Urban Deprivation Research Articles

The behavioural house indicator: A faster and real time small-area deprivation measure for England

Researchers have been long preoccupied with the measuring and monitoring of economic and social deprivation at small scales, neighbourhood, level in order to provide official government agencies and policy makers with more precise data insights. Whist valuable methodologies have been developed, the exercise of data collection associated with these methods tends to be expensive, time consuming, published infrequently with significant time delays, and subject to recurring changes to methodology. Here, we propose a novel method based on a straightforward methodology and data sources to generate a faster and real time indicator for deprivation at different scaling, small to larger areas. The results of our work show that our method provides a consistent view of deprivation across the regions of England and Wales, which are in line with the other indexes, but also highlight specific flash points of deep rural and highly dense urban deprivation areas that are not well captured by existing indexes. Our method is intended to aid researchers and policy makers by complementing existing but infrequent indexes.

Addressing Urban Management Challenges for Sustainable Development: Analyzing the Impact of Neighborhood Deprivation on Crime Distribution in Chicago

Urban crime prevention remains a critical challenge intertwined with socioeconomic disparities. This study investigates the spatial nexus between urban deprivation and crime decline in Chicago from 2015 to 2022, addressing the question: How do changes in neighborhood deprivation relate to crime reduction patterns? Using comprehensive crime incident and census tract Area Deprivation Index (ADI) data, this study conducted exploratory spatial analysis, regression modeling, and local bivariate relationship analysis. The findings reveal persistent hotspots of concentrated deprivation on Chicago’s south and west sides, alongside a general citywide crime decline. However, the current research uncovered significant spatial heterogeneity in both deprivation patterns and crime reduction, challenging the achievement of equitable public safety outcomes. Surprisingly, while crime incidents in 2015 and 2022 significantly predicted crime change, ADI scores were not statistically significant predictors in our regression model. Local bivariate analysis exposed diverse relationships between ADI changes and crime rate changes across neighborhoods, with 72.2% of census tracts showing no statistically significant relationship. This underscores the complexity of urban crime dynamics and the importance of local context in understanding these patterns. Our research contributes a nuanced understanding of the intricate relationship between urban deprivation and crime patterns, providing valuable insights for policymakers, law enforcement agencies, and urban planners. These findings highlight the need for integrated, long-term strategies that address both crime prevention and socioeconomic disparities, ultimately fostering safer, more equitable urban environments and informing evidence-based interventions tailored to specific neighborhood contexts.

The revenge of districts that don’t matter: inequality and elections in Rome from 2000 to 2023

ABSTRACT This article examines whether the theory of the ‘revenge of the places that don’t matter’ applies to urban districts within a metropolis like Rome, Italy. To contribute to this body of literature, we test the hypothesis of a ‘deprivation loop’ arising from local issues related to socio-economic marginality and urban deprivation. To explore this hypothesis, we analyse the relationships among compositional and contextual indicators using principal components analysis. We then use the resulting factor components as explanatory variables in multiple linear regression analyses of voting patterns. Between 2000 and 2023, electoral outcomes reveal that left-wing parties perform best in semi-central, densely populated districts, while right-wing and populist parties gain more support in low-density, sprawling outskirts. Other areas exhibit more variable trends following the turning points of 2008 and 2016. Our findings indicate that certain territorial determinants of discontent enhance electoral support for right-wing or populist parties by channelling protest votes. Key factors include residence in new sprawling districts with many young households, in socially challenged and marginal areas with a high prevalence of precarious or underemployed workers (often foreigners), and generally in ‘left behind’ places.

Urban form and socioeconomic deprivation in Isfahan: An Urban MorphoMetric approach

Studies on urban deprivation date back to the 19th Century but remain important today due to rising levels of inequality and social segregation. However, while social causes of deprivation have been investigated, the role of the built environment remains neglected. Existing studies either provide broad coverage at the expense of detailed morphological descriptions or offer meticulous accounts of small-scale areas without capturing the broader context. This paper addresses such a gap by investigating the relationship between urban form, measured at the building level, and deprivation across the entire city of Isfahan, Iran. By doing so, we position this study in the tradition of urban morphology. Operationally, we, first, identify urban types (UTs), that is, distinctive patterns of urban form, by clustering 200+ morphological characters; second, we explore the relationship between proportion of buildings belonging to each UT, in each neighbourhood, and deprivation; third, we offer detailed descriptions of the UTs most strongly associated with deprivation, discuss possible drivers for the observed correlations, and link findings to relevant literature in the field. Twelve UTs are identified, with four showing the strongest impacts on predicting deprivation. This study brings novel insights on the morphology of deprivation of Isfahan, while contextualising them with respect to domain-specific studies, which have predominantly focused on Western cities. The proposed methodology can be replicated to explore morphologies of deprivation in different contexts, further our understanding of the topic, and potentially inform planning and policy making.

Caring at the edges: Infrastructures of care and repair in urban deprivation

Care is an activity vital to making the world liveable. Alternative infrastructures of care have emerged that re-centre care and repair within everyday life. These infrastructures often require more care to address care deficits and repair the social fabric of society; however, insights are limited as to the implications of demands on people and the places they reside for such extra care. Through an ethnographic study of a community food hub in an area of entrenched deprivation, we examine how an alternative infrastructure of care is built in practice. We demonstrate how care unfolds expansively across people and place to (reactively) repair care deficits and (proactively) generate new care relations. We contribute to the complexity of care theorising by revealing the challenges of care provision that stem from a position of necessity and repair. We offer a critical discussion of alternative infrastructures of care that simultaneously recognises the opportunities for hope, whilst acknowledging their limits.

AI perceives like a local: predicting citizen deprivation perception using satellite imagery

Deprived urban areas, commonly referred to as ‘slums,’ are the consequence of unprecedented urbanisation. Previous studies have highlighted the potential of Artificial Intelligence (AI) and Earth Observation (EO) in capturing physical aspects of urban deprivation. However, little research has explored AI’s ability to predict how locals perceive deprivation. This research aims to develop a method to predict citizens’ perception of deprivation using satellite imagery, citizen science, and AI. A deprivation perception score was computed from slum-citizens’ votes. Then, AI was used to model this score, and results indicate that it can effectively predict perception, with deep learning outperforming conventional machine learning. By leveraging AI and EO, policymakers can comprehend the underlying patterns of urban deprivation, enabling targeted interventions based on citizens’ needs. As over a quarter of the global urban population resides in slums, this tool can help prioritise citizens’ requirements, providing evidence for implementing urban upgrading policies aligned with SDG-11.

Developing a Methodological Model for Monitoring and Measuring Urban Poverty and Deprivation, and Its Spatial Distributions

Developing a Methodological Model for Monitoring and Measuring Urban Poverty and Deprivation, and Its Spatial Distributions

Slum and urban deprivation in compacted and peri-urban neighborhoods in sub-Saharan Africa

UN-Habitat estimates that 51.3% of the urban population in sub-Saharan Africa (SSA) resided in slums in 2020, and future projections indicate continued growth. However, limited information on the spatial distribution and evolution of slums in the region underestimates the challenges they present.This study investigates the use of urban morphology to map slums in 95 cities across Nigeria, Kenya, Ghana, and Malawi. The approach employed an unsupervised classification and a tree-based clustering framework, integrating morphological and socio-economic indicators, as well as comprehensive sampling points for slums.Our findings indicate that morphological clusters with compact, small buildings are indicative of a high prevalence of slums, with an accuracy rate of 83.6%. Moreover, these morphological slum clusters exhibit significant correlations with socio-economic indicators, exhibiting lower GDP and wealth index compared to neighbouring clusters. Notably, larger and older slums demonstrate improved economic well-being and enhanced infrastructures services.Our findings underscore the potential of utilizing urban morphology to comprehend the diversity and dynamics of urban slums and socioeconomic development. These results provide a foundation for large-scale identification of slums and urban deprivation, offering support for targeted solutions to address the challenges associated with slums in developing countries.

Geospatial Analysis of Access to Evidence-Based Early Pregnancy Loss Management in New Mexico [ID: 1377363

INTRODUCTION: Little is known about availability of evidence-based early pregnancy loss (EPL) treatment in emergency departments (EDs), where patients often first seek care. We evaluated geographic access to mifepristone/misoprostol and uterine aspiration in New Mexican hospitals. METHODS: We used an enhanced two-step floating catchment area method to model accessibility from census block groups’ population-weighted centroids to hospitals. Our primary outcome was access to mifepristone/misoprostol and uterine aspiration in EDs; our secondary outcome was access to in-hospital aspiration, both outcomes defined as less than a 60-minute commute. We surveyed all EDs in New Mexico and used public databases to compute census block groups’ demographic, transportation, rurality, and area deprivation data. We used logistic regression to evaluate the associations between access and race and ethnicity, area deprivation, and rural location. The University of New Mexico IRB approved this study. RESULTS: Thirty-five (83%) of 42 hospitals responded. Two (6%) provided in-ED treatment, and 24 (69%) in-hospital aspiration. Half of reproductive-aged women had access to in-ED treatment, and 90% to in-hospital aspiration. Census block groups with higher quartile proportions of American Indian/Native Alaskan reproductive-aged women had higher adjusted odds ratios of accessing in-ED treatment (2.5–7.3, P<.05). Rural areas and higher area deprivation quartiles had lower in-ED access adjusted odds ratios (0.03–0.07 [P<.05] and 0.3–0.4 [P<.05], respectively) compared with urban and lower area deprivation quartiles. In-hospital aspiration results were similar to in-ED treatment results across all categories. CONCLUSION: By prioritizing rural areas and areas with higher socioeconomic deprivation, EPL treatment implementation efforts can improve equitable care access and equity for patients.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BackgroundAlthough malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.MethodsA workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.ResultsThe identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.ConclusionsThis study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

Contextualizing spatiality of multidimensional poverty in rural and urban India: A geographical perspective

The present study tries to estimate the spatial pattern of incidence, extent and severity of multidimensional poverty in rural and urban India during 2021 obtaining data from the report of NITI Aayog based on NFHS-4 dataset. The study reveals that large interstate disparities persist in the pattern of multidimensional poverty and its intensity in rural and urban India. The study also attempts to examine the interstate deprivation in different indicators of rural and urban MPI which confirms that the magnitude of deprivation is acute in most of the poverty indicators in the poorer states of India like Bihar, Jharkhand, Uttar Pradesh, Madhya Pradesh etc. Finally we have also performed the decomposition analysis to identify the most significant contributor for multidimensional poverty which revealed in both rural and urban space among the dimension health and among the indicators nutrition have made most significant contribution in overall score of multidimensional poverty. The target oriented policy for poverty eradication programmes needs to be stressed. The present study may be beneficial for the development planners and policy makers to understand the pattern of rural and urban multidimensional poverty and deprivation across the states of India in a better way.

The neighborhood context and all-cause mortality among older adults in Puerto Rico.

Recent efforts have been made to collect data on neighborhood-level attributes and link them to longitudinal population-based surveys. These linked data have allowed researchers to assess the influence of neighborhood characteristics on the health of older adults in the US. However, these data exclude Puerto Rico. Because of significantly differing historical and political contexts, and widely ranging structural factors between the island and the mainland, it may not be appropriate to apply current knowledge on neighborhood health effects based on studies conducted in the US to Puerto Rico. Thus, we aim to (1) examine the types of neighborhood environments older Puerto Rican adults reside in and (2) explore the association between neighborhood environments and all-cause mortality. We linked data from the 2000 US Census to the longitudinal Puerto Rican Elderly Health Conditions Project (PREHCO) with mortality follow-up through 2021 to examine the effects of the baseline neighborhood environment on all-cause mortality among 3,469 participants. Latent profile analysis, a model-based clustering technique, classified Puerto Rican neighborhoods based on 19 census block group indicators related to the neighborhood constructs of socioeconomic status, household composition, minority status, and housing and transportation. The associations between the latent classes and all-cause mortality were assessed using multilevel mixed-effects parametric survival models with a Weibull distribution. A five-class model was fit on 2,477 census block groups in Puerto Rico with varying patterns of social (dis)advantage. Our results show that older adults residing in neighborhoods classified as Urban High Deprivation and Urban High-Moderate Deprivation in Puerto Rico were at higher risk of death over the 19-year study period relative to the Urban Low Deprivation cluster, controlling for individual-level covariates. Considering Puerto Rico's socio-structural reality, we recommend that policymakers, healthcare providers, and leaders across industries to (1) understand how individual health and mortality is embedded within larger social, cultural, structural, and historical contexts, and (2) make concerted efforts to reach out to residents living in disadvantaged community contexts to understand better what they need to successfully age in place in Puerto Rico.

Towards accessibility or affordability? Multidimensional energy poverty across the South African urban–rural divide

This study combines energy affordability and accessibility into a multidimensional energy poverty measure, which we stratify by rural and urban locale. Accessibility considers a number of binary indicators related to the type of energy used for a series of household activities, while affordability is determined by the ratio of household required energy expenditure to total expenditure. We employ an equivalence scale approach to estimate household energy requirements using publicly available household expenditure survey data. Our results suggest extensive urban–rural disparities across our multidimensional indicators—37% of rural households are affordability deprived, which is nearly double urban affordability deprivation; the rural–urban differences are at least double, when it comes to clean cooking, lighting, space heating, water heating and multidimensional headcount poverty. After splitting the households by degrees of energy poverty, it is found that the extreme energy-poor are more likely to be income-poor. However, urban extreme energy-poor is driven by affordability deprivation, while more than half of the rural extreme energy-poor are deprived in both affordability and accessibility.

Moderation effect of visible urban greenery on the association between neighbourhood deprivation and subjective well-being: Evidence from Hong Kong

The well-being benefit of urban greenery has been extensively documented broadly, though less is known about its moderation effect on the relationship between neighbourhood deprivation and subjective well-being. Some scholars also argued that the inconclusive associations between urban greenery and subjective well-being might be partially attributed to the various measuring methods of greenery and the difficulty to measure visible greenery objectively. In this study, we applied three approaches to objectively measure different aspects of urban greenery, including overall greenery by Normalized Difference Vegetation Index (NDVI), visible greenery by Google Street View (GSV) images, and park proximity by geospatial data. We captured two dimensions of neighbourhood deprivation: socio-economic disadvantage and social fragmentation. Using data from the first wave (2015) of the Hong Kong Panel Survey for Poverty Alleviation (N = 1752), the association between urban greenery, neighbourhood deprivation, and subjective well-being was investigated with multilevel linear regression models, while controlling other covariates. We found that subjective well-being level was negatively associated with social fragmentation but no socio-economic disadvantages, while positively associated with overall greenery and visible greenery. Additional moderation effect analysis reveals that the negative linkage between social fragmentation and subjective well-being was significantly mitigated by visible greenery. These findings demonstrated the importance of visible greenery in enhancing subjective well-being, especially for residents in deprived neighbourhoods, and offered new insights to support urban planning and public health strategies to create a healthy living environment.

Study on Importance, Procedure, and Scope of UNNAT Bharat Abhiyan –A Scoping Review

Unnat Bharat Abhiyan is an essential aspect of enhancing the standard of living for rural residents. The purpose is to "provide rural and local governments in India with access to specialized resources for science, engineering, technology, and management, particularly those with exceptional academic achievements in these domains." Readers can acquire knowledge for specific needs and research in the field of Unnat Bharat Abhiyan. Unnat Bharat Abhiyan is a government program with a unique, transformative and comprehensive approach to development. It anticipates the development of chosen communities in a variety of sectors, including agriculture, health, education, cleanliness, and the environment, in an integrated manner. Unnat Bharat Abhiyan not just to promotes infrastructure, but also links people's involvement, women's equality, hygiene, social justice, educational establishments and localities among rural areas and their people, and other initiatives. It aspires to be a paradigm for implementation and future scope. Furthermore, rural poverty, unemployment, and poor infrastructure have a domino effect on urban slums, culminating in social and economic conflicts, which include economic hardship and urban deprivation. Urbanization in India is the result of demographic explosions and poverty-led rural-to-city migration. Therefore, rural development related to economic growth is very important for improving the standard of living of rural residents. This study deals with this.

Primary care transformation in Scotland: qualitative evaluation of the views of national senior stakeholders and cluster quality leads.

Primary care transformation in Scotland aims to improve population health, reduce health inequalities, and reduce GP workload. Two key strategies (formalised in April 2018 in the new Scottish GP contract [Scottish General Medical Services contract], although started in early 2016) are the expansion of the multidisciplinary team (MDT) and GP cluster working. To explore progress in the implementation of the GP contract in Scotland in terms of the MDT and cluster working. Qualitative study with key national primary care stakeholders (PCSs) (n = 6) and cluster quality leads (CQLs) in clusters serving urban high deprivation areas (n = 4), urban mixed areas (n = 4), and remote and rural areas (n = 4). Semi-structured interviews with thematic analysis. There was general support for the initial aims of the new GP contract but all interviewees felt that progress on both MDT expansion and cluster working was slow, even before the pandemic. None of the CQLs (and few PCSs) felt that GP workload had reduced significantly, nor that the care of patients with complex needs had improved. Lack of time and poorly developed relationships were key barriers, as was a lack of relevant primary care data, and additional support (including guidance, administration, training, and protected time). Key PCSs and CQLs in different areas of Scotland report limited progress in primary care transformation, only partly related to the pandemic. There is a need for better workforce planning and support if the new GP contract is to succeed in transforming primary care in Scotland.

On the association between housing deprivation and urban size: Evidence from South Asia

Housing is a basic human need; however, in recent decades slums have become the face of urbanization in developing economies. Urbanization drives economic growth, playing an important role in providing adequate housing and reducing poverty. In this paper, we investigate the association between housing deprivation and the urban size at a regional level for South Asian countries in the year 2015. We use two main sources of data. The first is the Demographic and Health Surveys (DHS), which provide microdata that allows us to build housing deprivation indexes based on the material characteristics of households and housing assets. The second source is satellite imagery, used to define urban cores and measure the urban size of each region. Then, we use a two-step procedure to identify the relationship between the urban size and housing deprivation. Our results indicate that age and higher levels of education (of the head of household) are negatively associated with our housing deprivation indexes in South Asian households. Furthermore, a greater number of children in South Asian households is related to higher levels of housing deprivation. In the second step, we show that there is a significant negative association between material housing deprivation and the urban size at a regional level for our full sample and for the majority of countries taken individually. An important exception is India, where an inverted-U-shaped relationship between deprivation and urban size is found. This result is robust even when an IV approach is used.

Using mobile money data and call detail records to explore the risks of urban migration in Tanzania

Understanding what factors predict whether an urban migrant will end up in a deprived neighbourhood or not could help prevent the exploitation of vulnerable individuals. This study leveraged pseudonymized mobile money interactions combined with cell phone data to shed light on urban migration patterns and deprivation in Tanzania. Call detail records were used to identify individuals who migrated to Dar es Salaam, Tanzania’s largest city. A street survey of the city’s subwards was used to determine which individuals moved to more deprived areas. t-tests showed that people who settled in poorer neighbourhoods had less money coming into their mobile money account after they moved, but not before. A machine learning approach was then utilized to predict which migrants will move to poorer areas of the city, making them arguably more vulnerable to poverty, unemployment and exploitation. Features indicating the strength and location of people’s social connections in Dar es Salaam before they moved (‘pull factors’) were found to be most predictive, more so than traditional ‘push factors’ such as proxies for poverty in the migrant’s source region.

Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas

Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituting an urgent knowledge gap. The objective of this study is to apply deep learning techniques and morphological analysis to identify degrees of deprivation in DUAs. To this end, we first generate a reference dataset of building footprints using a participatory community-based crowd-sourcing approach. Secondly, we adapt a deep learning model based on the U-Net architecture for the semantic segmentation of satellite imagery (WorldView 3) to generate building footprints. Lastly, we compute multi-level morphological features from building footprints for identifying the deprivation variation within DUAs. Our results show that deep learning techniques perform satisfactorily for predicting building footprints in DUAs, yielding an accuracy of F1 score = 0.84 and Jaccard Index = 0.73. The resulting building footprints (predicted buildings) are useful for the computation of morphology metrics at the grid cell level, as, in high-density areas, buildings cannot be detected individually but in clumps. Morphological features capture physical differences of deprivation within DUAs. Four indicators are used to define the morphology in DUAs, i.e., two related to building form (building size and inner irregularity) and two covering the form of open spaces (proximity and directionality). The degree of deprivation can be evaluated from the analysis of morphological features extracted from the predicted buildings, resulting in three categories: high, medium, and low deprivation. The outcome of this study contributes to the advancement of methods for producing up-to-date and disaggregated morphological spatial data on urban DUAs (often referred to as ‘slums’) which are essential for understanding the physical dimensions of deprivation, and hence planning targeted interventions accordingly.

The public health role of caseloading midwives in advancing health equity in childbearing women and babies living in socially deprived areas in England: The Mi-CARE Study protocol.

This article outlines the protocol for a qualitative Constructivist Grounded Theory study, examining the public health role of caseloading midwives working in a continuity model of care in areas of urban social deprivation. The study is currently being conducted in a city in the south of England during the COVID-19 pandemic. Focusing specifically on the Social Determinants of Health impacting women and babies in this context and from the perspectives of women themselves, the study is developing a theoretical framework examining the actions caseloading midwives take in response to these determinants and how these actions contribute to advancing equity and equality for women and babies at increased risk of adverse perinatal outcomes. Examining and integrating the experiences of women and midwives from a Constructivist Grounded Theory perspective, the study findings will inform current NHS maternity policy and contribute to our understanding about the social processes and mechanisms underpinning the known benefits of midwifery continuity of care models in different contexts.