Neighborhood Effects Research Articles

Research on neighbourhood effects shows that social and physical environments can influence individual health, yet no national-level study has explored this relationship in Italy. This study examines the association between neighbourhood characteristics and both physical and mental health across Italy, combining subjective perceptions and objective measures. This research represents an initial exploration of this phenomenon due to the lack of available evidence specific to Italy. The ITA.LI survey gathered data from 8778 participants across 4900 households residing in 278 municipalities. Individual physical and mental health, measured through the SF-12, are the outcomes considered in this study. In studying the context, reference is made to both subjective perceptions and objective measures. Multilevel analysis is implemented considering a two-level structure in which individuals are nested in neighbourhoods (census blocks). Evidence suggests the existence of neighbourhood effects, especially on mental health conditions. The subjective perception of social cohesion is associated only with mental health, while the perceived neighbourhood disorder is related to both mental and physical health. Moreover, compositional characteristics are associated with worse physical health.

Neighborhood frequency effects in late bilingual phonological neighborhoods.

Plants can have a facilitative effect on co‐occurring neighbours by moderating microclimate conditions. In forested areas, vegetation can ameliorate macroclimate extremes relative to open areas, yet the neighbourhood characteristics that contribute to this buffering effect, particularly during early stand development, remain unclear. Here, we examined how the local neighbourhood affects aboveground temperature and relative humidity in the canopy layer of 76 mapped neighbourhood plots in an 8‐year‐old experimental forest in Tasmania, Australia. We assessed which aspects of the neighbourhood – species identity, density, summed basal area and spatial arrangement – best described the microclimatic conditions over a year‐long period, and examined how seasonal and extreme climatic conditions influenced the strength of neighbourhood effects on microclimate. Microclimate conditions varied substantially across neighbourhoods, with a significant proportion explained by neighbourhood metrics (NMs). Daytime temperature and relative humidity (RH) were strongly associated with the community neighbourhood index (NI), which reflects the number, size and proximity of neighbouring trees rather than their species identity. Higher NI values – indicating more, larger or closer neighbours – led to cooler, moister microclimates, with the strongest effects observed in summer, where the NI explained 57% of the variation in temperature and 33% in RH. Although nighttime microclimate relationships with NMs were generally weaker, they were positively associated with the number and size of neighbours (summed basal area). The buffering effect of high NI and basal area neighbourhoods was most evident under extreme climatic conditions, with temperatures 3.5°C cooler on the hottest recorded day and 1.2°C warmer during the coldest. The amelioration in microclimatic conditions due to neighbouring plants has important implications for tree performance and forest regeneration, particularly in projected hotter and drier conditions.

It Takes a Village: The Economics of Parenting with Neighborhood and Peer Effects

Background:Neighborhood context is a root cause of health disparities, but few epidemiologic studies have measured the effect of neighborhoods on fertility, a reproductive outcome with known racial disparities.Methods:The Black Women’s Health Study is a prospective cohort study of US Black women who enrolled in 1995. Participants completed questionnaires at baseline and every 2 years afterwards. We linked participant addresses to US Census block group data on six variables representing neighborhood disadvantage and used factor analysis to derive a composite neighborhood disadvantage score. In 2011, participants reported how many months it took to conceive each of their planned pregnancies and whether they had ever tried to conceive for 12 or more months without success. We included 2,085 participants who contributed 2,712 pregnancy attempts during 1995–2011. We fit proportional probabilities regression models with generalized estimating equations to estimate fecundability ratios and 95% confidence intervals (CI). We stratified models by individual-level educational attainment.Results:Relative to the lowest quintile of neighborhood disadvantage, fecundability ratios for participants in quintiles 2, 3, 4, and 5 were 0.89 (95% CI = 0.79, 1.01), 0.94 (95% CI = 0.83, 1.06), 0.84 (95% CI = 0.74, 0.96), and 0.89 (95% CI = 0.79, 1.02), respectively. The association persisted only among participants with a college degree or higher.Conclusion:Higher neighborhood disadvantage was associated with reduced fecundability in this cohort of Black women, particularly among those with high educational attainment. These results support the relevance of studying place-based risk factors for subfertility and have important implications for advancing reproductive justice.

The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and Object-Based Image Analysis (OBIA) to detect and monitor individual grapevines throughout the growing season. Vines are identified directly from 3D point clouds without the need for prior training data or predefined row structures, achieving a mean Euclidean distance of 10.7 cm to the reference points. The OBIA framework segments vine vegetation based on spectral and geometric features without requiring pre-clipping or manual masking. All non-vine elements—including soil, grass, and infrastructure—are automatically excluded, and detailed canopy masks are created for each plant. Vegetation indices are computed exclusively from vine canopy objects, ensuring that soil signals and internal canopy gaps do not bias the results. This enables accurate per-vine assessment of vigour. NDRE values were calculated at three phenological stages—flowering, veraison, and harvest—and analyzed using Local Indicators of Spatial Association (LISA) to detect spatial clusters and outliers. In contrast to value-based clustering methods, LISA accounts for spatial continuity and neighborhood effects, allowing the detection of stable low-vigour zones, expanding high-vigour clusters, and early identification of isolated stressed vines. A strong correlation (R2 = 0.73) between per-vine NDRE values and actual yield demonstrates that NDRE-derived vigour reliably reflects vine productivity. The method provides a transferable, data-driven framework for site-specific vineyard management, enabling timely interventions at the individual plant level before stress propagates spatially.

Abstract Quality medical resources (QMR) play a crucial role in population health, and their spatial distribution disparities remain a significant cause of health inequities. This study constructs the quality medical resources composite index (QMRCI) using panel data from Chongqing (2015–2023), applying the Dagum Gini coefficient, kernel density estimation, Markov chain, and spatial autocorrelation methods to systematically analyze regional disparities, evolutionary trends, and spatial clustering characteristics of QMRCI, combined with overlay analysis of population density and per capita GDP. The results show that while Chongqing’s overall CQMRI allocation has improved, significant interregional disparities persist, exhibiting a gradient pattern of “core polarization - new area emergence - peripheral lag” with notable path dependence and neighborhood effects. In spatial terms, QMRCI demonstrates significant positive clustering and spatial dependence characteristics. Although developed areas concentrate QMRCI, they demonstrate mismatches with population and GDP distributions, whereas less developed areas with fewer QMRCI show better matching degrees. The study recommends enhancing medical resource supply efficiency in densely populated (developed) areas while optimizing regional coordination mechanisms to prevent low-level homogenization. It emphasizes aligning GDP growth with medical resource investment increases, particularly improving resource accessibility in peripheral (less developed) regions. To achieve this, Chongqing should establish a comprehensive dynamic balancing mechanism and targeted policy intervention system to strengthen the healthcare system’s overall resilience, ultimately realizing coordinated development of structural optimization and equity improvement.

Childhood obesity is a major risk factor for adult cardiovascular disease. Current obesity-prediction models were not developed in diverse populations and do not include heterogeneous social, environmental, and climate factors that may impact body mass index across the full pediatric spectrum. Additionally, they consider only the immediate neighborhood within which a child lives, ignoring contextual factors from expanded (i.e., distal) neighborhoods. This study uses expanded neighborhoods' social, environmental, and climate data to improve individual-level body mass index prediction-from underweight through obesity-using a novel machine learning approach. We obtained demographic and clinical data from the electronic health records of the Duke University Health System, identifying 12,226 children aged 6-18 years in Durham County, North Carolina, with body mass index data from 2014 to 2022. Participants' data were linked to socioeconomic and environmental information at the census block group level. We captured expanded neighborhood effects with a graph neural network and combined this information with individual-level factors to predict body mass index. Our model predicted body mass index more accurately than simpler models for children aged 6-11 (R2 = 0.234, mean absolute error = 3.352, root mean square error = 4.370) and 12-18 (R2 = 0.147, mean absolute error = 4.980, root mean square error = 6.343) using all features. Key predictive factors identified included rent burden, poverty rate, and tree coverage. This research highlights the value of including broader socioeconomic and environmental factors in body mass index prediction, offering insights that could guide targeted, neighborhood-level interventions.

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.

Neighboring tissues as diagnostic windows: Neighborhood effects in radiomic detection of pancreatic ductal adenocarcinoma.

ABSTRACT The neighborhood effect is a fundamental component of land use allocation, reflecting the influence of surrounding units on a central unit. Over the past decade, urban land use allocation models have increasingly relied on Cellular Automata (CA) frameworks to simulate these effects. However, the detailed and complex structure of parcel maps poses challenges when employing large neighborhood sizes in CA models. To address this issue, a new Multi-Level Parcel-based CA (MPCA) model is proposed, incorporating a hierarchical neighborhood structure at the parcel, block, and sector levels. This structure generalizes parcels at the block and sector levels while preserving detailed spatial data at the parcel level. The integrated multi-level neighborhood effect is determined in two steps: first, neighborhood effects are calculated independently using distances of 200 m, 1000 m, and the entire study area for the parcel, block, and sector levels, respectively; second, these effects are aggregated to produce a single integrated value for each parcel. For evaluation, the complete land use allocation process is implemented within a structured framework. With an overall accuracy of 0.868, a Kappa index of 0.715, and a Figure of Merit (FoM) of 0.66, the results demonstrate that incorporating multi-level neighborhoods significantly enhances allocation accuracy compared to conventional single-level models. This approach improves the ability to capture broader spatial interactions while maintaining computational efficiency, offering a more robust and realistic solution for urban land use allocation.

ABSTRACTThe adaptive calibration model (ACM) asserts that the stress response system, including the parasympathetic nervous system (PNS), conditionally adapts to one's environment. In infancy, the proximal context of parental influences (e.g., maternal depression/anxiety) affects the development of the infant stress response system. In contrast, the relation between broader contexts, such as neighborhood environment, and infant PNS development is less well understood, despite the impact neighborhoods may have on maternal mental health and youth outcomes. The present study bridges prior research examining relations between neighborhood and maternal depression, and maternal depression and infant PNS development. Latent growth curve analysis with mother–infant dyads (N = 320), assessed when infants were 4, 8, 12, 18, and 24 months old, indicated that maternal depressive symptoms showed global decreases across infancy and that infant resting RSA showed global increases, as well as variability across the sample in these trajectories. Moderated mediation modeling showed significant direct effects of neighborhood structural disadvantage on initial levels of infant resting RSA. However, there were no significant indirect effects of neighborhood through maternal depressive symptoms on RSA levels or trajectories. These findings suggest that the broader environment impacts infant stress response system development, but this may occur through other mechanisms beyond proximal maternal depressive symptoms.

Conspecific negative density dependence (CNDD) strongly influences plant demography, especially in highly diverse tropical forests, but its relative importance may vary between growth forms. We used hierarchical Bayesian models to assess how neighbor densities affected the survival of more than 30,000 seedlings from 87 tree and 56 liana species in a tropical forest across two 1-year census intervals. Although standardized CNDD coefficients were similar between lianas and trees, lianas showed a four- to sixfold stronger per capita CNDD on seedling survival compared to trees. Specifically, adding a single conspecific neighbor (2 cm dbh at 1 m distance) reduced liana survival by -1.2% to -1.8%, whereas it only reduced tree survival by 0.3%. Nevertheless, trees exhibited greater inter-annual variation in CNDD prevalence (47%-11% of species with significant CNDD) compared to lianas (13%-23%). These contrasting patterns likely reflect growth form-specific ecological strategies in density-dependent responses to inter-annual environmental fluctuations. Our findings highlight the importance of examining per capita neighborhood effects when assessing CNDD strength and suggest that lianas and trees may utilize different mechanisms driving CNDD and exhibit varying stability in their contribution to diversity maintenance in tropical forests.

This exploratory pilot study examined how Latine adolescents’ ethnic–racial identity (ERI)—specifically, centrality, private regard, and public regard—was shaped by parents’ gender role socialization (GRS) beliefs and perceptions of neighborhood connectedness and problems. Sixty Latine parent–adolescent dyads living in a Northeastern new destination context participated. Hierarchical regression models were used to test whether GRS beliefs moderated the effects of neighborhood on adolescents’ ERI. Traditional GRS beliefs moderated associations between neighborhood problems and ERI dimensions, such that adolescents whose parents endorsed stronger traditional GRS beliefs reported lower ERI centrality, private regard, and public regard in neighborhoods with more problems. These associations were not significant for neighborhood connectedness and did not differ by child gender. Findings suggest that parent beliefs about gender may shape identity development in environments perceived as risky or under-resourced. The context-dependent nature of socialization and the adaptive nature of parenting processes in emerging Latine communities are discussed.

The neighbourhood of plants in a patch can shape vulnerability of focal plants to herbivores, known as an associational effect. Associational effects of plant neighbourhoods are widely recognised. But whether a single neighbouring plant can exert an associational effect is unknown. Here, we tested if single neighbours indeed do influence the likelihood that a focal plant is visited and eaten by a mammalian herbivore. We then tested whether any refuge effect is strengthened by having more neighbours in direct proximity to a focal plant. We used native plant species and a browser/mixed feeder mammalian herbivore (swamp wallabies (Wallabia bicolor)) free-ranging in natural vegetation. We found that a single neighbouring plant did elicit associational effects. Specifically, plant pairs consisting of one high-quality seedling next to a single low-quality plant were visited and browsed by wallabies later and less than pairs of two high-quality seedlings. Having more neighbours did not strengthen these associational effects. Compared with no neighbours, one or five low-quality neighbours had the same effect in delaying time taken for wallabies to first visit a plot and browse on a high-quality focal seedling. While traditionally a ‘patch’ refers to a broad sphere-of-influence neighbouring plants have on a focal plant, our findings suggest the influence of plant neighbours can range from the nearest individual neighbour to the entire plant neighbourhood. Such fine-scale associational effects are fundamentally important for understanding intricate plant-herbivore interactions, and ecologically important by potentially having knock-on effects on plant survival, in turn influencing plant community structure.

Since 2000, the effect of neighbourhoods on the health of people has emerged as a pertinent domain of research in public health. Nevertheless, to comprehend neighbourhood’s influence on health, it becomes imperative to understand whether these impacts are heterogeneous among varying sociodemographic attributes such as gender. Since gender roles are often influenced by the residential environment’s characteristics, especially for women. Thus, this article provides a spatial analysis examining the impact of neighbourhood on women’s health through a systematic review of literature. Overall, 59 studies were reviewed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From these studies, it can be contended that improved physical and social characteristics of the neighbourhoods lead to good physical and mental health status among women in that area. However, the review suggests that most of the studies focusing on neighbourhood and health of women are concentrated in the USA, while very few are in developing countries and none in India. Also, the studies have not prioritised women specifically in the reproductive health group, which is an important sub-group of the population, suggesting it to be a potential area for future research.

Dengue fever is transmitted to humans through bites of Aedes mosquito vectors. Therefore, controlling the Aedes population can decrease the incidence and block transmission of dengue fever and other diseases transmitted by these mosquito species. In many countries, gravitraps are used to monitor mosquito vector densities, but this approach usually underestimates the population of Aedes mosquitoes. Moreover, literature on the spatio-temporal dynamics of Aedes populations in a single city is limited. For example, in Kaohsiung of Taiwan, population densities vary substantially between villages, and the city government has relatively limited resources to deploy gravitraps. Therefore, a well-defined index should be developed to reflect the spatial-temporal dynamics of adult Aedes mosquitoes in urban environments. This would allow reduction of sources and removal of vector habitats under various situations. An artificial intelligence (AI) surveillance based on an auto-Markov model with a non-parametric permutation test is proposed. The auto-Markov model takes neighborhood effects into consideration, and can therefore adjust spatial-temporal risks dynamically in various seasons and environmental background. Information from neighboring villages is incorporated into the model to enhance precision of risk prediction. The proposed AI gravitrap index integrates the auto-Markov and disease mapping models to enhance sensitivity in risk prediction for Aedes densities. Simulation studies and cross-validation analysis indicated that the AI index could be more efficient than traditional indices in assessing risk levels. This means that using the AI index could also reduce allocation cost for gravitraps. Moreover, since the auto-Markov model accommodates spatial-temporal dependence, a risk map by the AI index could reflect spatial-temporal dynamics for Aedes densities more accurate. The AI gravitrap index can dynamically update risk levels by the auto-Markov model with an unsupervised permutation test. The proposed index thus has flexibility to apply in various cities with different environmental background and weather conditions. Furthermore, a risk map by the AI index could provide guidance for policymakers to prevent dengue epidemics.

In order to alleviate the problems of uneven distribution of educational resources and limited social mobility in the Tibet region, the state has implemented the "Inland Tibet Class" policy since 1984, aiming to promote the social mobility and cultural adaptation of Tibetan students through access to quality educational opportunities. Drawing on the theories of cultural practice and social mobility, and in combination with the research method of case ethnography, this paper delves into the neighborhood effects, educational tracking, cultural tensions, as well as the mobility opportunities emerging during the implementation of this policy. The research finds that although the "Inland Tibet Class" provides a pathway for some students to cross social strata, it also brings structural problems such as identity conflict, cultural adaptation pressure, and further weakening of local educational resources. Educational mobility is not only a process of input and output of resources, but also a complex mechanism of cultural negotiation, social relationship reconstruction, and institutional transformation. This paper contends that, in order to promote educational equity, it is essential to begin with respecting the subjective experiences of students, attach importance to the interaction between local culture and the education system, and promote the design of more inclusive educational policies.

The effects of tree diversity and neighborhood on phyllosphere fungal communities

How mobility-based exposure measures may mitigate the underestimation of the association between green space exposures and health.