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Dynamic population mapping using mobile phone data

  1. Andrew J. Tatemg,h,i
  1. aDepartment of Applied Mathematics, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium;
  2. bCenter for Complex Network Research and Physics Department, Northeastern University, Boston, MA 02115;
  3. cFonds National de la Recherche Scientifique, B-1000 Brussels, Belgium;
  4. dBiological Control and Spatial Ecology, Université Libre de Bruxelles, B-1050 Brussels, Belgium;
  5. eUniversité de Lorraine CNRS, Centre de Recherche en Automatique de Nancy, UMR 7039, 54518 Vandoeuvre-lès-Nancy, France
  6. fDepartment of Geography and Geosciences, University of Louisville, Louisville, KY 40292;
  7. gDepartment of Geography and Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom;
  8. hFogarty International Center, National Institutes of Health, Bethesda, MD 20892; and
  9. iFlowminder Foundation, 17177 Stockholm, Sweden

  1. Edited by Michael F. Goodchild, University of California, Santa Barbara, CA, and approved September 15, 2014 (received for review May 8, 2014)

Significance

Knowing where people are is critical for accurate impact assessments and intervention planning, particularly those focused on population health, food security, climate change, conflicts, and natural disasters. This study demonstrates how data collected by mobile phone network operators can cost-effectively provide accurate and detailed maps of population distribution over national scales and any time period while guaranteeing phone users’ privacy. The methods outlined may be applied to estimate human population densities in low-income countries where data on population distributions may be scarce, outdated, and unreliable, or to estimate temporal variations in population density. The work highlights how facilitating access to anonymized mobile phone data might enable fast and cheap production of population maps in emergency and data-scarce situations.

Abstract

During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.

Footnotes

  • ↵1P.D. and C.L. contributed equally to this work.

  • ↵2To whom correspondence should be addressed. Email: linard.catherine{at}gmail.com.

  • Author contributions: P.D., C.L., S.M., M.G., V.D.B., and A.J.T. designed research; P.D. and C.L. performed research; F.R.S. and A.E.G. contributed new reagents/analytic tools; P.D., C.L., and S.M. analyzed data; and P.D., C.L., M.G., and A.J.T. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1408439111/-/DCSupplemental.

Freely available online through the PNAS open access option.

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