The racial landscape of US neighborhoods shifted tremendously over the past decade, raising questions about how these changes are reflected in local schools. This study combines school zone shapefiles with census and Civil Rights Database data to explore whether neighborhood racial change in Black populations predicts changes in Black-white school suspension disparities from 2010 to 2018. We find that racial disparities in suspensions increased, particularly in neighborhoods experiencing substantial shifts in Black populations. These increases were especially pronounced in suburban and rural areas with shifting Black populations and all predominantly White neighborhoods. Although school discipline reforms aimed to reduce racial disparities, they only declined in certain types of neighborhoods, most notably in urban areas. Our findings advance insights into processes of school and neighborhood change, school discipline, and racial stratification, with broad policy implications.

Scholarship on urban change has taken race, class, and culture as competing interpretations through which to explain socio-economic change in urban neighborhoods. However, the literature has focused less on religion as a structural factor shaping this change. This article addresses this gap by studying the spatial clustering of Ultra-Orthodox (Haredi) Jews in London and New York—a group overlooked by urban scholars despite living near commonly studied sites of gentrification. Under the same structural forces of urban change, this “non-liberal” group actively works to secure space through political dealmaking and communal welfare networks which promote the interests of its members over those of outsiders. This account reveals how members benefit from the religious group’s strong internal welfare system and relationship with the state, despite also suffering from housing shortages and unaffordability. Bringing together literatures on race, class, religion, and housing, this article provides a new angle from which to examine how urban communities experience urban change in the gentrification nexus.

BackgroundGoogle Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs.ObjectiveThis study aimed to use GSV images and computer vision to examine longitudinal changes in the built environment, demographic shifts, and health outcomes in Washington, DC, from 2014 to 2019.MethodsIn total, 434,115 GSV images were systematically sampled at 100 m intervals along primary and secondary road segments. Convolutional neural networks, a type of deep learning algorithm, were used to extract built environment features from images. Census tract summaries of the neighborhood built environment were created. Multilevel mixed-effects linear models with random intercepts for years and census tracts were used to assess associations between built environment changes and health outcomes, adjusting for covariates, including median age, percentage male, percentage Hispanic, percentage African American, percentage college educated, percentage owner-occupied housing, and median household income.ResultsWashington, DC, experienced a shift toward higher-density housing, with non-single-family homes rising from 66% to 72% of the housing stock. Single-lane roads increased from 37% to 42%, suggesting a shift toward more sustainable and compact urban forms. Gentrification trends were reflected in a rise in college-educated residents (16%-41%), a US $17,490 increase in the median household income, and a US $159,600 increase in property values. Longitudinal analyses revealed that increased construction activity was associated with lower rates of obesity, diabetes, high cholesterol, and cancer, while growth in non-single-family housing was correlated with reductions in the prevalence of obesity and diabetes. However, neighborhoods with higher proportions of African American residents experienced reduced construction activity.ConclusionsWashington, DC, has experienced significant urban transformation, marked by substantial changes in neighborhood built environments and demographic shifts. Urban development is associated with reduced prevalence of chronic conditions. These findings highlight the complex interplay between urban development, demographic changes, and health, underscoring the need for future research to explore the broader impacts of neighborhood built environment changes on community composition and health outcomes. GSV imagery, along with advances in computer vision, can aid in the acceleration of neighborhood studies.

Neighborhood environments may significantly impact psychological well-being, particularly in Black American communities where historical inequities and resilience factors intersect. This mixed-methods study investigates retrospective perceptions of childhood and current neighborhood social environments and their associations with loneliness and psychological distress in adulthood. Data were drawn from the Think PHRESH study, an ancillary project to the Pittsburgh Hill/Homewood Research on Neighborhood Change and Health (PHRESH). A mixed-methods design integrated survey data from 739 participants (77.5% female; mean age = 63.37) and qualitative interviews with 56 residents (60.7% female; mean age = 65.95). Measures assessed childhood and adulthood neighborhood social cohesion, collective child-rearing, current neighborhood safety, satisfaction, loneliness, and distress. A mixed-methods expansion approach was used, where qualitative themes informed quantitative model development. Thematic analysis was applied to qualitative data, and regression analyses examined associations between neighborhood factors and mental health. Participants reported significantly higher childhood social cohesion (M = 4.47, SD = 0.70) than adulthood (M = 3.22, SD = 0.69). Qualitative findings highlighted declines in community engagement and safety. Regression analyses showed greater current social cohesion (b = -0.61, SE = 0.25, p = 0.02), neighborhood safety (b = -0.48, SE = 0.21, p = 0.02), and satisfaction (b = -0.55, SE = 0.18, p = 0.002) were associated with lower distress, while higher satisfaction was linked to lower loneliness (b = -0.08, SE = 0.03, p = 0.002). Findings highlight the importance of policies that enhance neighborhood social environments, particularly in predominantly Black urban communities affected by structural inequities.

ABSTRACT This study examines the spatiotemporal economic and social transformations associated with proximity to major university campuses in Canada’s eight largest urban regions from 1981 to 2016. Using quinquennial census data, we develop spatial panel regression models to analyze four dimensions of neighborhood change at the census tract level: rents, young adult populations, immigrant populations, and bachelor’s degree holders. Our findings reveal that census tracts closer to universities exhibit significantly higher average rents, larger young adult populations, greater immigrant populations, and a higher proportion of university-educated residents. However, we find that these relationships vary greatly over time, indicating more complex dynamics than previously understood. The concentration of young adults, immigrants, and educated individuals near universities has only emerged since the 1980s, while rents in these areas have increased more slowly compared to other metropolitan regions, suggesting convergence rather than gentrification. Additionally, the growing proximity of the immigrant population to universities reflects a longstanding trend rather than a recent development associated with international student enrollment. These results highlight the dynamic nature of university-neighborhoods’ relationships and underscore the significance of these institutions in shaping the economic and social geographies of Canadian urban regions.

Abstract While individual correlates (age, sex, physical function) of loneliness have been well-documented, less is known about whether neighborhood characteristics modify these associations. Guided by the Ecological Theory of Aging, this study examined whether subjective and objective neighborhood factors moderate the associations between individual correlates and loneliness among adults in two predominantly Black neighborhoods. Data were drawn from the Think PHRESH study (N = 753), an ancillary study to a longitudinal cohort study of the Pittsburgh Hill/Homewood Research on Neighborhood Change and Health (PHRESH). Loneliness was measured using the UCLA 3-item scale. Subjective neighborhood factors included perceived safety and neighborhood satisfaction. Objective factors included socioeconomic deprivation and residential instability. Multivariable linear regressions with interaction terms tested moderation effects. Among those perceiving their neighborhoods as less safe, middle-aged adults (50-65 years) and older adults (≥65 years) reported significantly higher loneliness compared to younger adults (31-49 years). These age-related differences were not observed among those reporting high safety. A similar pattern emerged for neighborhood satisfaction: greater loneliness was reported among middle-aged and older adults with low neighborhood satisfaction, but not among those with high satisfaction. Functional limitations were also linked to greater loneliness among those with low perceived safety or neighborhood satisfaction. Objective neighborhood measures did not moderate associations. Findings demonstrate that perceptions of neighborhood safety and satisfaction interact with individual vulnerabilities to shape loneliness. Supportive neighborhood conditions may buffer loneliness among high-risk groups. These findings highlight the need for community-based interventions that enhance subjective neighborhood experiences to buffer against loneliness in historically marginalized communities.

Abstract More accurately measuring lifecourse environmental exposures requires working with data not readily available from digitized sources. Here we detail the processes for scouring city archives to uncover aspects of structural racism in the built environment. In our initial non-human subjects work in Baltimore, we have built datasets on the built environment and prepared methods to collect residential histories, which together allow us to study impacts of residential exposures across the lifecourse for this longitudinal cohort of aging adults. We discuss the iterative process of archive visits—working with team members who are experts in this field—to obtain the full complement of features in the built environment driven by structurally racist policies (i.e. redlining, blockbusting, restrictive covenants, urban renewal, freeway construction, predatory lending). We then discuss how residential histories are collected from our cohort, giving us lifecourse perspectives on where individuals have lived. These allow for the assignment of age- and period-specific exposures (e.g. one might expect developmental exposure to proximal neighborhood change to be more impactful than adult exposure to stable neighborhoods). These environmental data will eventually be spatially and temporally joined to our health data in GIS software, and multilevel models will allow us to examine what features of the environment and durations of exposure matter most to cognitive decline. Such data allow us to move ‘beyond redlining’ and other simplistic methods to consider more complex exposure frameworks.

Gentrification is a process of neighborhood change characterized by capital investment in historically disinvested neighborhoods and an influx of residents of higher socioeconomic status. While gentrification generates neighborhood improvements that benefit newcomers, it has been recognized as a public health issue characterized by inequities in housing, economic, and health opportunities for long-term residents, specifically low-income and Black populations, who are at high risk of physical and social displacement. Thus, this cross-sectional ecological study based in Allegheny County, Pennsylvania, sought to (1) identify the relationships between gentrification status and three neighborhood-level indices representing housing conditions, socioeconomic conditions, and environmental conditions and (2) determine whether the rate of the Black population change from 2010 to 2020 modified these relationships. We used separate logistic regression models for three neighborhood-level indices to quantify the relationship between each index and gentrifying census tracts compared with census tracts that were eligible for gentrification but did not gentrify. Gentrifying census tracts were statistically associated with stronger housing markets (i.e., higher housing value) (OR = 0.66; 95% CI: 0.57-0.77), lower levels of community need (OR = 0.42; 95% CI: 0.27-0.65), and lower levels of environmental burden (OR = 0.62; 95% CI: 0.47-0.81) than nongentrifying census tracts were. Because higher index values indicate worse outcomes, these results suggest that gentrifying tracts had relatively better housing market conditions, lower community need, and reduced environmental burden compared to nongentrifying tracts. The rate of Black population change had a marginal interaction effect with each of the indices.

Photoproximity labeling proteomics (PLP) methods have recently shown that cell surface receptors can form lateral interactome networks. Here, we present a paired set of PLP workflows that dynamically track neighborhood changes for oncogenic epidermal growth factor receptor (EGFR) over time, both outside and inside of cells. We achieved this by augmenting the multiscale PLP workflow we call MultiMap, where three photoprobes with different labeling ranges were photoactivated by one photocatalyst, eosin Y, anchored extracellularly and intracellularly on EGFR. We identified hundreds of neighboring proteins that changed within minutes to over 1 h after the addition of EGF. These neighborhoods reveal dynamic interactomes during early, middle and late signaling that drive phosphorylation, internalization, degradation and transcriptional regulation. This rapid 'molecular photographic' labeling approach provides snapshots of signaling neighborhoods, revealing their dynamic nature and potential for drug targeting.

This study advances research on neighborhood change and crime by examining how key structural characteristics relate to changes in violent crime across diverse urban contexts. Utilizing longitudinal data from 2010 to 2018 in twelve U.S. cities, the analysis applies latent trajectory modeling to test three dynamic patterns: monotonic, asymmetric, and perturbation. Results indicate an asymmetric relationship between disadvantage and violent crime, particularly in large cities. Similarly, residential instability exhibits an asymmetric association with violent crime in stagnant and large cities. Changes in racial/ethnic heterogeneity are generally linked to reductions in violent crime in stagnant and large cities, while in small or growing cities, the relationship is asymmetric. This study provides a more nuanced understanding of how structural conditions shape violence.

This study analyzes the language used in real estate advertisements to understand how neighborhoods are communicated and sold as they undergo significant demographic changes. Drawing on a longitudinal database of more than 700,000 property listings across ten U.S. cities from 2007 to 2020, we use text analysis in a ridge logistic regression framework to identify terms most associated with listings in neighborhoods experiencing either an influx or a decline in white residents. We find that white influx neighborhoods are marketed using language associated with consumption amenities like restaurants, fitness studios, stadiums, and museums, and they feature housing characteristics with historic character and luxury upgrades (bungalows, craftsman homes, and high-end appliances). In contrast, listings in white decline neighborhoods emphasize family-oriented public amenities and dated suburban features (schools, playgrounds, and libraries, alongside vinyl and carpeting). These contrasting amenities correspond with associated shifts in neighborhood education, income, and household compositions. By identifying patterns across multiple cities and over time, our study offers a scalable framework for analyzing neighborhood change through real estate language. In doing so, we demonstrate an approach for constructing detailed, spatially granular indicators of neighborhood change typically unavailable through traditional, quantitative spatial data sources.

This study examines how gentrification reshapes neighborhood crime through two underexplored dimensions: third places and demolitions. Traditional research on gentrification and crime has often relied on broad socioeconomic indicators, such as property values or median income, but these measures fail to capture the lived and place-based processes of neighborhood change. Drawing on place-in-neighborhood theory and routine activities theory, this research conceptualizes gentrification as a multidimensional transformation of social and physical space. Using data from Norfolk, Virginia (2015–2019), hierarchical linear models were employed to assess how the emergence of alcohol-licensed third places (e.g., bars, restaurants) and the issuance of demolition permits influenced community-level crime rates. Results indicate that third places decrease crimes against society but increase property crimes, while demolitions exert mixed effects depending on the offense type. By integrating spatial statistics and multilevel modeling, this study demonstrates the value of real-time municipal data for understanding neighborhood transformation. These results suggest that revitalization cannot be treated as a one-dimensional solution to urban crime. This work reframes gentrification as a contested process whose influence on crime depends on place, race, and neighborhood transformation.

Numerous studies have sought to clarify the relationship between gentrification, displacement, and health. However, few quantitative studies have investigated how gentrification may shape the fear of displacement among residents who remain in gentrifying neighborhoods - a hypothesized mechanism linking gentrification to downstream health inequities. Our study used data from a large national cohort of older Black and white US adults to examine the relationship between neighborhood gentrification and individual-level fear of displacement. In 2023 and 2024, participants in the REasons for Geographic and Racial Differences in Stroke study were asked about fear of displacement related to neighborhood change over the past 3-5 years (N = 4,092). Census tract-level gentrification was based on socioeconomic and housing cost changes from the American Community Survey (ACS) 2008-2012 to ACS 2015-2019. We estimated adjusted prevalence ratios (aPR) using Poisson regression and examined effect measure modification by race, educational attainment, and length of residence. Participants' most recent residence was classified as not gentrified (N = 1,927), moderately gentrified (N = 477), intensely gentrified (N = 561), or ineligible to gentrify (N = 1,127). Intense gentrification was associated with more displacement fear (aPR = 1.39; 95% CI: 1.06, 1.83) relative to no gentrification. Any gentrification (combining both moderate and intense gentrification exposure groups) was associated with more displacement fear among Black participants (aPR = 1.69; 95% CI: 1.27, 2.25), but not white participants (aPR = 0.79; 95% CI: 0.52, 1.19). Our findings suggest that gentrification may heighten displacement fears of older US adults, especially among Black residents. Future research should investigate approaches to mitigate displacement fears and racial inequities related to gentrification.

From disorder to distress: Investigating neighborhood change and mental health with Streetview and longitudinal survey data.

Neighborhood affluence and cognitive decline: Moderation by race?

Neighborhood determinants of health have been documented in several populations, yet less is known about their role in pediatric obesity treatment. A systematic review of longitudinal studies examining associations between neighborhood environment features and changes in obesity and in lifestyle behaviors among children participating in obesity management interventions was conducted. Searches were conducted in Medline, Embase, CINAHL, and Web of Science for peer-reviewed articles published in English from database inception until April 2025. We included studies of children with overweight/obesity at baseline, participating in multicomponent obesity management interventions, and with at least one pre- and one post-intervention measurement of obesity or lifestyle behaviors. Of the 27,310 records screened, six met inclusion criteria. Studies were conducted in the United States (n = 5) and United Kingdom (n = 1), with participants' age ranging from 6 to 18 years, and a total of 13,364 participants. Studies examined availability of parks (n = 3), supermarkets (n = 2), greenspaces (n = 1), walkability (n = 1), recreational facilities (n = 1), and neighborhood deprivation (n = 1). Residing in neighborhoods with more parks was associated with greater reductions in post-intervention body mass index in two studies. Inconsistent findings relating availability of supermarkets to changes in fruit and vegetable intake were reported. Residing in neighborhoods with more recreational facilities was associated with increases in objectively measured physical activity but not with self-reported screen time. Findings among the few studies that examined neighborhood determinants of obesity management outcomes among children were inconsistent. Neighborhood resources that support physical activity (parks, recreational facilities) may be associated with better outcomes.

The role of sidewalk availability in gentrification: A longitudinal study of U.S. neighborhoods and racial/ethnic composition

ABSTRACT Urban reinvestment projects commonly embed ‘healthy‐city’ objectives like Safe Routes to School (SRTS), yet little is known about their effects in neighborhoods undergoing rapid gentrification. This mixed-methods study positions children’s active mobility as a behavioral lens on neighborhood change in Greenville, SC’s Historic West End – a once Black, low-income district now reshaped by park development and rising property speculation. Systematic counts, behavior maps, and field observation documented school-hour travel. Semi-structured interviews with 38 parents, planners, and community advocates provided depth. Contrary to SRTS expectations, logistic models show no significant link between living within one mile of A.J. Whittenberg Elementary and walking or biking, once race and built environment are considered. Black children in nearby Special Emphasis Neighborhoods were slightly less likely to use active modes than White peers commuting farther. Interviews reveal concerns about a ‘wall of cars’ at pickup, cultural fears related to traffic and surveillance, and the disappearance of traditional curbside spaces. These findings complicate SRTS health narratives: without community co-design, walking and biking infrastructure may signal displacement. The study calls for context-tailored active-mobility planning that honors local practices and positions children’s travel behavior as a barometer of social fragmentation before conventional displacement metrics emerge.

ABSTRACT This paper examines the phenomenon of foodification as a catalyst for consumption-driven urban transformation in Hazelwood, a suburban neighbourhood in Pretoria, South Africa. Hazelwood has undergone a visible shift from a quiet residential area to a vibrant culinary hub, reflecting broader global trends in urban change driven by food and consumption. This study explores how the proliferation of upscale restaurants and cafes has redefined Hazelwood’s spatial identity, creating a walkable, European-inspired enclave within a city dominated by shopping malls and chain eateries. The paper situates Hazelwood’s transformation within the context of post-apartheid urban dynamics, where neoliberal economic policies intersect with efforts to redress spatial injustices, resulting in complex patterns of social and economic change. Methodologically, this study employs content analysis, semi-structured interviews with key stakeholders and participant observation to capture the nuances of Hazelwood’s transformation. The findings highlight the cyclical relationship between consumption and the production of space, demonstrating how these processes drive rapid urban change and contribute to the commodification of urban neighbourhoods. Hazelwood’s evolution serves as a lens through which to explore the broader implications of consumption-driven urban change on social dynamics and spatial identity in South African cities.

Abstract This work demonstrates the efficiency of the software IVEN (Internal Versus External Neighbourhood) in describing the dynamic changes in neighbourhoods of all cell lineages in the mammalian blastocyst. In the mouse model, the primitive endoderm (PrE)/epiblast (Epi) dichotomy is established during blastocyst formation, which results in a seemingly random distribution of cells from both lineages within the ICM (‘salt and pepper’ model). Nevertheless, differences in cell potency, plasticity and distribution suggest that specific cell traits, such as environment, might be defining the ultimate fate acquisition. We have tested the new functionalities in the latest IVEN version and its efficiency to explore the changes in cell distribution within cell lineages and sub-populations. For this purpose, we have developed pipelines that combine functionalities from the imaging software (IMARIS) with IVEN internal algorithms to provide an insight into the dynamic cell neighbourhood within the early blastocyst. IVEN returns detailed reconstructions and numerical arrays that can be interpreted to describe the evolution of cell neighbourhoods within and between lineages. Thus, we have been able to identify specific subsets of cells within the TE and the ICM lineages depending on their relative position to the blastocyst cavity and revealed distinct neighbourhood features. IVEN analyses were essential to provide quantitative understanding of the intrinsic dynamics of the mouse blastocyst. Our approach demonstrates the accuracy of IVEN as a descriptive analytical tool and offers the possibility of applying it on to other systems to uncover differences between species.