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A global map of travel time to cities to assess inequalities in accessibility in 2015

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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Figure 1: Global map of travel time to cities for 2015.
Figure 2: Global disparities in accessibility.
Figure 3: Relating accessibility to human wellbeing.

References

  1. United Nations. Transforming our World: The 2030 Agenda for Sustainable Development. (United Nations Department of Economic and Social Affairs, 2015)

  2. Nelson, A. Travel time to major cities: a global map of accessibility. http://forobs.jrc.ec.europa.eu/products/gam/ (Global Environment Monitoring Unit, Joint Research Centre of the European Commission, 2008)

  3. Young, A. Inequality, the urban–rural gap and migration. Q. J. Econ. 128, 1727–1785 (2013)

    Article  Google Scholar 

  4. Fotso, J.-C. Urban–rural differentials in child malnutrition: trends and socioeconomic correlates in sub-Saharan Africa. Health Place 13, 205–223 (2007)

    Article  Google Scholar 

  5. Bloom, D. E., Canning, D. & Fink, G. Urbanization and the wealth of nations. Science 319, 772–775 (2008)

    Article  CAS  ADS  Google Scholar 

  6. Frelat, R. et al. Drivers of household food availability in sub-Saharan Africa based on big data from small farms. Proc. Natl Acad. Sci. USA 113, 458–463 (2016)

    Article  CAS  ADS  Google Scholar 

  7. Pesaresi, M. & Freire, S. GHS settlement grid following the REGIO model 2014 in application to GHSL landsat and CIESIN GPW v4-multitemporal (1975–1990–2000–2015) Data Sets. http://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a (Joint Research Centre of the European Commission, 2016)

  8. Nelson, A. & Chomitz, K. M. Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6, e22722 (2011)

    Article  CAS  ADS  Google Scholar 

  9. Schmitz, C. et al. Trading more food: implications for land use, greenhouse gas emissions, and the food system. Glob. Environ. Change 22, 189–209 (2012)

    Article  Google Scholar 

  10. Gilbert, M. et al. Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nat. Commun. 5, 4116 (2014)

    Article  CAS  ADS  Google Scholar 

  11. Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211 (2015)

    Article  CAS  ADS  Google Scholar 

  12. Dijkstra, E. W. A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)

    Article  MathSciNet  Google Scholar 

  13. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017)

    Article  ADS  Google Scholar 

  14. Gaughan, A. E., Stevens, F. R., Linard, C., Jia, P. & Tatem, A. J. High resolution population distribution maps for Southeast Asia in 2010 and 2015. PLoS ONE 8, e55882 (2013)

    Article  CAS  ADS  Google Scholar 

  15. Linard, C., Gilbert, M., Snow, R. W., Noor, A. M. & Tatem, A. J. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE 7, e31743 (2012)

    Article  CAS  ADS  Google Scholar 

  16. Sorichetta, A. et al. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Sci. Data 2, 150045 (2015)

    Article  Google Scholar 

  17. Center for International Earth Science Information Network and Centro Internacional de Agricultura Tropical. Gridded Population of the World, Version 3 (GPWv3): Population Density Grids. http://dx.doi.org/10.7927/H4ST7MRB (NASA Socioeconomic Data and Applications Center, 2005)

  18. World Bank. GDP (current US$). http://data.worldbank.org/indicator/NY.GDP.MKTP.CD (2016)

  19. Center for International Earth Science Information Network, Columbia University Institute for Demographic Research, International Food Policy Research Institute, The World Bank & Centro Internacional de Agricultura Tropical. Global Rural–Urban Mapping Project, Version 1 (GRUMPv1): Settlement Points Revision 01. https://doi.org/10.7927/H4BC3WG1 (NASA Socioeconomic Data and Applications Center, 2016)

  20. United Nations. World Urbanization Prospects: The 2014 Revision, Highlights (United Nations Department of Economic and Social Affairs, 2014)

  21. Allan, J. R. et al. Recent increases in human pressure and forest loss threaten many Natural World Heritage Sites. Biol. Conserv. 206, 47–55 (2017)

    Article  Google Scholar 

  22. Ibisch, P. L. et al. A global map of roadless areas and their conservation status. Science 354, 1423–1427 (2016)

    Article  CAS  ADS  Google Scholar 

  23. Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232 (2014)

    Article  CAS  ADS  Google Scholar 

  24. Laurance, W. F. & Arrea, I. B. Roads to riches or ruin? Science 358, 442–444 (2017)

    Article  CAS  ADS  Google Scholar 

  25. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013)

    Article  CAS  ADS  Google Scholar 

  26. Central Intelligence Agency, Office of Geographic and Cartographic Research. World Data Bank II: North America, South America, Europe, Africa, Asia. https://doi.org/10.3886/ICPSR08376.v1 (Inter-university Consortium for Political and Social Research, 2000)

  27. Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos (Washington DC) 89, 93–94 (2008)

    ADS  Google Scholar 

  28. National Imagery and Mapping Agency. Vector Map Level 0 (VMAP0). http://www.mapability.com/info/vmap0_download.html (mapAbility, 1997)

  29. Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016)

    Article  CAS  ADS  Google Scholar 

  30. Walbridge, S. Assessing Ship Movements using Volunteered Geographic Information, MA Thesis, Univ. California, Santa Barbara, (2013)

  31. Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010)

    Article  ADS  Google Scholar 

  32. Danielson, J. J. & Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010). https://explorer.earthengine.google.com/#detail/USGS%2FGMTED2010 (US Geological Survey, 2011)

  33. Wehrlin, J. P. & Hallén, J. Linear decrease in. VO2max and performance with increasing altitude in endurance athletes. Eur. J. Appl. Physiol. 96, 404–412 (2006)

    Article  Google Scholar 

  34. Tobler, W. Three Presentations on Geographical Analysis and Modeling: Non- Isotropic Geographic Modeling; Speculations on the Geometry of Geography; and Global Spatial Analysis. Technical Report 93-1. (National Center for Geographic Information and Analysis, 1993)

  35. Google Earth Engine Developers. Cumulative Cost Mapping. https://developers.google.com/earth-engine/image_cumulative_cost (2017)

  36. van Etten, J. R package gdistance: distances and routes on geographical grids. J. Stat. Softw. 76, 1–21 (2017)

    Article  ADS  Google Scholar 

  37. Chambers, J. M ., Cleveland, W. S ., Kleiner, B. & Tukey, P. A. Graphical Methods for Data Analysis (Wadsworth International Group, 1983)

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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

Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to D. J. Weiss.

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Competing interests

The authors declare no competing financial interests.

Additional information

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 figures and tables

Extended Data Figure 1 Accessibility and forest loss in Brazil.

a, b, Maps of travel time to urban centres (a) and forest loss (b) from 2000 to 2015. Forest loss is defined as the fraction of land area identified as forest in 2000 that experienced any loss in forest density (but not necessarily total removal) by 2015.

Extended Data Figure 2 Accessibility and forest loss in Indonesia.

a, b, Maps of travel time to urban centres (a) and forest loss (b) from 2000 to 2015. Forest loss is defined as the fraction of land area identified as forest in 2000 that experienced any loss in forest density (but not necessarily total removal) by 2015.

Extended Data Figure 3 Forest loss relative to accessibility.

a, b, The distribution of the population and land area by accessibility category in Brazil (a) and Indonesia (b). c, The percentage of area that experienced any loss in forest density since 2000 for each country and the global average.

Extended Data Figure 4 Travel time relative to percentage of urban population.

Mean national accessibility for countries with populations over ten million relative to the percentage of urban population as estimated by the UN, colour-coded by World Bank income category.

Supplementary information

Life Sciences Reporting Summary (PDF 72 kb)

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. (XLS 239 kb)

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Weiss, D., Nelson, A., Gibson, H. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018). https://doi.org/10.1038/nature25181

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