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Disproportionate flood exposure for slum populations of the Global South

Nature Cities (2025)Cite this article

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Abstract

Rapid urbanization is leading to the expansion of human settlements in flood-prone areas, and the impact is not uniform across different communities. Few studies have comprehensively investigated the flood exposure faced by vulnerable communities in the Global South, where pervasive slums present a major challenge to inclusive urban planning and flood management. Here, combining advanced machine learning techniques and publicly available satellite images, we identify hot spots of urban slum populations in floodplains in the Global South and examine their settlement patterns. We find that approximately one in three people living in slums in the Global South resides in a floodplain. Slum dwellers are 32% more likely to settle in floodplains compared with residents in adequate housing. The concentration of slum populations is particularly high in areas that have experienced severe floods. These data-driven insights highlight the disproportionate flood exposure faced by slum dwellers in the Global South and underscore the need for just and equitable flood adaptation management.

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Fig. 1: Hotspots of slum dwellers settling in floodplains in the Global South.
Fig. 2: The floodplain settlement bias between slum dwellers and nonslum residents at global and regional levels.
Fig. 3: The divergence of floodplain settlement bias in different geographic regions.
Fig. 4: Relationship between the concentration of slum dwellers and their settlement in floodplains by flood-hazard level.
Fig. 5: Workflow of the modeling approach and floodplains settlement bias analysis.

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Data availability

All household-based data are available to download free of charge by registered users from the DHS program (https://www.dhsprogram.com/data/). Satellite images and land cover layer products can be accessed via the GEE platform (https://earthengine.google.com/), specifically including the Landsat images (https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2, https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2), and the Copernicus Global Land Cover Layers (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global). The Global NPP-VIIRS-like nighttime light dataset is from the Harvard Dataverse (https://doi.org/10.7910/DVN/YGIVCD). The Settlement Model grid (GHS-SMOD) and Population Grid (GHS-POP) can be obtained from the Global Human Settlement Layer (https://ghsl.jrc.ec.europa.eu/download.php). The Emergency Events Database (EM-DAT) records and their geocoded extension are obtained from the Centre for Research on the Epidemiology of Disasters (https://www.emdat.be/) and the Socioeconomic Data and Applications Center (https://sedac.ciesin.columbia.edu/data/set/pend-gdis-1960-2018). The administrative boundary data used in this study are publicly available from the GADM database (https://gadm.org/).

Code availability

Codes for reproducibility of the findings are available via Zenodo at https://doi.org/10.5281/zenodo.14790692 (ref. 84).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers 72033005, 72403147 and 42401379), China Postdoctoral Science Foundation (grant number 2023M732043) and the Shandong Provincial Natural Science Foundation (grant number ZR2023QG076).

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Authors and Affiliations

  1. Institute of Blue and Green Development, Shandong University, Weihai, China

    Dan Li & Ning Zhang

  2. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Laixiang Sun & Kuishuang Feng

  3. School of Finance and Management, SOAS University of London, London, UK

    Laixiang Sun

  4. Department of Land Economy, University of Cambridge, Cambridge, UK

    Ning Zhang

  5. Division of Liberal Studies, Howard Community College, Columbia, MD, USA

    Yang Yu

  6. Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China

    Dandan Zhao

  7. Department of Geography, The University of Hong Kong, Hong Kong, China

    Yuyu Zhou

  8. Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, China

    Yuyu Zhou

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Contributions

D.L., L.S. and K.F. designed the study. D.L., Y.Y., D.Z. and Y.Z. performed the analysis and prepared the manuscript. L.S., K.F. and N.Z. coordinated and supervised the project. D.L., L.S., K.F., N.Z. and Y.Y. participated in writing and revising the manuscript.

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Correspondence to Laixiang Sun, Kuishuang Feng or Ning Zhang.

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Nature Cities thanks Konstantin Klemmer, Ron Mahabir and Jun Rentschler for their contribution to the peer review of this work.

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Li, D., Sun, L., Feng, K. et al. Disproportionate flood exposure for slum populations of the Global South. Nat Cities (2025). https://doi.org/10.1038/s44284-025-00273-3

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