Accessibility to Cities

Global Malaria Epidemiology

The ease with which people are able to connect with the services, institutions, and individuals supportive of socioeconomic success, good health, and overall wellbeing can ultimately separate communities that thrive from those left behind. During the last few decades, advances in electronic and online communications (e.g., internet or mobile-based banking) have transformed social services that historically have been out-of-reach for more remote populations. Nevertheless, such gains cannot fully offset the disadvantages posed by persistent inequalities in physical access to resources and opportunities that are primarily concentrated in urban centres. This perspective is widely supported by the United Nations’ Sustainable Development Goals (SDGs), which call for improved or universal access to a wide swathe of high-priority services and establishments, including educational and vocational programs, health facilities and corresponding services, and financial and banking institutions – none of which can be fully addressed by technological advances alone. As such, understanding where the largest gaps in accessibility remain both globally and locally is of critical importance to a broad range of policymakers, investors, and development partners.

In the present study, we quantify and validate global accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time. The last global mapping effort to measure accessibility was for the year 2000, a time that predates both substantial investment and expansion of transportation infrastructure and an extraordinary improvement in the data quantity and quality of accessibility measures. The game-changing improvement underpinning this work is the first-ever, global-scale synthesis of two leading roads datasets – Open Street Map (OSM) data and distance-to-roads data derived from the Google roads database – which resulted in a nearly five-fold increase in the mapped road area relative to that used to produce the circa 2000 map. A major strength of the new roads data is its inclusion of minor roads (e.g., unpaved rural roads), which comprise a large proportion of roads in many low-resource settings and were largely absent or geographically inaccurate in previous roads databases. As such, the improvements in our accessibility map are most prominent in the areas where quality data are most needed for informing sustainable development policies and actions. To illustrate the far-reaching utility of our 2015 global accessibility map, we conduct exploratory analyses that enumerate geographic and wealth-based inequities in accessibility. We also show that shorter travel times to population centres in low- to middle-income countries is strongly associated with socioeconomic and health indicators (i.e., household wealth, educational attainment, and healthcare utilization), highlighting the vital role of accessibility in the pursuit of sustainable development worldwide. Beyond the socioeconomic and health domains, this work could be used to inform environmental and conservation efforts to balance infrastructure demands with ecosystem preservation.

In summary, the present study offers a first, crucial step toward tracking exactly where gaps in accessibility remain in 2015 and where the world can collectively address the most fundamental inequalities still experienced today. With this 2015 map we offer the most up-to-date accessibility product to the scientific community and fields for which such locally resolved data on accessibility are of high demand. To date no other study offers the quality, consistency, and geographic extent provided by this analysis, all of which enable exploration of differences in accessibility across borders, regions, and resource settings.

Principal Collaborations

This work was done in collaboration with the Google, the Joint Research Centre (JRC) of the EU, and the University of Twente (Netherlands). It builds upon earlier work led by Andrew Nelson.

Additional Resources

MAP DPhil student Amelia Bertozzi-Villa has created a step-by-step Blog post giving an example on how to use malariaAtlas with our friction surface to determine travel time.

Full Citation

D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181.

Data Visualisation


Example Code for Accessibility Mapping


Creative Commons Licence
This work is licensed under a Creative Commons Attribution 4.0 International License.

Contains data from OpenStreetMap © OpenStreetMap contributors.

Related Publications

URLDOIWeiss DJ., Nelson A., Gibson HS., Temperley WH., Peedell S., Lieber A., Hancher M., Poyart E., Belchior S., Fullman N., Mappin B., Dalrymple U., Rozier J., Lucas TCD., Howes RE., Tusting LS., Kang SY., Cameron E., Bisanzio D., Battle KE., Bhatt S., Gething PW.,

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

Nature. January 2018 553: 333–336.