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Over the last decades, massive urbanization processes lead to the emergence of large slum areas making them home to about a seventh of the global population. Although the variety of morphological characteristics varies significantly within as well as across cities, common determinants exist. Informal, or unplanned settlements in particular, do show similar morphologies over the world. They are characterized mostly by extremely high building densities and small building sizes, irregular arrangement of buildings and street network and are often located at exposed sites . Based on these characteristics, we deploy satellite images for a systematic mapping of morphological slum areas in the city of Rio de Janeiro, Brazil based solely on physical characteristics and analyse the mapping result with the official census data. Outcomes show first that morphological slums are a semantic and spatial sub-group of all slum areas contained by the Brazilian census and that remote sensing-based mapping yields accuracies of almost 94%. Second, analysis of census-based income data proofs that while almost 45% of all mapped slum blocks are characterized by incomes below the poverty line, as defined by the Organisation for Economic Co-operation and Development (OECD), this holds true for only about 6% of the formal urban neighbourhoods. [ABSTRACT FROM PUBLISHER]

Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with better knowledge. Better understanding through harmonising and analysing data in large numbers of cities is essential to identifying the most effective means of disease prevention and understanding context dependencies important for policy., Competing Interests: Declaration of competing interest The findings and conclusions in this commentary are those of the authors. In particular, these should not be construed to represent any official USDA or U.S. Government determination or policy. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the conclusions reported in this paper.

Background: As the world becomes increasingly urbanised, there is recognition that public and planetary health relies upon a ubiquitous transition to sustainable cities. Disentanglement of the complex pathways of urban design, environmental exposures, and health, and the magnitude of these associations, remains a challenge. A state-of-the-art account of large-scale urban health studies is required to shape future research priorities and equity- and evidence-informed policies., Objectives: The purpose of this review was to synthesise evidence from large-scale urban studies focused on the interaction between urban form, transport, environmental exposures, and health. This review sought to determine common methodologies applied, limitations, and future opportunities for improved research practice., Methods: Based on a literature search, 2958 articles were reviewed that covered three themes of: urban form; urban environmental health; and urban indicators. Studies were prioritised for inclusion that analysed at least 90 cities to ensure broad geographic representation and generalisability. Of the initially identified studies, following expert consultation and exclusion criteria, 66 were included., Results: The complexity of the urban ecosystem on health was evidenced from the context dependent effects of urban form variables on environmental exposures and health. Compact city designs were generally advantageous for reducing harmful environmental exposure and promoting health, with some exceptions. Methodological heterogeneity was indicative of key urban research challenges; notable limitations included exposure and health data at varied spatial scales and resolutions, limited availability of local-level sociodemographic data, and the lack of consensus on robust methodologies that encompass best research practice., Conclusion: Future urban environmental health research for evidence-informed urban planning and policies requires a multi-faceted approach. Advances in geospatial and AI-driven techniques and urban indicators offer promising developments; however, there remains a wider call for increased data availability at local-levels, transparent and robust methodologies of large-scale urban studies, and greater exploration of urban health vulnerabilities and inequities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The world is becoming increasingly urbanised. As cities around the world continue to grow, it is important for urban planners and policy makers to understand how different urban configuration patterns affect the environment and human health. However, previous studies have provided mixed findings. We aimed to identify European urban configuration types, on the basis of the local climate zones categories and street design variables from Open Street Map, and evaluate their association with motorised traffic flows, surface urban heat island (SUHI) intensities, tropospheric NO 2 , CO 2 per person emissions, and age-standardised mortality., Methods: We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal-Wallis test followed by a post-hoc Dunn's test., Findings: We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO 2 exposure were significantly higher in compact high density and open low-rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO 2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types., Interpretation: Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health., Funding: Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making as a Horizon Europe project., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)