Dynamic population mapping using mobile phone data
Dynamic population mapping using mobile phone data
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.
15888-15893
Deville, P.
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Linard, C.
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Martin, S.
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Gilbert, M.
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Stevens, F.R.
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Gaughan, A.E.
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Blondel, V.D.
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Tatem, A.J.
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11 November 2014
Deville, P.
e13cc888-49ba-4af7-b3d6-8623f996eb4e
Linard, C.
40dc396f-bbf0-4ae2-8732-7a73447a9100
Martin, S.
638d43ca-2c18-49a1-886c-8cc4252361e0
Gilbert, M.
1783ad6f-c32e-46dd-8de0-eb0e139afce0
Stevens, F.R.
83fd6663-d8f2-4db5-bc71-c7961dc5218e
Gaughan, A.E.
46808c1b-4c19-4342-b628-8ad7251d9f90
Blondel, V.D.
7d6e866e-1ce6-4407-93f2-c9d40f745c41
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D. and Tatem, A.J.
(2014)
Dynamic population mapping using mobile phone data.
Proceedings of the National Academy of Sciences, 111 (45), .
(doi:10.1073/pnas.1408439111).
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.
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More information
Accepted/In Press date: 15 September 2014
e-pub ahead of print date: 27 October 2014
Published date: 11 November 2014
Organisations:
Global Env Change & Earth Observation, WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 370705
URI: http://eprints.soton.ac.uk/id/eprint/370705
ISSN: 0027-8424
PURE UUID: a48e6731-d4e9-4156-ad89-b79ac0fe8b8f
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Date deposited: 04 Nov 2014 11:52
Last modified: 15 Mar 2024 03:43
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Contributors
Author:
P. Deville
Author:
C. Linard
Author:
S. Martin
Author:
M. Gilbert
Author:
F.R. Stevens
Author:
A.E. Gaughan
Author:
V.D. Blondel
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