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Mapping urban slums and their inequality in sub-Saharan Africa

Nature Cities volume 2, pages 1037–1048 (2025)Cite this article

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Abstract

Slums house nearly one-quarter of the global urban population, yet their spatial and socioeconomic dynamics remain poorly understood, hindering progress toward the United Nations’ commitment to ‘leave no one behind’. Here, focusing on sub-Saharan Africa, we integrate geospatial data with household surveys to map slum prevalence and asset-based wealth inequalities across 32 countries. We identify that 54.6% of the urban population in these countries live in slums, with lower wealth levels compared with non-slum areas. Despite a reduction in the proportion of slum populations over the past two decades, wealth inequalities have risen, especially in countries with substantial slum populations. This study highlights profound inequalities in urban essential services, living conditions and household wealth in sub-Saharan African countries, emphasizing the importance of fine-scale analysis of slum distributions and associated wealth inequalities to inform inclusive programs.

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Fig. 1: Framework for slum mapping and urban inequality assessment.
Fig. 2: Trends in urban slum conditions and wealth inequality across African countries (2005–2022).
Fig. 3: Accuracy assessment of slum mapping.
Fig. 4: Predicted wealth index and estimated SSI across slum and non-slum areas in selected SSA cities.
Fig. 5: Wealth index prediction and comparison between slum and non-slum areas.
Fig. 6

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Data availability

All data are publicly available via Zenodo at https://zenodo.org/records/14998570 (ref. 72). The repository includes Google Earth Engine code with 79 geospatial datasets for mapping slums and the wealth index as well as estimating the slum population. The geospatial dataset includes a slum sampling dataset, environmental input data, a socioeconomic dataset and other output data, including datasets on ‘Living Space Slum’ and ‘Greenspace’ as well as processed satellite data. The satellite images of Sentinel-1 and Sentinel-2, land cover data from European Space Agency WorldCover, land surface temperature data from MODIS LST (MYD11A2 V6.1) and a topography dataset of SRTM, as well as a population dataset, road networks accessed through Google Earth Engine and the awesome-gee-community-dataset. Data on the health sites are available at https://healthsites.io/, and the dataset on waterways is available at https://www.hydrosheds.org/products. The repository also includes CSV datasets and R code to facilitate further analysis, such as assessing accuracy, comparing the slum population in this study with UN estimates, and estimating changes in slum populations and wealth inequality by analyzing the DHS dataset. The DHS survey is available at https://www.dhsprogram.com/.

Code availability

All code is publicly available via Zenodo at https://zenodo.org/records/14998570 (ref. 72).

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Acknowledgements

This work was supported by the National Key R&D Program of China (grant no. 2022YFE0209400, L.Y.), the Tsinghua University Initiative Scientific Research Program (grant no. 20223080017, L.Y.), the China Postdoctoral Science Foundation (grant no. 2022M721770, C.L.), the Shuimu Tsinghua Scholar Program (C.L.) and the Research Project (General Project) of the Ministry of Education of the People’s Republic of China (grant no. 24YJAZH001, A.J.B.).

Author information

Authors and Affiliations

  1. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Chengxiu Li & Le Yu

  2. School of Geography and Environmental Science, University of Southampton, Southampton, UK

    Chengxiu Li & Jim Wright

  3. Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing, China

    Le Yu

  4. Department of Earth System Science, Tsinghua University, Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing, China

    Le Yu

  5. Data and Analytics Section, Knowledge and Innovation Branch, United Nations Human Settlements Programme, Nairobi, Kenya

    Robert Ndugwa

  6. Department of Social Sciences, Beijing Normal-Hong Kong Baptist University, Zhuhai, China

    Adrian J. Bailey

  7. Department of Real Estate and Construction, The University of Hong Kong, Hong Kong, China

    Xiaoling Zhang

  8. Sustainability X-Lab, The University of Hong Kong, Hong Kong, China

    Xiaoling Zhang

  9. GeoPsy Research, Nairobi, Kenya

    Francis Oloo

  10. School of Natural and Applied Sciences, University of Malawi, Zomba, Malawi

    Ellasy Gulule Chimimba

  11. Ndata School of Climate and Earth Sciences, Malawi University of Science and Technology, Thyolo, Malawi

    Ellasy Gulule Chimimba

  12. Beijing Technology and Business University, Beijing, China

    Xiangzheng Deng

  13. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Xiangzheng Deng

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Contributions

C.L., L.Y. and J.W. conceptualized the study. C.L. led the data analysis and drafted the paper. A.J.B., X.Z. and J.W. contributed to the methodological design and paper revisions. F.O. and E.G.C. supported data interpretation and provided critical local context. L.Y. supervised the overall project. All authors contributed to discussions, provided critical feedback and approved the final version of the paper.

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Correspondence to Le Yu.

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Nature Cities thanks John Friesen, Karin Pfeffer and Tanzil Shafique for their contribution to the peer review of this work.

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Li, C., Yu, L., Ndugwa, R. et al. Mapping urban slums and their inequality in sub-Saharan Africa. Nat Cities 2, 1037–1048 (2025). https://doi.org/10.1038/s44284-025-00276-0

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