Dynamic population mapping using mobile phone data

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 111; no. 45; pp. 15888 - 15893
• Main Authors: Deville, Pierre, Linard, Catherine, Martin, Samuel, Gilbert, Marius, Stevens, Forrest R., Gaughan, Andrea E., Blondel, Vincent D., Tatem, Andrew J.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 11.11.2014; National Acad Sciences
• Subjects: Anonymity; Automatic; Cell Phone; Cell phones; Cellular telephones; Censuses; Datasets; Density estimation; Engineering Sciences; Estimate reliability; Estimation methods; Female; France; Geodemographics; Humans; Male; Mapping; Models, Theoretical; Physical Sciences; Population; Population density; Population distributions; Population Dynamics; Population estimates; Portugal; Remote sensing; Social Sciences; France; Portugal; phone calls; population distribution; remote sensing; human mobility; census
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1408439111

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.