Neighborhood Location Research Articles

Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan

While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly.

ASSESSING THE IMPACT OF INEQUALITIES ON RESIDENTS' PERCEPTION AND CRIME INCIDENTS IN URBAN AND PERI-URBAN NEIGHBOURHOODS

The COVID-19 pandemic has brought the issue of inequality to the forefront, exacerbating economic uncertainty and social injustice, which has led to widespread social disorder. This study examines the relationship between inequality and crime in urban and peri-urban neighborhoods in Penang; Moreover, an association between inequality and the incidence of crime will be investigated in two neighborhoods. Having considered the intricate relationship between social inequality and crime in previous studies, a quantitative approach was conducted to investigate how neighborhood locations impact the crime and inequality relationship. A face-to-face survey using a closed-ended questionnaire was conducted with 761 respondents, comprising 329 from Balik Pulau and 432 from Bayan Baru. The findings reveal that economic inequality significantly impacts crime rates in both Bayan Lepas and Balik Pulau, whereas social inequality only shows a negative significant effect in Bayan Lepas. Subsequently, results indicate income does not influence the fear of crime among respondents in Balik Pulau, but residents with higher income levels in Bayan Lepas exhibit a greater fear of crime. However, neighborhood locations do not distinctly affect criminal activity. These insights can help enhance crime prevention strategies, improve security, and foster balanced development in Penang.

Shelter in place: neighborhood policing of homelessness in Montreal, Canada

ABSTRACT Montreal police officers are increasingly called on to respond to visible homelessness. While previous research has focused on strategies of containment or banishment (where individuals experiencing homelessness are removed from certain areas of the city to more homelessness resource–rich areas by the police or expelled entirely), this study presents evidence of a different strategy: that of maintaining individuals within a given neighborhood. Drawing on 29 semi-structured interviews conducted with officers from the Service de Police de la Ville de Montréal (City of Montreal Police Service or SPVM) about the nature of their work with homeless individuals, a neighborhood-level analysis of these interventions found that officers often saw the neighborhood location of a homeless individual as key. The findings revealed officers bonding and creating alliances with homeless individuals, the presence of neighborhood-specific homelessness resources as central to their interventions, and a concern around the migration of homeless individuals from one area of the city to another. In addressing the role of police officers in frontline interventions with individuals experiencing homelessness, a consideration of the centrality of the precinct neighborhood is essential.

Analysing Inequity in Accessibility to Services with Neighbourhood Location and Socio‐Economic Characteristics in Delhi

The lack of comprehensive spatial data for neighbourhoods in cities in the global South has posed a significant challenge for examining socio‐economic inequities in accessibility to services. By combining the primary (survey data) and secondary data sources with new spatial data sources (Earth observation data, Google Maps), we create a spatial database of 4,145 residential locations in Delhi, aggregating them into 1 km grid‐shaped neighbourhoods. The neighbourhood's economic status is evaluated using a composite index of the built environment, land price, and household income. Social characteristics are examined through the percentage of the scheduled caste (SC) population, considering their historical marginalization in Indian society. Using the E‐2SFCA method, we calculate accessibility to four key services and employ the geographically weighted regression (GWR) model to explore inequities in accessibility based on neighbourhood location and socio‐economic characteristics. Findings reveal inequity in accessibility to services at the neighbourhood level is primarily driven by spatial location rather than income or percentage of SC population. Moreover, the influence of socio‐economic characteristics on accessibility varies across locations. The spatial data mapping approach employed in this article can be applied to numerous rapidly urbanizing cities in the global South lacking block or neighbourhood‐level spatial data.

Development of a methodology for the evaluation of environmental equity at urban level with high spatial resolution. Case study in Valencia (Spain)

Detecting vulnerable areas or areas with less favourable conditions than the rest of the city is essential for public administrations to achieve sustainable development. This paper describes the development of a methodology to obtain the most vulnerable neighbourhoods at an urban scale based on a series of geo-referenced economic, social and environmental variables. In addition, a network of NO2 passive sensors has been deployed to introduce air quality as an environmental variable in the developed methodology. This allows us to identify the neighbourhoods with the worst air quality to determine the areas in the city where vulnerable groups and poor air quality conditions coincide. Finally, results are analysed to see if there is a direct relationship between vulnerable neighbourhoods and neighbourhoods with poorer air quality.Then, the developed methodology has been applied in Valencia (Spain) as a case study. Thus, the methodology developed has been allowed to identify the most vulnerable neighbourhoods based on facilities, demography and socioeconomic variables. This would allow public decision-makers to use available resources to reduce vulnerability efficiency. Finally, cross-referencing vulnerability and air quality results give the location of vulnerable neighbourhoods with the worst air quality, allowing the identification of environmental inequities at an urban level with high spatial resolution. In the pilot case of Valencia, it is concluded that there is no direct relationship between vulnerable neighbourhoods and neighbourhoods with high NO2 levels, although 14.28% meet both conditions.

Modelling Residential Housing Rent Price Using Machine Learning Models

Background: Providing shelter and security through housing is a basic need for people. However, the housing market is frequently troubled by issues such as exorbitant rent pricing and a lack of transparency, posing challenges for both landlords and home renters. Objective: This study examines the factors affecting residential housing rent in Ibadan City to thoroughly investigate the cost-effectiveness of rents. We looked into a number of variables, including neighborhood characteristics, property size, amenities, and location, aiming to pinpoint the essential elements significantly impacting rental costs. Methods: Data were collected from two well-known real estate websites, privateproperty.com and propertypro.com. We built a rent prediction model and determined key features using regression analysis utilizing Ordinary Least Squares (OLS), and the Extreme Gradient Boosting (XGBoost) algorithm. The analysis revealed several factors with notable relationships to rent prices. Results: The five variables with the highest correlation coefficients were identified as duplex (0.54), toilet (0.34), detached (0.31), flat (-0.3), and bedroom (0.29), indicating their substantial influence on housing rental rates. Furthermore, the study highlights specific neighborhoods as either relatively expensive or budget-friendly in terms of median rent prices. Notably, Agodi and Kolapo Ishola emerged as the most expensive areas, surpassing the overall median rent by significant margins (454.55% and 450%, respectively). Conversely, Sanyo, Moniya, and Academy recorded the lowest median rent prices, being 50% lower than the overall median. Conclusion: This study provides valuable insights into the intricate factors governing residential housing rent in Ibadan City, offering a foundation for further research and practical applications in the real estate domain.

Spatial Patterns of Under-Resourced Neighbourhood Settlements in Sidenrengrappang Regency

Under-resourced neighbourhood settlements in Sidenreng Rappang Regency, which are growing, need to get the proper treatment, so that its scope does not expand so that it impacts the decline in the quality of the community’s living environment. The direction of development is identified as early as possible. This is seen from the spatial pattern. This study describes and explains the distribution pattern and spatial correlation of Under-resourced neighbourhood settlements in Sidenreng Rappang Regency. The spatial distribution uses the 2014 and 2020 Under-resourced neighbourhood location map overlay method with administrative maps and adds the attribute data on the percentage of Under-resourced neighbourhood Heads of Families (KK) while the spatial autocorrelation uses Moran’s Index, Moran’s scatterplot and Local Indicator Spatial Autocorrelation (LISA). The results showed that Under-resourced neighbourhood settlements in 2014 were concentrated in the city center while in 2020 they expanded to the west, north, east, and south of Sidenreng Rappang Regency. The results of the Moran Index and Moran’s Scatterplot testing show that there is no difference in the spatial distribution pattern of the percentage of Under-resourced neighbourhood households in 2014 and 2020 where there appears to be a spatial grouping (cluster). The results of the LISA analysis show that 13 villages have spatial autocorrelation. This indicates that the handling of Under-resourced neighbourhood settlements by the central and regional governments until 2019 has no impact on reducing the area and percentage of KK Under-resourced neighbourhoods, but instead grows new Under-resourced neighbourhoods and expands to other areas.

Influential Spreaders Identification by Fusing Network Topology

With the development of network science, complex network analysis has received extensive attention. In recent years, influential spreaders identification has become a hot topic in the research of complex networks. In general, influential spreader identification algorithms are mainly divided into centrality-based algorithms and topology-based algorithms. However, centrality-based algorithms have to face the information limitation problem that leads to low accuracy for identifying influential spreaders. Topology-based algorithms have both structural and positional limitation problems that lead to low accuracy for identifying peripheral influential spreaders. In this paper, we focus on improving the situation and propose two influential spreader identification algorithms, NSC (neighborhood structure centrality) and NPC (neighborhood position centrality) from both the perspective of the centrality and the network topology. NSC algorithm collects various types of network structure information through structure embedding and clustering, so as to solve missing network structure information problem. NPC algorithm calculates neighborhood location information by improving the k-shell algorithm to tackle location limitation problem. Experimental results with fourteen baseline algorithms show that our proposed algorithms NSC and NPC can achieve higher accuracy.

Dissecting probable indicators of juvenile delinquency among Gweru Urban adolescents in Zimbabwe

Society today is still naïve that some children are no longer innocent, beautiful souls but callous and dangerous to the community. The escalating crime incidents by juveniles make it imperative to concede the rampant prevalence of juvenile delinquency. This research sought to identify the probable indicators of juvenile delinquency in the milieu of the unprecedented global trend. The study used a mixed-method approach guided by the pragmatist research paradigm. A descriptive research design enhanced an appreciation of participants’ multiple realities based on their lived experiences premised on qualitative and quantitative research. Random sampling was used in selecting 209 students to complete a translated questionnaire. An interview was used to collect data from teachers and parents selected purposively. Data was analysed using frequency count. Data from interviews was thematically analysed. Significant findings were that mental illness, social conditions such as neighbourhood location, home conditions such as family size, school conditions such as strictness of school and environmental factors were the indicators of juvenile delinquency. After-school programs to increase school time counselling services at schools were some of the recommendations by key informants on curbing juvenile delinquency. The overall conclusion is that probable indicators of juvenile delinquency result from several factors such as individual, home, social environment, and school.

Neighborhood location and nutritional resources as a risk factor for congenital heart disease in the fetus.

Congenital heart disease (CHD) is the most common birth defect, influenced by maternal health, environmental conditions, and genetics. Maternal health and nutrition, particularly maternal diabetes, is a modifiable risk factor for development of CHD in the fetus. However, the importance of food access during pregnancy on the development of CHD remains unknown. The objective of this study was to investigate the association between maternal neighborhood characteristics, particularly food access, and occurrence of prenatally diagnosed CHD. A retrospective case series studied maternal-fetal dyads with prenatally diagnosed CHD between 2019 and 2021 in Washington, DC. Moran's I of maternal addresses evaluated geographic clustering of disease. Negative binomial regression assessed association between census tract demographics and population-adjusted CHD rate. A total of 307 dyads were analyzed. Global Moran's I showed significant CHD clustering (p-value = .004). However, degree of clustering was not clinically meaningful. After adjusting for neighborhood socioeconomic status, residing in food deserts was not a predictor for CHD. However, neighborhoods with a higher percentage of households receiving Supplemental Nutrition Assistance Program (SNAP) benefits were associated with higher rates of conotruncal heart defects (Incident Rate Ratio [IRR] = 1.04, CI = 1.01-1.08) and aggregate CHD (IRR = 1.03, CI = 1.01-1.05). Neighborhood location and food access were not associated with CHD. However, increased enrollment in SNAP was associated with higher rates of CHD. The association between CHD and SNAP benefits warrants further exploration. Understanding food access and maternal nutrition may illuminate disparities in the burden of CHD.

Proprietários estão mais satisfeitos que inquilinos? Uma análise discriminante no contexto urbano

Abstract This study investigates whether there are differences between the perceptions of owners and tenants in the urban context. Understanding this matter can influence decisions on housing policies, as it helps to comprehend the general satisfaction of subjects. Based on the questionnaire that was administered and on statistical analyses, we observed the importance of the maintenance and infrastructure of the urban context for both groups. The neighborhood's location, services, and resources discriminated between the groups, and highlighted the relevant characteristics for each target audience.

There Goes the Neighborhood: How Racial Threat Shapes the Formation of Integrated and Segregated Neighborhoods

Neighborhoods in the United States are highly segregated by race. Black–white segregation is particularly acute. Although racial segregation has been declining in recent years, it remains extremely high—especially in the largest metropolitan areas. The historical pattern has been that as African Americans move into a neighborhood, whites move out and thus the neighborhood (gradually or rapidly) transitions from predominantly white to predominantly black. A seemingly contradictory pattern has recently been observed: integrated neighborhoods are becoming more common. Why do some neighborhoods transition from white to black and others become integrated? We use racial competition theory to answer this question. We examine the characteristics of stable, integrated neighborhoods and contrast them with segregated predominantly white, predominantly black, and rapidly transitioning neighborhoods in six large metropolitan areas. We find that the location of black neighborhoods is a key factor in whether an integrated neighborhood remains integrated. Specifically, we find integrated neighborhoods are spatially distant from majority black neighborhoods while transitioning neighborhoods are adjacent to them. Our findings support the “sense of threat” hypothesis of racial competition theory.

Planning for fear of crime reduction: Assessing the impact of public space regeneration on safety perceptions in deprived neighborhoods

Fear of crime significantly reduces people’s time–space access to and use of public space, notably in high-crime neighborhoods where concerns around personal safety are more acute. One widely used strategy to reduce fear of crime is the regeneration of the built environment. However, tension remains on whether this strategy is effective, and if it is, then where, which and for whom public space interventions work. This research, incorporating a gender perspective, assesses whether neighborhood-level regeneration of public space significantly enhances or reduces residents’ perceptions of safety in deprived urban areas with a gender perspective. To test these impacts, we run a randomized control trial with 100 residents in a high-crime neighborhood in Santiago de Chile. A series of geotagged photographs of the area and ten treatment photo simulations of proposed interventions were rated by residents according to their perceived safety related to crime. The results suggest that: highly unsafe perceptions cluster in specific neighborhood locations and are particularly acute for women; the regeneration of public spaces significantly increases perceived safety for both men and women; and the effectiveness of different interventions is gender-specific. The results suggest, while an effective technique, public space regeneration in deprived neighborhoods can be further optimized through urban design and planning policy that are space- and gender-specific. The technique presented could support researchers and practitioners to understand the spatial distribution of perceptions of safety, to select effective interventions to make deprived neighborhoods feel safer, and to assess the impact of regeneration strategies.

The Impacts of the Multiscale Built Environment on Commuting Mode Choice: Spatial Heterogeneity, Moderating Effects, and Implications for Demand Estimation

Promoting the usage of sustainable commuting modes requires in-depth understanding about residents’ commuting mode choice behavior. This study presents an empirical study to investigate the relationship between the built environment and commuting mode choice using CLDS 2016 cross-city questionnaire data. Several multilevel multinomial logit models including the null model, base model, and moderating effect model are developed to analyze the effects of built environments at both city and neighborhood levels on commuting mode choice. Estimation results of the null model reveal the significant spatial heterogeneities in commuting mode choice across different cities and different neighborhoods within a specific city. We then explore the potential built environment variables yielding the spatial heterogeneity via the base model. Results show that the built environment at the city level (including the urbanization rate, number of public transportation vehicles, metro operating mileage, GDP, city population density, and road area per capita) and neighborhood level (including neighborhood population density, air quality, neighborhood location, and land use diversity) could partially explain the spatial heterogeneities in commuting mode choice. In addition, the moderating effects of these built environments on the link between commuting time and commuting mode choice are examined. Results imply that the urbanization rate and neighborhood population density moderate the effect of commuting time on choosing nonmotorized modes, while neighborhood location moderates the effect of commuting time on choosing public transit. Also, the mode shares of nonmotorized mode and public transit under different levels of commuting time are estimated in different built environment contexts. The findings of this study are expected to provide serviceable support for urban planning and transportation policy making.

If Residential Segregation Persists, What Explains Widespread Increases in Residential Diversity?

Recent studies have identified increasing residential diversity as a near-universal trend across the United States. At the same time, a wide range of scholarship notes the persistence of White flight and other mechanisms that reproduce residential segregation. In this article, we attempt to reconcile these findings by arguing that current trends toward increased residential diversity may sometimes mask population changes that are more consistent with racial turnover and eventual resegregation. Specifically, we show that increases in diversity occur nearly identically across neighborhoods where White populations remain stable or decline in the face of non-White population growth. Our findings demonstrate that, particularly in its early stages, racial turnover decouples diversity and integration, leading to increases in diversity without corresponding increases in residential integration. These results suggest that in many neighborhoods, diversity increases may be transitory phenomena driven primarily by a neighborhood's location in the racial turnover process. In the future, stalled or decreasing levels of diversity may become more common in these areas as segregation persists and the process of racial turnover continues.

Global survey of star clusters in the Milky Way

Aims. We built Galactic open star cluster mass functions (CMFs) for different age sub-samples and spatial locations in the wider solar neighbourhood. Here, we present a simple cluster formation and evolution model to reproduce the main features of the CMFs. Methods. We used an unbiased working sample of 2227 clusters of the Milky Way Star Cluster (MWSC) catalogue, which occupy the heliocentric cylinders with magnitude-dependent completeness radii of 1–5 kpc. The MWSC survey provides an extended set of open star cluster parameters, including tidal radii, distances, and ages. From an analytic three-component Galaxy model, we derived tidal masses of clusters with a typical accuracy of about 70%. Our simple model includes a two-section cluster initial mass function, constant cluster formation rate, supervirial phase after a sudden expulsion of the remaining gas, and cluster mass loss due to stellar evolution and the clusters’ gradual destruction in the Galactic tidal field. The dynamical evolution model is based on previous N-body simulations. Results. The obtained tidal masses have been added to the MWSC catalogue. A general CMF (GCMF), built for all cluster ages around the Sun, has a bell-like shape and extends over four decades in mass. The high-mass slope found for tidal mass log mt/M⊙ ≥ 2.3 is equal to 1.14 ± 0.07. The CMFs for different age groups show the same high-mass slopes, while the low-mass slope is nearly flat for the youngest sub-sample (clusters younger than 20 Myr) and about −0.7 for the others. The inner and outer sub-samples covering Galactocentric radii R = 4.2–8.1 kpc and 8.9–13.5 kpc, respectively, are consistent with the GCMF, once the exponential decline of the Galactic disc density is taken into account. The model suggests star formation with low efficiency of 15–20%, where only 10% of stars remain bound in a cluster after gas expulsion and subsequent violent relaxation. The cluster formation rate required to reproduce the observed distributions in age and mass is about 0.4 M⊙ pc−2 Gyr−1. Conclusions. The obtained high-mass slope of the GCMF for the wide neighbourhood of the Sun is similar to slopes determined earlier in nearby galaxies for more luminous clusters with log m/M⊙ > 3.8. The MWSC catalogue supports models with a low star-formation efficiency, where 90% of stars are lost quickly after gas expulsion. The obtained cluster formation rate corresponds to open clusters’ contribution to the stellar content of the thin disc at the level of 30%.

Implementation of Sustainable Agrotourism for Food Crops Planning in Agricultural Land

Agricultural land for food crops is a pillar of the economy in an area to increase local revenue, besides that, it is also a source of food crops for the local area. In maintaining agricultural land so that land conversion does not occur, one of the efforts that can be done is through food crops as sustainable agro-tourism. The research location is in the Anja neighborhood, Tawaeli District, Palu City. The purpose of this research is to make the concept and design of agricultural land for food crops to become a sustainable agro-tourism area through architectural design methods. The research method used is the planning and architectural design methodology through a dimensional approach to a comprehensive approach. This is based on the fact that this research interacts thoroughly with many aspects related to the concept and design of agricultural land for food crops as a sustainable agro-tourism area. Based on the analysis of the potential of the area, the results showed that the location of agricultural land for food crops in the Anja neighborhood has a strong attraction as agro-tourism, besides the Anja neighborhood location is very strategic. The facilities and infrastructure owned by agricultural land in the Anja neighborhood can also be maintained in several agro-tourism facilities

Single-Family Zoning and Race: Evidence From the Twin Cities

The city of Minneapolis recently changed its zoning to allow two- and three-family houses in formerly single-family zones, in part with the goal of furthering racial integration. To test whether this policy approach holds promise, we assemble digital zoning data covering the Minneapolis–St. Paul metro area and quantify the relationship between different types of residential zoning and racial and ethnic shares of neighborhood populations. Controlling for neighborhood location, we find that a neighborhood zoned for middle housing, such as Minneapolis’ triplexes, has a non-White population share that is 14 percentage points higher than that of a single-family zoned neighborhood. A neighborhood zoned for multifamily housing has a non-White population share 21 percentage points higher. This is consistent with the argument that upzoning single-family zones to allow middle and multifamily housing can promote racial integration. Our method can be easily replicated in other regions as data become available.

Emergency General Surgery Procedures in Older Adults: Where You Live Matters!

Neighborhood location and its built environment are important social determinants of health that impact health outcomes. Older adults (OAs) represent the fastest growing population in the United States with many requiring emergency general surgery procedures (EGSPs). The aim of this study was to evaluate whether neighborhood location, represented by zip code, influences mortality and disposition in OAs undergoing EGSPs in Maryland. A retrospective review was undertaken of hospital encounters in the Maryland Health Services Cost Review Commission from 2014 to 2018 of OAs undergoing EGSPs. Older adults residing in the 50 most affluent (MANs) and 50 least affluent (LANs) neighborhoods based on zip codes were compared. Data collected included demographics, all patient-refined (APR)-severity of illness (SOI), APR-risk of mortality (ROM), Charlson Comorbidity Index, complications, mortality, and discharge to a higher level of care. Of the 8661 OAs analyzed, 2362 (27.3%) resided in MANs and 6299 (72.7%) in LANs. Older adults in LANs were more likely to undergo EGSPs, had higher APR-SOI and APR-ROM, and experienced more complications, discharge to higher level of care, and mortality. Living in LANs was independently associated with discharge to higher level of care (OR 1.56, 95% CI: 1.38-1.77, P < .001) and increased mortality (OR 1.35, 95% CI: 1.07-1.71, P = .01). Mortality and quality of life in OAs undergoing EGSPs are dependent on environmental factors likely determined by neighborhood location. These factors need to be defined and incorporated in predictive models of outcomes. Public health opportunities to improve outcomes for those who are socially disadvantaged are necessary.

Narratives of people with serious mental illness in Housing First: a qualitative analysis of factors contributing to housing instability and housing stability

ABSTRACT There has been little previous research that has examined factors related to housing stability and instability among Housing First (HF) tenants. Qualitative research was used to determine themes related to housing stability and instability for people with mental illness participating in a HF program implemented in five Canadian cities. Data were gathered for all participants who completed qualitative interviews at baseline and an 18-month follow-up. At the 18-month follow-up interviews, those who were stably housed (n = 110) were significantly more likely to report positive life changes than those who were unstably housed (n = 75). Among a sub-sample of these participants, in-depth narratives were compared for stably housed (n = 25) and unstably housed (n = 21) tenants. Challenges to achieving housing stability included substance use and continued exposure to substance-using networks, evictions/multiple housing losses, incarceration and/or involvement with the legal system, and neighborhood location of housing. Themes promoting housing stability were having positive relationships with the HF program staff and program, achieving greater community integration, and making progress towards recovery. Recommendations for promoting housing stability were provided.