Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Urban sprawl is associated with reduced access and increased costs of water and sanitation

Nature Cities volume 2, pages 1148–1159 (2025)Cite this article

Subjects

Abstract

Many cities are expanding into areas with scarce rainfall and limited water retention capacity, and are also becoming elongated and sprawled, making it harder to deliver services. Here we quantify the impact of urban form on access to water. We craft comparable urban forms for over 100 cities in Asia, Africa and Latin America. For each city, we analyze the distance to the center, one of the most critical features of cities. We introduce two metrics: remoteness, which quantifies the distance of any location to the city center, and sparseness, a population-weighted average of all locations. We find that less remote areas have higher average income, are closer to critical infrastructure and have higher access to sewage and piped water. Sparser cities have higher water tariffs, lower proximity to critical infrastructure and lower access to sewage and piped water. Finally, we model urban expansion under three scenarios: compact, persistent and horizontal growth. When cities expand through compact growth rather than horizontal expansion, 220 million more people could gain access to piped water, and 190 million to sewage services.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Variation in water availability across urban areas.
Fig. 2: The delineation of cities depends on the proximity of the building footprints.
Fig. 3: Location of buildings in cities depending on the distance to the center.
Fig. 4: Observing a city based on the pixels that constitute it.
Fig. 5: Heterogeneities depending on the distance to the city center.
Fig. 6: Variations across cities in Africa, Asia and Latin America.

Similar content being viewed by others

Data availability

The data used for the analysis are structured into three separate tables, containing information at the city level (105 cities), at the pixel level (20,000 pixels) and at the survey level (with nearly 125,000 survey respondents). The structure is available in Supplementary Note 11, and the aggregate data and results tables are available via GitHub at https://github.com/rafaelprietocuriel/WaterAndCities. Visualizations regarding the radial profile of all cities in the study can be found at https://vis.csh.ac.at/radial-cities.

References

  1. Liu, J. et al. Water scarcity assessments in the past, present, and future. Earth’s Futur. 5, 545–559 (2017).

    Google Scholar 

  2. Adams, E. A., Stoler, J. & Adams, Y. Water insecurity and urban poverty in the Global South: implications for health and human biology. Am. J. Hum. Biol. 32, e23368 (2020).

    Google Scholar 

  3. Ambel, A. A. et al. Addressing gaps in data on drinking water quality through data integration and machine learning: evidence from Ethiopia. Nat. Part. J. Clean Water 6, 63 (2023).

    Google Scholar 

  4. Pumain, D. & Guerois, M. Scaling laws in urban systems. Working Papers 1–26 (Santa Fe Institute, 2004).

  5. He, C. et al. Future global urban water scarcity and potential solutions. Nat. Commun. 12, 4667 (2021).

    Google Scholar 

  6. World Health Organization et al. Progress on Household Drinking Water, Sanitation and Hygiene 2000–2020: Five Years into the SDGs (WHO, 2021).

  7. McDonald, R. I. et al. Urban growth, climate change, and freshwater availability. Proc. Natl Acad. Sci. USA 108, 6312–6317 (2011).

    Google Scholar 

  8. Bischoff-Mattson, Z. et al. Shape of a water crisis: practitioner perspectives on urban water scarcity and ‘Day Zero’ in South Africa. Water Policy 22, 193–210 (2020).

    Google Scholar 

  9. Aboelnga, H. T., El-Naser, H., Ribbe, L. & Frechen, F.-B. Assessing water security in water-scarce cities: applying the integrated urban water security index (IUWSI) in Madaba, Jordan. Water 12, 1299 (2020).

    Google Scholar 

  10. March, H. & Saurí, D. The suburbanization of water scarcity in the Barcelona metropolitan region: sociodemographic and urban changes influencing domestic water consumption. Prof. Geogr. 62, 32–45 (2010).

    Google Scholar 

  11. Haddeland, I. et al. Global water resources affected by human interventions and climate change. Proc. Natl Acad. Sci. USA 111, 3251–3256 (2014).

    Google Scholar 

  12. Zhang, F. & Borja-Vega, C. Water for Shared Prosperity (World Bank, 2024).

  13. Flörke, M., Schneider, C. & McDonald, R. I. Water competition between cities and agriculture driven by climate change and urban growth. Nat. Sustain. 1, 51–58 (2018).

    Google Scholar 

  14. Gómez, J. A., Patiño, J. E., Duque, J. C. & Passos, S. Spatiotemporal modeling of urban growth using machine learning. Remote Sens. 12, 109 (2019).

    Google Scholar 

  15. Prieto-Curiel, R., Patino, J. E. & Anderson, B. Scaling of the morphology of African cities. Proc. Natl Acad. Sci. USA 120, e2214254120 (2023).

    Google Scholar 

  16. Angel, S., Franco, S. A., Liu, Y. & Blei, A. M. The shape compactness of urban footprints. Prog. Plann. 139, 100429 (2020).

    Google Scholar 

  17. Anderson, B., Prieto-Curiel, R. & Patiño-Quinchía, J. E. City Shapes and Climate Change in Africa (West African Papers, 2023).

  18. Balaian, S. K., Sanders, B. F. & Abdolhosseini, Q. M. J. How urban form impacts flooding. Nat. Commun. 15, 6911 (2024).

    Google Scholar 

  19. Ki, J. & Yoon, D. K. The impact of urban form on residential electricity consumption: panel data analyses of South Korean urban municipalities. Energy Policy 186, 113986 (2024).

    Google Scholar 

  20. Depersin, J. & Barthelemy, M. From global scaling to the dynamics of individual cities. Proc. Natl Acad. Sci. USA 115, 2317–2322 (2018).

    Google Scholar 

  21. Tusting, L. S. et al. Mapping changes in housing in Sub-Saharan Africa from 2000 to 2015. Nature 568, 391–394 (2019).

  22. Sirko, W. et al. Continental-scale building detection from high resolution satellite imagery. Preprint at https://arxiv.org/abs/2107.12283 (2021).

  23. Muth, R. F. The spatialstructure of the housing market. In Papers of the Regional Science Association. Vol. 7, 207–220 (Springer, 1961).

  24. Nirandjan, S., Koks, E. E., Ward, P. J. & Aerts, J. C. J. H. A spatially-explicit harmonized global dataset of critical infrastructure. Sci. Data 9, 150 (2022).

    Google Scholar 

  25. Moriconi-Ebrard, F., Harre, D. & Heinrigs, P. Urbanisation Dynamics in West Africa 1950–2010 (OECD, 2015).

  26. Lemoy, R. & Caruso, G. Radial analysis and scaling of urban land use. Sci. Rep. 11, 22044 (2021).

    Google Scholar 

  27. Burgess, E. W. in The Trend of Population 212–220 (American Sociological Society, 1925).

  28. Alonso, W. A theory of the urban land market. Pap. Reg. Sci. 6, 149–157 (1960).

    Google Scholar 

  29. Iungman, T. et al. The impact of urban configuration types on urban heat islands, air pollution, CO2 emissions, and mortality in Europe: a data science approach. Lancet Planet. Health 8, e489–e505 (2024).

    Google Scholar 

  30. Kummu, M., Kosonen, M. & Masoumzadeh Sayyar, S. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Sci. Data 12, 178 (2025).

    Google Scholar 

  31. Chi, G., Fang, H., Chatterjee, S. & Blumenstock, J. E. Microestimates of wealth for all low-and middle-income countries. Proc. Natl Acad. Sci. USA 119, e2113658119 (2022).

    Google Scholar 

  32. Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) (World Health Organization and UNICEF, 2023).

  33. Sugiyama, T., Kubota, A., Sugiyama, M., Cole, R. & Owen, N. Distances walked to and from local destinations: age-related variations and implications for determining buffer sizes. J. Transport Health 15, 100621 (2019).

    Google Scholar 

  34. Coury, M., Kitagawa, T., Shertzer, A. & Turner, M. The Value of Piped Water and Sewers: Evidence from 19th Century Chicago. Working paper, wp 25-07 (Federal Reserve Bank of Philadelphia, 2025).

  35. Spatial Data Repository, the Demographic and Health Surveys Program (ICF International, 2023).

  36. 2018 Revision of World Urbanization Prospects (United Nations, Department of Economic and Social Affairs, Population Division, 2018).

  37. Pierce, G. Why is basic service access worse in slums? A synthesis of obstacles. Dev. Pract. 27, 288–300 (2017).

    Google Scholar 

  38. Guidelines for user fees and cost recovery for urban, networked water and sanitation delivery. African Development Bank https://www.afdb.org/fileadmin/uploads/afdb/Documents/Project-and-Operations/2010_11 (accessed 28 April 2025).

  39. Abramovsky, L. F. et al. Study of the Distributional Performance of Piped Water Consumption Subsidies in 10 Developing Countries (World Bank Policy Research Working Paper, 2020).

  40. Utility Tariffs Dataset (IBNET tariffs db) (International Benchmarking Network for Water and Sanitation Utilities, 2023).

  41. Rodríguez-Tapia, L., Revollo-Fernández, D. A. & Morales-Novelo, J. A. Household’s perception of water quality and willingness to pay for clean water in Mexico City. Economies 5, 12 (2017).

    Google Scholar 

  42. Ravenscroft, P. & Lytton, L. Seeing the Invisible (World Bank, 2022).

  43. World Bank. High and Dry: Climate Change, Water, and the Economy (World Bank, 2016).

  44. Rusca, M., Alda-Vidal, C., Hordijk, M. & Kral, N. Bathing without water, and other stories of everyday hygiene practices and risk perception in urban low-income areas: the case of Lilongwe, Malawi. Environ. Urban. 29, 533–550 (2017).

    Google Scholar 

  45. Hasan, S. M. et al. Healthcare systems strengthening in smaller cities in Bangladesh: geospatial insights from the municipality of Dinajpur. Health Serv. Insights 13, 1178632920951586 (2020).

    Google Scholar 

  46. Hoornweg, D. & Pope, K. Population predictions for the world’s largest cities in the 21st century. Environ. Urban. 29, 195–216 (2017).

    Google Scholar 

  47. Rathore, L. S., Kumar, M., Hanasaki, N., Mekonnen, M. M. & Raghav, P. Water scarcity challenges across urban regions with expanding irrigation. Environ. Res. Lett. 19, 014065 (2024).

    Google Scholar 

  48. Germain Abwe, F. & Daniel, K. Local public markets infrastructure and urban food systems in African small cities: insights from Arusha, Tanzania. Cities Health 5, S127–S130 (2021).

    Google Scholar 

  49. Guan, C. H. et al. Evaluating the impact of water protection policy on urban growth: a case study of Jiaxing. Environ. Plann. B 50, 1000–1019 (2023).

    Google Scholar 

  50. Behnisch, M., Krüger, T. & Jaeger, JochenA. G. Rapid rise in urban sprawl: global hotspots and trends since 1990. PLoS Sustain. Transform. 1, e0000034 (2022).

    Google Scholar 

  51. Islam, S. M. F. & Karim, Z. in Desalination Challenges and Opportunities (eds Farahani, M. H. D. A. et al.) Ch. 4 (OECD, 2019).

  52. Hanoon, S. K., Abdullah, A. F., Shafri, H. Z. M. & Wayayok, A. Using scenario modelling for adapting to urbanization and water scarcity: towards a sustainable city in semi-arid areas. Period. Eng. Nat. Sci. 10, 518 (2022).

    Google Scholar 

  53. Athey, S. Beyond prediction: using big data for policy problems. Science 355, 483–485 (2017).

    Google Scholar 

  54. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. Open Street Map https://www.openstreetmap.org (2021).

  55. Esch, T. et al. World Settlement Footprint 3D-a first three-dimensional survey of the global building stock. Remote Sens. Environ. 270, 112877 (2022).

    Google Scholar 

  56. NASADEM Merged DEM Global 1 Arc Second V001 (NASA JPL, 2020).

  57. R: A Language and Environment for Statistical Computing (R Core Team, 2018).

  58. Arcaute, E. et al. Constructing cities, deconstructing scaling laws. J. R. Soc. Interface 12, 20140745 (2015).

    Google Scholar 

  59. Rozenfeld, H. D. et al. Laws of population growth. Proc. Natl Acad. Sci. USA 105, 18702–18707 (2008).

    Google Scholar 

  60. Cottineau, C., Hatna, E., Arcaute, E. & Batty, M. Diverse cities or the systematic paradox of urban scaling laws. Comput. Environ. Urban Syst. 63, 80–94 (2017).

    Google Scholar 

  61. Rozenblat, C. Extending the concept of city for delineating large urban regions (LUR) for the cities of the world. Cybergeo Eur. J. Geogr. 954, (2020).

  62. Lall, S., Lebrand, M., Park, H., Sturm, D. & Venables, A. Pancakes to pyramids. World Bank Group (2021).

  63. Batik, T. et al. What is the best shape of a city. Preprint at https://arxiv.org/abs/2507.00100 (2025).

  64. Sartirano, D. et al. Strengths and limitations of relative wealth indices derived from big data in Indonesia. Front. Big Data 6, 10 (2023).

    Google Scholar 

  65. Gridded Population of the World, Version 4 (gpwv4): Population Count (Center for International Earth Science Information Network CIESIN, 2018).

  66. Niva, V. et al. World’s human migration patterns in 2000–2019 unveiled by high-resolution data. Nat. Hum. Behav. 1, 1–15 (2023).

    Google Scholar 

  67. Prieto-Curiel, R., Heo, I., Schumann, A. & Heinrigs, P. Constructing a simplified interurban road network based on crowdsourced geodata. MethodsX 9, 101845 (2022).

    Google Scholar 

  68. Hewko, J., Smoyer-Tomic, K. E. & Hodgson, M. J. Measuring neighbourhood spatial accessibility to urban amenities: does aggregation error matter? Environ. Plann. A 34, 1185–1206 (2002).

    Google Scholar 

  69. Aqueduct Global Maps 4.0: Baseline water stress. World Resources Institute https://www.wri.org/data/aqueduct-global-maps-40-data (accessed July 2025).

  70. Fick, S. E. & Hijmans, R. J. Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Google Scholar 

Download references

Acknowledgements

We thank L. Yang at the Complexity Science Hub for creating the visualizations for this project, available at vis.csh.ac.at/urban-thirst/. R.P.-C. is funded by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (grant no. 2021-0.664.668) and by the Austrian Ministry for Innovation, Mobility and Infrastructure (grant no. GZ 2023-0.841.266).

Author information

Authors and Affiliations

  1. Complexity Science Hub, Vienna, Austria

    Rafael Prieto-Curiel

  2. World Bank, Washington, DC, USA

    Pavel Luengas-Sierra & Christian Borja-Vega

Authors
  1. Rafael Prieto-Curiel
    View author publications

    Search author on:PubMed Google Scholar

  2. Pavel Luengas-Sierra
    View author publications

    Search author on:PubMed Google Scholar

  3. Christian Borja-Vega
    View author publications

    Search author on:PubMed Google Scholar

Contributions

R.P.-C. conceived the study, designed the methodology, analysed the results and wrote the manuscript. P.L.-S. compiled the data, analysed the results and wrote the manuscript. C.B.-V. conceived the study, analysed the results and wrote the manuscript.

Corresponding author

Correspondence to Rafael Prieto-Curiel.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cities thanks Gregory Pierce and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Tables 1–11, Notes 1–11, discussions and references.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prieto-Curiel, R., Luengas-Sierra, P. & Borja-Vega, C. Urban sprawl is associated with reduced access and increased costs of water and sanitation. Nat Cities 2, 1148–1159 (2025). https://doi.org/10.1038/s44284-025-00338-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44284-025-00338-3

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing