Abstract

The economic and man-made resources that sustain human wellbeing are not distributed evenly across the world, but are instead heavily concentrated in cities. Poor access to opportunities and services offered by urban centres (a function of distance, transport infrastructure, and the spatial distribution of cities) is a major barrier to improved livelihoods and overall development. Advancing accessibility worldwide underpins the equity agenda of ‘leaving no one behind’ established by the Sustainable Development Goals of the United Nations1. This has renewed international efforts to accurately measure accessibility and generate a metric that can inform the design and implementation of development policies. The only previous attempt to reliably map accessibility worldwide, which was published nearly a decade ago2, predated the baseline for the Sustainable Development Goals and excluded the recent expansion in infrastructure networks, particularly in lower-resource settings. In parallel, new data sources provided by Open Street Map and Google now capture transportation networks with unprecedented detail and precision. Here we develop and validate a map that quantifies travel time to cities for 2015 at a spatial resolution of approximately one by one kilometre by integrating ten global-scale surfaces that characterize factors affecting human movement rates and 13,840 high-density urban centres within an established geospatial-modelling framework. Our results highlight disparities in accessibility relative to wealth as 50.9% of individuals living in low-income settings (concentrated in sub-Saharan Africa) reside within an hour of a city compared to 90.7% of individuals in high-income settings. By further triangulating this map against socioeconomic datasets, we demonstrate how access to urban centres stratifies the economic, educational, and health status of humanity.

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Acknowledgements

We thank J. C. Alonso, G. Pacheco, E. Brett, A. Arenzana, and B. A. Laken for the development of http://roadlessforest.eu/map.html and D. Pfeffer and K. Twohig for formatting of the figures and references. Funding was provided by a Google Earth Engine Research Award entitled “Developing and validating an online accessibility mapping tool powered by Google Earth Engine” and the “Roadless Forest” project “Making efficient use of EU climate finance: Using roads as an early performance indicator for REDD+ projects” (European Parliament/European Commission Directorate General for Climate Action).

Author information

Affiliations

  1. Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford OX3 7FY, UK

    • D. J. Weiss
    • , H. S. Gibson
    • , U. Dalrymple
    • , J. Rozier
    • , T. C. D. Lucas
    • , R. E. Howes
    • , L. S. Tusting
    • , S. Y. Kang
    • , E. Cameron
    • , D. Bisanzio
    • , K. E. Battle
    •  & P. W. Gething
  2. Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands

    • A. Nelson
  3. European Commission, Joint Research Centre, Unit D6 Knowledge for Sustainable Development and Food Security, Via Fermi 2749, Ispra 21027, Varese, Italy

    • W. Temperley
    •  & S. Peedell
  4. Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, USA

    • A. Lieber
    • , M. Hancher
    •  & E. Poyart
  5. Vizzuality, Office D, Dales Brewery, Gwydir Street, Cambridge CB1 2LJ, UK

    • S. Belchior
  6. Institute for Health Metrics and Evaluation, University of Washington, 2301 5th Avenue, Suite 600, Seattle, Washington 98121, USA

    • N. Fullman
  7. Centre for Biodiversity and Conservation Science, School of Biological Sciences, University of Queensland, St Lucia, Queensland 4072, Australia

    • B. Mappin
  8. Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK

    • S. Bhatt

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Contributions

D.J.W., A.N., S.P. and P.W.G. came up with the research concept and designed experiments. D.J.W. drafted the manuscript. D.J.W., A.N., H.S.G., W.T., A.L., B.M. and U.D. prepared and supplied data. D.J.W. conducted the analyses. M.H. and E.P. implemented algorithms. D.J.W. and A.L. coordinated the project. A.N., H.S.G., N.F., T.C.D.L., R.E.H., K.E.B. and S.Bh. supported the analyses and interpretations. J.R. and S.Be. produced visualizations. All authors discussed the results and revised the final manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to D. J. Weiss.

Reviewer Information Nature thanks J. Birkmann and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Supplementary information

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    Life Sciences Reporting Summary

Excel files

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    Supplementary Tables

    This file contains Supplementary Tables 1-2. Supplementary Table 1, national travel times by population, shows cumulative proportion of national population by travel time, binned in 30-minute increments. Supplementary Table 2, national travel times by area, shows cumulative proportion of national land area by travel time, binned in 30-minute increments.

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DOI

https://doi.org/10.1038/nature25181

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