crime over this time period. We find that neighborhoods with a higher number of block parties have a significantly higher crime rate, while those holding a greater proportion of spontaneous block party events have a significantly lower crime rate. We also find that neighborhoods which have an increase in the proportion of spontaneous block parties over time are significantly more likely to have a decreasing trend in total crime incidence over that same time period.

Stay-at-Home Orders during COVID-19: The Influence on Physical Activity and Recreational Screen Time Change among Diverse Emerging Adults and Future Implications for Health Promotion and the Prevention of Widening Health Disparities

Background: The purpose of this study was to examine changes in physical activity (PA) and recreational screen time (RST) behaviors from pre-COVID-19 in 2018 to Spring 2020 during the mandatory stay-at-home order in an ethnically/racially, socioeconomically diverse sample of emerging adults. Methods: Longitudinal data were analyzed from 218 participants (Mage = 24.6 ± 2.0 years) who completed two surveys: EAT 2018 (Eating and Activity over Time) and C-EAT in 2020 (during COVID-19). Repeated ANCOVAs and multiple linear regression models were conducted. Results: Moderate-to-vigorous and total PA decreased (4.7 ± 0.3 to 3.5 ± 0.3 h/week [p < 0.001] and 7.9 ± 0.4 to 5.8 ± 0.4 h/week [p < 0.001], respectively), and RST increased from 26.5 ± 0.9 to 29.4 ± 0.8 h/week (p = 0.003). Perceived lack of neighborhood safety, ethnic/racial minoritized identities, and low socioeconomic status were significant predictors of lower PA and higher RST during COVID-19. For example, low SES was associated with 4.04 fewer hours of total PA compared to high SES (p < 0.001). Conclusions: Stay-at-home policies may have significantly influenced PA and RST levels in emerging adults with pre-existing disparities exacerbated during this mandatory period of sheltering-in-place. This suggests that the pandemic may have played a role in introducing or magnifying these disparities. Post-pandemic interventions will be needed to reverse trends in PA and RST, with a focus on improving neighborhood safety and meeting the needs of low socioeconomic and ethnic/racial minoritized groups.

Crime and Perceived Neighborhood Safety Across Racial-Ethnic Groups

Crime often increases safety concerns for residents, and safety concerns are generally associated with worse health. Despite that marginalized racial/ethnic groups are more likely than non-Hispanic Whites to live in areas with more crime, prior studies have documented that these groups differentially view crime as a threat to safety. Furthermore, older adults are more likely to report safety concerns than younger adults, despite a lesser chance of being victimized. Using multiple waves of data from the Health and Retirement Study, a representative sample of US adults aged 51 years and older (n= 11,161, mean age of 66 years), we conducted weighted repeated cross-sectional linear regressions to examine whether the association between crime and perceived neighborhood safety varies by racial/ethnic group, by age, or by wave of data collection. Study results indicated that higher crime rates consistently predicted more safety concerns among non-Hispanic Whites, Hispanics, and “Others,” but were inconsistently associated with safety concerns among non-Hispanic Blacks, adjusting for age, household wealth, and census tract-level concentrated disadvantage, population density, and racial/ethnic heterogeneity. Furthermore, among non-Hispanic Whites, greater crime predicted more safety concerns before, but not after including a measure of racial/ethnic heterogeneity. These patterns persisted across the full age span. Racial/ethnic differences in the crime-safety link could be explained by additional sociopolitical and environmental variables including diversity that vary over time. Follow-up analysis is needed to determine if the racial/ethnic differences in crime-safety links extend to health.

Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases

An accurate crime prediction and risk estimation can help improve the efficiency and effectiveness of policing activities. However, reports have revealed that biases like racial prejudice could exist in policing enforcement, and trained predictors may inherit them. In this work, we study the possible reasons and countermeasures to this problem, using records from the New York frisk and search program (NYCSF) as the dataset. Concretely, we provide analysis on the possible origin of this phenomenon from the perspective of risk discrepancy, and study it with the scope of selection bias. Motivated by theories in causal inference, we propose a re-weighting approach based on propensity score to balance the data distribution, with respect to the identified treatment: search action. Naively applying existing re-weighting approaches in causal inference is not suitable as the weight is passively estimated from observational data. Inspired by adversarial learning techniques, we formulate the predictor training and re-weighting as a min-max game, so that the re-weighting scale can be automatically learned. Specifically, the proposed approach aims to train a model that: 1) able to balance the data distribution in the searched and un-searched groups; 2) remain discriminative between treatment interventions. Extensive evaluations on real-world dataset are conducted, and results validate the effectiveness of the proposed framework.

Active Aging through Volunteerism: A Longitudinal Assessment of Perceived Neighborhood Safety as a Predictor among Older Adults, 2008-2018

Volunteering can play an important role in active aging. The resource theory of volunteering posits that volunteerism depends on human, social, and cultural capital. Benefits of volunteering have been documented at the micro-, meso-, and macrolevels, positively affecting individual older people as well as their local communities and society at large. Taking a process-oriented theoretical approach, this study focused on the mesolevel factor of the environment with the purpose of determining the relationship between perceived neighborhood safety and volunteerism over the course of a decade and the extent to which this relationship differs by gender and race. Longitudinal data from the Health and Retirement Study in the United States of America between 2008 and 2018 were used ( N = 72,319 adults 60 years and older). Generalized estimating equations (GEE) with robust standard errors were employed while controlling for a number of covariates. A third of the sample volunteered in the past year (33%). The probability of volunteering among older adults who rated their perceived neighborhood safety as excellent was greater compared with those who rated their perceived neighborhood safety as fair/poor after controlling for all other model covariates (ME: 0.03, 95% CI: 0.02, 0.05). Among males rating their perceived neighborhood safety as excellent, the probability of volunteering was higher (ME: 0.04, 95% CI: 0.02, 0.07). Among females, the probability of volunteering was higher among those who perceived their neighborhood safety to be excellent (ME: 0.03, 95% CI: 0.01, 0.05) or very good (ME: 0.02, 95% CI: 0.00, 0.04). White respondents who rated their neighborhood safety as excellent (ME: 0.05, 95% CI: 0.03, 0.07) or very good (ME: 0.04, 95% CI: 0.02, 0.06) had a higher probability of volunteerism. Results were not significant among Black respondents and those who described their race as “other.” This study’s process-oriented theoretical approach indicates that initiatives aimed at improving neighborhood safety and older adults’ perceptions of neighborhood safety could increase social capital and lead older adults to engage in more volunteering, providing benefits at micro-, meso-, and macrolevels—to older individuals, their local communities, and society at large.