The Role of Infant Health Problems in Constraining Interneighborhood Mobility: Implications for Citywide Employment Networks.

Infant health problems are a persistent concern across the United States, disproportionally affecting socioeconomically vulnerable communities. We investigate how inequalities in infant health contribute to differences in interneighborhood commuting mobility and shape neighborhoods' embeddedness in the citywide structure of employment networks in Chicago over a 14-year period. We use the Census Bureau's Longitudinal Employer-Household Dynamics' Origin-Destination Employment Statistics to analyze commuting networks between 2002 and 2015. Results from longitudinal network analyses indicate two main patterns. First, after the Great Recession, a community's infant health problems began to significantly predict isolation from the citywide employment network. Second, pairwise dissimilarity in infant health problems predicts a lower likelihood of mobility ties between communities throughout the entire study period. The findings suggest that infant health problems present a fundamental barrier for communities in equally accessing the full range of jobs and opportunities across the city-compounding existing inequalities.

Connected in health: Place-to-place commuting networks and COVID-19 spillovers.

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

Socially Connected Neighborhoods and the Spread of Sexually Transmitted Infections.

Sexually transmitted infections (STIs) in the United States have been increasing at record levels and exhibit unequal spatial patterning across urban populations and neighborhoods. Research on the effects of residential and nearby neighborhoods on STI proliferation has largely ignored the role of socially connected contexts, even though neighborhoods are routinely linked by individuals' movements across space for work and other social activities. We showcase how commuting and public transit networks contribute to the social spillover of STIs in Chicago. Examining data on all employee-employer location links recorded yearly by the Census Bureau for more than a decade, we assess network spillover effects of local community STI rates on interconnected communities. Spatial and network autoregressive models show that exposure to STIs in geographically proximate and socially proximate communities contributes to increases in local STI levels, even net of socioeconomic and demographic factors and prior STIs. These findings suggest that geographically proximate and socially connected communities influence one another's infection rates through social spillover effects.

Creative Approaches for Assessing Long-term Outcomes in Children.

Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.

Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods.

OBJECTIVES: Our goal is to understand the social dynamics affecting domestic and sexual violence in urban areas by investigating the role of connections between area nodes, or communities. We use innovative methods adapted from spatial statistics to investigate the importance of social proximity measured based on connectedness pathways between area nodes. In doing so, we seek to extend the standard treatment in the neighborhoods and crime literature of areas like census blocks as independent analytical units or as interdependent primarily due to geographic proximity. METHODS: In this paper, we develop techniques to incorporate two types of proximity, geographic proximity and commuting proximity in spatial generalized linear mixed models (SGLMM) in order to estimate domestic and sexual violence in Detroit, Michigan and Arlington County, Virginia. Analyses are based on three types of CAR models (the Besag, York, and Mollié (BYM), Leroux, and the sparse SGLMM models) and two types of SAR models (the spatial lag and spatial error models) to examine how results vary with different model assumptions. We use data from local and federal sources such as the Police Data Initiative and American Community Survey. RESULTS: Analyses show that incorporating information on commuting ties, a non-spatially bounded form of social proximity, to spatial models contributes to better deviance information criteria (DIC) scores (a metric which explicitly accounts for model fit and complexity) in Arlington for sexual and domestic crime as well as overall crime. In Detroit, the fit is improved only for overall crime. The distinctions in model fit are less pronounced when using cross-validated mean absolute error (MAE) as a comparison criteria. CONCLUSION: Overall, the results indicate variations across crime type, urban contexts, and modeling approaches. Nonetheless, in important contexts, commuting ties among neighborhoods are observed to greatly improve our understanding of urban crime. If such ties contribute to the transfer of norms, social support, resources, and behaviors between places, they may then transfer also the effects of crime prevention efforts.

Network spillovers and neighborhood crime: A computational statistics analysis of employment-based networks of neighborhoods.

Research on communities and crime has predominantly focused on social conditions within an area or in its immediate proximity. However, a growing body of research shows that people often travel to areas away from home, contributing to connections between places. A few studies highlight the criminological implications of such connections, focusing on important but rare ties like co-offending or gang conflicts. The current study extends this idea by analyzing more common ties based on commuting across Chicago communities. It integrates standard criminological methods with machine learning and computational statistics approaches to investigate the extent to which neighborhood crime depends on the disadvantage of areas connected to it through commuting. The findings suggest that connected communities can influence each other from a distance and that connectivity to less disadvantaged work hubs may decrease local crime-with implications for advancing knowledge on the relational ecology of crime, social isolation, and ecological networks.

Non-Stationary Model for Crime Rate Inference Using Modern Urban Data.

Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime rate is critical for policy makers in their efforts to reduce crime and increase citizens' life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we use large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observe significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis. The correlations between crime and various observed features are not constant over the whole city. In order to address this geospatial non-stationary property, we further employ the geographically weighted regression on top of negative binomial model (GWNBR). Experiments have shown that GWNBR outperforms the negative binomial model.

Neighborhood Isolation in Chicago: Violent Crime Effects on Structural Isolation and Homophily in Inter-Neighborhood Commuting Networks, 2002-2013.

Urban sociologists and criminologists have long been interested in the link between neighborhood isolation and crime. Yet studies have focused predominantly on the internal dimension of social isolation (i.e., increased social disorganization and insufficient jobs and opportunities). This study highlights the need to assess the external dimension of neighborhood isolation, the disconnectedness from other neighborhoods in the city. Analyses of Chicago's neighborhood commuting network over twelve years (2002-2013) showed that violence predicted network isolation. Moreover, pairwise similarity in neighborhood violence predicted commuting ties, supporting homophily expectations. Violence homophily affected tie formation most, while neighborhood violence was important in dissolving ties.

The Long Arm of Poverty: Extended and Relational Geographies of Child Victimization and Neighborhood Violence Exposures.

Current models of neighborhood effects on victimization predominantly assume that residential neighborhoods function independent of their surroundings. Yet, a surprising proportion of violence occurs outside of victims' residential neighborhoods. The current study extends on recent advances in spatial dynamics and neighborhood effects to explore the importance of different geographic scales and relational exposures to poverty for child violent victimization. We examine longitudinal data on over 4,400 low-income children from high poverty neighborhoods in five cities, who participated in the Moving to Opportunity randomized intervention. The results suggest that surrounding poverty matters for child victimization beyond the effect of residential poverty. Moreover, moving farther from extreme poverty also seems to buffer against victimization and to amplify the benefits of moving to improved extended (residential and surrounding) neighborhoods. All children in the study, but especially boys older than 10 years of age, seemed to be affected by the long arm of poverty.

Moving to opportunity and mental health: Exploring the spatial context of neighborhood effects.

Studies of housing mobility and neighborhood effects on health often treat neighborhoods as if they were isolated islands. This paper argues that conceptualizing neighborhoods as part of the wider spatial context within which they are embedded may be key in advancing our understanding of the role of local context in the life of urban dwellers. Analyses are based on mental health and neighborhood context measurements taken on over 3000 low-income families who participated in the Moving to Opportunity for Fair Housing Demonstration Program (MTO), a large field experiment in five major U.S. cities. Results from analyses of two survey waves combined with Census data at different geographic scales indicate that assignment to MTO's experimental condition of neighborhood poverty <10% significantly decreased average exposure to immediate and surrounding neighborhood disadvantage by 97% and 59% of a standard deviation, respectively, relative to the control group. Escaping concentrated disadvantage in either the immediate neighborhood or the surrounding neighborhood, but not both, was insufficient to make a difference for mental health. Instead, the results suggest that improving both the immediate and surrounding neighborhoods significantly benefits mental health. Compared to remaining in concentrated disadvantage in the immediate and surrounding neighborhoods, escaping concentrated disadvantage in both the immediate and surrounding neighborhoods (on average over the study duration) as a result of the intervention predicts an increase of 25% of a standard deviation in the composite mental health scores.

(Un)natural disaster: vulnerability, long-distance displacement, and the extended geography of neighborhood distress and attainment after Katrina.

After Hurricane Katrina, socioeconomically vulnerable populations were slow to return to their poor and segregated pre-disaster neighborhoods. Yet, very little is known about the quality of their post-disaster neighborhoods. While vulnerable groups rarely escape neighborhood poverty, some Katrina evacuees showed signs of neighborhood improvement. The current study investigates this puzzle and the significance of long-distance moves for neighborhood change among participants in the Resilience in the Survivors of Katrina Project. Seven hundred low-income, mostly minority mothers in community college in New Orleans before Katrina were tracked across the country a year and a half later. The findings show that respondents' immediate and extended neighborhoods and metropolitan areas after Katrina were less disadvantaged, less organizationally isolated, and more racially and ethnically diverse compared to their pre-hurricane environments, and to the environments of those staying or returning home. Counterfactual analyses showed that more than within-neighborhood changes over time, between-neighborhood mobility and long-distance migration decreased respondents' exposures to distress in their neighborhood, extended geographic area, and metropolitan area.

Health Selection into Neighborhoods Among Families in the Moving to Opportunity Program.

Moving to Opportunity for Fair Housing was a randomized experiment that moved very low-income US families from high-poverty neighborhoods to low-poverty neighborhoods starting in the early 1990s. We modeled report of a child's baseline health problem as a predictor of neighborhood outcomes for households randomly assigned to move from high- to low-poverty neighborhoods. We explored associations between baseline health problems and odds of moving with the program upon randomization (1994-1997), neighborhood poverty rate at follow-up (2002), and total time spent in affluent neighborhoods and duration-weighted poverty. Among 1,550 households randomized to low-poverty neighborhoods, a smaller share of households reporting baseline child health problems (P = 0.004) took up the intervention (38%) than those not reporting a health problem (50%). In weighted and covariate-adjusted models, a child health problem predicted nearly 40% lower odds of complying with the experimental condition (odds ratio = 0.62, 95% confidence interval: 0.42, 0.91; P = 0.015). Among compliers, a baseline child health problem predicted 2.5 percentage points' higher neighborhood poverty at take-up (95% confidence interval: 0.90, 4.07; P = 0.002). We conclude that child health problems in a household prior to randomization predicted lower likelihood of using the program voucher to move to a low-poverty neighborhood within the experiment's low-poverty treatment arm and predicted selection into poorer neighborhoods among experimental compliers. Child morbidity may constrain families attempting to improve their life circumstances.

Urban Poverty and Neighborhood Effects on Crime: Incorporating Spatial and Network Perspectives.

Research on neighborhoods and crime is on a remarkable growth trajectory. In this article, we survey important recent developments in the scholarship on neighborhood effects and the spatial stratification of poverty and urban crime. We advance the case that, in understanding the impact of neighborhoods and poverty on crime, sociological and criminological research would benefit from expanding the analytical focus from residential neighborhoods to the network of neighborhoods individuals are exposed to during their daily routine activities. This perspective is supported by reemerging scholarship on activity spaces and macro-level research on inter-neighborhood connections. We highlight work indicating that non-residential contexts add variation in criminogenic exposure, which in turn influence offending behavior and victimization risk. Also, we draw on recent insights from research on gang violence, social and institutional connections, and spatial mismatch, and call for advancements in the scholarship on urban poverty that investigates the salience of inter-neighborhood connections in evaluating the spatial stratification of criminogenic risk for individuals and communities.

Spatial Heterogeneity in the Effects of Immigration and Diversity on Neighborhood Homicide Rates.

This paper examines the connection of immigration and diversity to homicide by advancing a recently developed approach to modeling spatial dynamics-geographically weighted regression. In contrast to traditional global averaging, we argue on substantive grounds that neighborhood characteristics vary in their effects across neighborhood space, a process of "spatial heterogeneity." Much like treatment-effect heterogeneity and distinct from spatial spillover, our analysis finds considerable evidence that neighborhood characteristics in Chicago vary significantly in predicting homicide, in some cases showing countervailing effects depending on spatial location. In general, however, immigrant concentration is either unrelated or inversely related to homicide, whereas language diversity is consistently linked to lower homicide. The results shed new light on the immigration-homicide nexus and suggest the pitfalls of global averaging models that hide the reality of a highly diversified and spatially stratified metropolis.