Socioeconomic Segregation Research Articles

Examining trends in school segregation in compulsory and upper secondary education in Sweden: the showcase of an urban school market

ABSTRACT Educational success is central to the life chances of young people, but increasing differences in student achievement between schools are commonly attributed to increasing school segregation. The aim of the study was to examine the trends in socioeconomic, ethnic, and academic segregation in compulsory and upper secondary education in the Gothenburg school market between 1998 and 2020. Utilizing register data from the Gothenburg Longitudinal Database (n = 1 662 166), the study showed that school segregation intensified over time and across education levels, particularly in the Gothenburg municipality and upper secondary education. The geography of upper secondary education becomes more clustered after stricter upper secondary qualifications are implemented. These findings are discussed in relation to the Swedish school reforms that resulted in the materialization and expansion of the school market with a significant increase in school provisions, leading to the (re-)production of unjust geographies.

Segregated by whom? Examining triple neighbourhood disadvantages and activity spaces in Seoul, South Korea

This study explores the potential mechanisms by which neighbourhood isolation is created and reinforced in non-home activity locations. The existing discussion on neighbourhood isolation, its consequences, and the subsequent reproduction of interactions between individuals and their environment has focused on their residential locations. However, the vitality of a neighbourhood depends not only on its own socioeconomic conditions but also on the conditions of the other neighbourhoods its residents visit and are visited by. Residential locations are only one of the many spaces in which individuals engage in their activities; therefore, to fully comprehend people’s everyday experiences of integration and isolation, it is imperative to look at interactions within both home and non-home activity spaces. Building on this extended perspective, we examine: (1) whether residential neighbourhood isolation is intensified by socioeconomic conditions individuals’ encounter in consumption and commuting activity spaces; and (2) which socioeconomic group drives the association between residential neighbourhood isolation and activity spaces—whether both disadvantaged and privileged groups contribute to isolation through the differential use of urban space or either group predominantly leads this association. We use Seoul as a case study, characterised as a city that is highly populated, contains multiple economic centres, functions with a highly affordable and efficient public transportation system, and suffers from increasing socioeconomic segregation. The results suggest that mobility patterns in activity spaces tend to reinforce neighbourhood isolation through potential forces driven by neighbourhood homophily. Isolation in the use of urban spaces appears to be driven mainly by the self-seclusion of the most advantaged neighbourhood group, while neighbourhoods in the lower socioeconomic status groups indicate broader cross-interactions across status groups, probably because of Seoul’s transit-friendly environment. Our study contributes to enhancing the understanding of spatial-societal inequalities in Korea and beyond and could assist in formulating policies for a more balanced provision of diverse urban social services and infrastructure.

From walking to staying: The impact of spatial qualities on socio-spatial segregation in innovation districts

Innovation districts can spur urban economic growth but present challenges like socio-spatial segregation. To mitigate segregation, we emphasize the importance of “staying activities” such as sitting, playing, and relaxing, over transient activities like walking or cycling in the design of public spaces. Focused on One-north, an innovation district in Singapore, we examine how different spatial qualities influence the “staying activities” of diverse socioeconomic groups. Using a GeoAI workflow that integrates various data-sources with machine learning and AI, we assess thirteen spatial quality indicators and their impact on staying activities. Our deep neural network (DNN) model, which achieves an 83.74% accuracy rate, confirms the effectiveness of these indicators in capturing the co-location of staying activities between diverse income groups in One-north. Our findings reveal that typical markers of well-designed public spaces, such as visual diversity and greenery, do not consistently enhance staying activities. Their effectiveness varies across One-north and is shaped by the unique functional contexts and land-use types of each sector in the district. Our research thus highlights the necessity for context-specific designs that encourage meaningful social interactions and reduce socioeconomic segregation. Our proposed GeoAI workflow can assist in the proactive evaluation of design strategies, supporting decision-making prior to major construction projects.

Relationship between body mass index and residential segregation in large cities of Latin America

BackgroundObesity is a global health problem, and its connection with social and environmental factors is well-established. Social factors, such as urban segregation, may impact obesity through various mechanisms, including food and physical activity environments, as well as social norms and networks. This multilevel study aims to examine the effect of socio-economic residential segregation of Latin American cities on the obesity of individuals within those cities.MethodsWe analyzed data from national surveys for a total of 59,340 individuals of 18–70 years of age, conducted in 156 cities across Brazil, Chile, Colombia, and Mexico between 2007 and 2013. We adjusted two-level linear mixed models for body mass index (BMI) stratified by sex and country, controlling for age, educational level and poverty. Separate models were built for dissimilarity and isolation segregation indices.ResultsThe relationships between segregation indices and BMI were mostly not statistically significant, and in some cases, they were opposite to what was expected. The only significant relationships were observed in Colombian men, using the dissimilarity index (-7.5 [95% CI: -14.4, -0.5]) and in Colombian women, using the isolation index (-7.9 [95% CI: -14.1, -1.7]).ConclusionsWhile individual-level factors cannot fully explain differences among people in the same city, segregation indices may help. However, we found that in some cases, the relationship between BMI and segregation indices is opposite to what is expected based on prior literature. This should be considered in examining the phenomenon. Further research on obesogenic environments in segregated neighborhoods could provide valuable evidence.

An agent-based model of school tracking, accountability, and segregation

ABSTRACT Extant research on the effect of education system characteristics on school socio-economic segregation does not consider education as a complex system. This paper’s contribution lies in using agent-based modelling to simulate the effect of the interaction between families’ strategies for school selection and two education system characteristics: tracking – a system where students of different academic abilities are separated in different schools – and school accountability – the public availability of information on school quality. The model shows that school tracking and accountability tend to at the same time attenuate and increase school socio-economic segregation, but overall both policies tend to exacerbate segregation by eliciting competition for the best schools. The policy implications are: (i) tracking has a stronger exacerbating effect on segregation than accountability, (ii) the two polices interact to create compounding effects and (iii) by reducing residential segregation between families the segregating effect on schools of the two policies diminishes dramatically.

Built Environment, Natural Environment, and Mental Health.

Mental health disorders have become a global problem, garnering considerable attention. However, the root causes of deteriorating mental health remain poorly understood, with existing literature predominantly concentrating on socioeconomic conditions and psychological factors. This study uses multi-linear and geographically weighted regressions (GWR) to examine the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that job sprawl and land mixed use are highly correlated with a lower risk of depression. Additionally, the presence of green spaces, especially in urban area, is associated with improved mental health. Conversely, higher concentrations of air pollutants, such as PM2.5 and CO, along with increased precipitation, are linked to elevated depression rates. When considering spatial correlation through GWR, the impact of population density and social capital on mental health displays substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals nuanced determinants. In northwestern counties, depression rates are more influenced by factors like precipitation and socioeconomic conditions, including unemployment and income segregation. In southeastern counties, population demographic characteristics, particularly racial composition, are associated with high depression prevalence, followed by built environment factors. Interestingly, job growth and crime rates only emerge as significant factors in the context of high depression risks in southeastern counties. This study underscores the robust linkages and spatial variations between built and natural environments and mental health, emphasizing the need for effective depression treatment to incorporate these multifaceted factors.

Explaining the spatial segregation of ethnic groups in an early industrial city: the case of Vyborg

Abstract An early industrial town’s spatial segregation is studied using empirical data concerning the Russian population of the town of Vyborg. Several hypotheses for explaining segregation are considered using spatial analysis. The spatial data are derived from historical maps and demographic data from various tax records. Socioeconomic segregation is studied as a possible cause of ethnic segregation. The main drivers of spatial segregation were the explicit policies of segregation enforced by both the Russian military administration and the town’s civilian administration. While the effects of segregation gradually diminished due to social diffusion, the impact of policy decisions driving segregation in the 18th and early 19th centuries was still visible in the population’s later 19th-century segregation. Yet neither the different preferences of Russians and others nor the income differences between areas explains the distribution of Russians. Segregation based on the membership of a guild was insignificant, with a few exceptions. Other factors such as discrimination, prejudice, and differences in housing market information probably contributed to segregation, but they cannot be studied with the data used.

Neighborhood-level factors associated with COVID-19 vaccination rates: a case study in Chicago

IntroductionChicago’s deeply-rooted racial and socioeconomic residential segregation is a pattern mirrored in other major cities, making it a prototype for studying the uptake of public health interventions across the US. Residential segregation is related to availability of primary care, sense of community, and trust in the healthcare system, components which are essential in the response to crises like Covid-19 in which vaccine rollout was primarily community-based. We aimed to evaluate the association between rates of access to primary care and community-belonging with Covid-19 vaccination within Chicago’s neighborhoods.MethodsData from Chicago Department of Public Health (12/2020-6/2022) on Covid-19 vaccination rates, race/ethnicity (% Black and % Hispanic/Latinx residents), age (% >65), gender (% female), socioeconomic status (% below the federal poverty line), access to needed care rate, and rate of self-reported sense of community-belonging on the neighborhood level were analyzed. Linear mixed models (LMMs) were used to study the impact of variables on vaccination; each neighborhood was added as a random effect to account for with-community association.ResultsThe average Covid-19 vaccination rates across Chicago’s neighborhoods was 79%, ranging from 37 to 100%, with median 81%. We found that Covid-19 vaccination rates were positively correlated with access to needed care (p < 0.001) and community-belonging (p < 0.001). Community areas that had lower vaccination rates had greater percentage of Black residents (p < 0.0001) and greater poverty rates (p < 0.0001). After adjusting for poverty, race, gender and age in the models, the association between vaccination rates and access to care or community-belonging were no longer significant, but % Black residents and poverty remained significant.ConclusionsThough access to needed primary care and community-belonging are correlated with vaccination rates, this association was not significant when controlling for demographic factors. The association between poverty, race and vaccination status remained significant, indicating that socioeconomic and racial disparities across Chicago drive Covid-19 vaccine recommendation adherence regardless of care access. Understanding how poverty, and its intersectional relation to race and primary care access, affects vaccination should be a priority for public health efforts broadly.

Abstract 2153: The impact of socioeconomic and racial segregation on cancer stage at diagnosis, treatment and mortality among patients with small cell lung cancer

Abstract Background: Although studies have revealed the adverse impact of residential segregation on early detection, treatment, and survival of non-small cell lung cancer―a predominant type of lung cancers, it remains unknown regarding the role of residential segregation in treatment and outcomes of small cell lung cancer (SCLC)―a rare type of lung cancer characterized by rapid growth and poor prognosis. Therefore, we examined the associations of racialized socioeconomic segregation with risks of extensive-stage diagnosis, non-adherence to guideline-recommended treatment, and mortality among non-Hispanic White (NHW) and non-Hispanic Black (NHB) patients with SCLC. Methods: This population-based retrospective cohort study included patients who were diagnosed with SCLC as a first primary malignancy at age 20 and older between January 2007 and December 2015, followed through December 2016, and identified from the Surveillance, Epidemiology, and End Results. County-level racialized socioeconomic segregation was measured using the Index of Concentration at the Extremes (ICE). Multilevel logistic regression accounting for county-level clustering was used to estimate odds ratios (ORs) for extensive-stage diagnosis and non-adherence to guideline-recommended treatment. Hazard ratios (HRs) for lung cancer-specific mortality and all-cause mortality were computed using multilevel Cox regression. Results: Among 38,393 patients, 90.1% were NHW and 9.9% were NHB. Patients dwelling in the counties with the highest concentration of low-income NHB households (highest ICE quartile) had higher risks of non-adherence to guideline-recommended treatment (OR=1.23, 95% CI: 1.08-1.40; Ptrend &amp;lt;0.01), lung cancer-specific mortality (HR=1.08, 95% CI: 1.04-1.12; Ptrend &amp;lt;0.01), and all-cause mortality (HR=1.13, 95% CI: 1.09-1.17; Ptrend &amp;lt;0.0001) as compared with patients dwelling in the counties with the highest concentration of high-income NHW households (lowest ICE quartile). These associations were comparable between NHB and NHW patients. Racialized economic segregation was not significantly associated with extensive-stage diagnosis. Conclusions: Residing in the segregated, low-income NHB counties had adverse impacts on stage-appropriate treatment for SCLC and survival outcomes. Future research on the pathway(s) linking residential segregation to SCLC treatment underutilization and poor outcomes is warranted. Citation Format: Bayu B. Bekele, Min Lian, Ying Liu. The impact of socioeconomic and racial segregation on cancer stage at diagnosis, treatment and mortality among patients with small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2153.

Social inequality and residential segregation trends in Spanish global cities. A comparative analysis of Madrid, Barcelona, and Valencia (2001-2021)

Over the last three decades, the most populated Spanish cities have experienced significant spatial, social, and economic changes. The new urban economies have played an essential role in the acceleration of such transformations, entailing a range of both positive and negative impacts at the spatial, social, economic, and environmental levels. This paper presents a quantitative analysis of how the advancement of the globalization and deindustrialization processes has encouraged social polarization in the cities of Madrid, Barcelona, and Valencia, as well as a significant increase in intra-urban socioeconomic residential segregation. The article concludes by arguing that the increase in levels of social inequality and residential segregation reflects the trend towards polarized urban models, which reproduce in urban space the differences observed in the social structure.

Social Disconnectedness, Perceived Loneliness, and Cognitive Functioning: The Role of Neighborhood Environment.

Social disconnectedness and loneliness pose significant challenges for older Chinese immigrants. Yet, it remains unclear whether they are associated with an increased risk of cognitive decline in this population. This study aimed to investigate the association of social disconnectedness and loneliness with cognitive functioning and examine the moderation role of neighborhood contexts. This longitudinal analysis examined a sample of individuals aged 60 years and older from the Population Study of Chinese Elderly in Chicago (N = 2,044). Global cognition was assessed using the averaged z-scores of cognitive performance tests. Social disconnectedness was constructed using 5 indicators about structural aspects of social relationships. Loneliness was assessed with the R-UCLA loneliness scale. Neighborhood socioeconomic status (NSES) and neighborhood segregation index were constructed using 2010-2014 American Community Survey data at the census tract level. Individual perceptions about neighborhood environments were used to construct neighborhood cohesion index and neighborhood disorder index (NDI). Latent growth curve models with adjusted cluster robust standard errors were estimated. More loneliness was associated with a higher level of initial cognitive functioning (B = 0.030, p < .01), but also with a faster decline rate over time (B = -0.007, p < .01) after adjusting for covariates. High NSES and less neighborhood segregation buffered the negative effects of loneliness on cognitive decline, respectively. High NDI amplified the positive relationship between loneliness and initial functioning, but accelerated the rate of cognitive decline associated with loneliness. The study revealed that perceived loneliness, but not social disconnectedness, is a risk factor for cognitive decline among older Chinese immigrants. Living in a neighborhood with low socioeconomic status, more segregation, and high disorder elevated the detrimental effect of loneliness on long-term cognitive decline. Further research needs to investigate the complex interplay between social relationships, neighborhood environment, and cognition.

Is socioeconomic residential segregation associated with sleep problems? Insights from ELSA-Brasil

Several factors influence sleep, which is essential for health. While the role of neighborhood socioeconomic context on sleep health has been studied in recent years, results are inconsistent. The study aimed to investigate the association between socioeconomic residential segregation and sleep problems, using data from the second evaluation (2012-2014) of 9,918 public servants participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Socioeconomic residential segregation was assessed using the Getis-Ord Local Gi* statistic. Sleep duration and deprivation, complaints of insomnia, and daytime sleepiness were obtained through interviews. Binomial and multinomial logistic regression models were used to estimate the odds ratio (OR). Regarding sleep, 49% had short duration and 3% long duration, 23% reported complaints of insomnia, 45% sleep deprivation, 42% daytime sleepiness, and 48% reported ≥ 2 sleep problems. In the model adjusted for demographic and socioeconomic variables, there was an association between high socioeconomic residential segregation and short sleep duration (OR = 1.22; 95%CI: 1.07; 1.40), sleep deprivation (OR = 1.20; 95%CI: 1.05; 1.37), daytime sleepiness (OR = 1.17; 95%CI: 1.03; 1.34) and ≥ 2 associated sleep problems (OR = 1.24; 95%CI: 1.08; 1.41). Individuals living in neighborhoods with high socioeconomic residential segregation are more likely to have short sleep duration, sleep deprivation, daytime sleepiness, and ≥ 2 associated sleep problems. This information reinforces that public policy measures to reduce socioeconomic inequalities can improve the population's sleep health.

Regionalisation process and the pattern of socio-residential specialisation between the crisis of the productive and redistributive spheres in the regional assemblage of Milan

The Milanese urban region can be understood as a trans-scalar assemblage, composed not only of the diverse territories within it and the materialities and individuals present there. It is also shaped by regulatory frameworks, such as administrative boundaries, resource allocation choices, the effects of the 2008 crisis on the population’s income, and the related spending review reforms. This paper explores the mechanisms and patterns of unequal accumulation of capital, both private and public, driven by the process of socio-residential specialisation taking place in the Milanese assemblage at the intersection between the neighbourhood effects generated by the overlap between regionalised residential geographies and the stability of municipal boundaries and the advent of the 2008 economic crisis, which acted on both productive and redistributive spheres. What emerges is a process of progressive socio-economic specialisation and segregation of the territories that make up the urban region.

Bodies Holding up Communities Uncaring Infrastructures in Santiago, Chile and Beyond

In this article, we explore the role of bodies, housing, and mobility as infrastructures of care for low-income women living in peripheral neighbourhoods in Santiago, Chile. Drawing on feminist political economists and urbanists, we describe the way that bodies act as infrastructures, often compensating for inadequate built and social environments. Even as the caring of these women sustains life, livelihoods, and communities, they suff er slow infrastructural violence ampli fied by immobility, isolation, and insuffi cient support. This reinforces and occurs within a broader context of gendered inequality and gendered violence, in a city where socioeconomic segregation is very pronounced. While there are geographic particularities to this case, the lack of infrastructure for care persists in cities and communities across the Global North and South. We provide policy recommendations oriented toward transforming material and social urban infrastructures, simultaneously addressing gendered and intersectional power relations.

Walkability assessment: a comparative study between two neighborhoods in João Pessoa - PB

The urban design can influence human behavior. This influence can be manifested through walkability, an attribute of the urban environment capable of encouraging active transport and physical activity, and which has been presented as an intelligent alternative to make cities more sustainable and healthier. In view of this, this study has the general objective of evaluating the walkability of João Pessoa - PB from a sample formed by two neighborhoods of the city, Miramar and São José, which have different morphological characteristics. Urban sections in the neighborhoods were selected using the Proportional Stratified Sampling (PSS) method. The evaluation was then carried out in the study areas, using the walkability index iCam 2.0, a tool developed by the Institute for Transport and Development Policies (ITDP Brazil). The results reveal that walkability is insufficient in several sections of both neighborhoods, but the results of the São José neighborhood show worse conditions of mobility, pedestrian infrastructure, safety, and attractiveness, suggesting a socioeconomic segregation in relation to urban planning. The results of this research can contribute to the construction of the literature concerning on the subject and to the understanding of aspects of urban design that favor walkability in Brazilian cities.

The Power of Diversity: Class, Networks and Attitudes Towards Inequality

We examine how inter-class relationships shape attitudes towards inequality. Our theoretical frame defines attitudes towards inequality as ethical dispositions and hypothesises that class-based network diversity may favour the development of what has been termed a ‘reflexive habitus’ that likely pushes for more egalitarian dispositions. The focus of our enquiry is on Chile – one of the most unequal countries in the world. We use large-scale representative survey data collected in 2016 (N = 2927). Results of our regression analyses indicate that class diversity in social networks strongly increases awareness of inequality and more egalitarian preferences while reducing belief in meritocracy and the perception that the current distribution is fair. Additional analyses suggest that network diversity influences views on inequality to a similar degree among individuals from different social classes, except for meritocratic beliefs, where network diversity especially affects the upper class. We conclude by discussing the importance of reducing socio-economic segregation.

Socioeconomic reorganization of communication and mobility networks in response to external shocks

Socioeconomic segregation patterns in networks usually evolve gradually, yet they can change abruptly in response to external shocks. The recent COVID-19 pandemic and the subsequent government policies induced several interruptions in societies, potentially disadvantaging the socioeconomically most vulnerable groups. Using large-scale digital behavioral observations as a natural laboratory, here we analyze how lockdown interventions lead to the reorganization of socioeconomic segregation patterns simultaneously in communication and mobility networks in Sierra Leone. We find that while segregation in mobility clearly increased during lockdown, the social communication network reorganized into a less segregated configuration as compared to reference periods. Moreover, due to differences in adaption capacities, the effects of lockdown policies varied across socioeconomic groups, leading to different or even opposite segregation patterns between the lower and higher socioeconomic classes. Such secondary effects of interventions need to be considered for better and more equitable policies.

Human mobility networks reveal increased segregation in large cities

A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1–6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city’s hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.

Mobility and transit segregation in urban spaces

Segregation is a highly nuanced concept that researchers have worked to define and measure over the past several decades. Conventional approaches tend to estimate segregation based on residential patterns. However, the residential dimension does not fully comprise individuals’ interactions with their environment and, consequently, can misrepresent individuals’ lived experiences. To address this gap, we analyse how segregation extends to other dimensions of the urban life. We accomplish this by using the Index of Concentration at the Extremes (ICE) to measure socioeconomic segregation at amenities and on public transit lines. Moreover, we consider the pivotal role that transport plays in democratising access to opportunities. Using transport networks, amenity visitations, and census data, we leverage agent-based models to approximate socioeconomic composition at amenities and on transit lines. Consequently, we can estimate socioeconomic segregation within the United States, for various aspects of urban life. We find that neighbourhoods that are segregated in the residential domain tend to exhibit similar levels of segregation in amenity visitation patterns and transit usage, albeit to a lesser extent. Moreover, we discover that low-income neighbourhoods experience a greater decrease from residential to amenity segregation, than their high-income segregated counterparts, highlighting how mobility can be used as a tool for overcoming residential inequalities, given the proper infrastructure. We identify inequalities embedded into transit service, which impose constraints on residents from segregated areas, limiting the neighbourhoods that they can access within an hour to areas that are similarly disadvantaged. By exploring socioeconomic segregation from a transit perspective, we underscore the importance of conceptualising experiential segregation, while also highlighting how transport systems can contribute to a cycle of disadvantage.

Mapping high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy

PurposeDespite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development. MethodsWe performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters. ResultsThe most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters. ConclusionsResults provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.