Dynamic population mapping using mobile phone data

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.O ur knowledge of human population numbers and distribution for many areas of the world remains poor (1) despite their importance for policy (2, 3), operational decisions (4), and research (5-7) across many fields. In the 1990s, a growing interest in the global mapping of human populations emerged (8, 9), leading to the advanced development of methodologies that undertake the spatial downscaling of human population count data from censuses summarized over large and irregular administrative units to grid squares of 100 m to 5 km resolution (10-16). Initial efforts to downscale these data used simple areal weighting methods (10, 17) or dasymetric modeling approaches (13-15), which use ancillary layers to redistribute population counts within administrative units (18). Modeling techniques that spatially downscale population numbers into gridded datasets continue to be refined, with basic dasymetric models increasing in sophistication, incorporating multiscale remotely sensed and geospatial data and making improvements in the type of statistical algorithms used in the modeling process (19)(20)(21). These detailed population databases have proven crucial for studies reliant on information about human population distributions, typically for calculating populations at risk for human or natural disasters (22-24), to assess vulnerabilities (7, 25), or to derive hea...

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