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.

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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).

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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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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.

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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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