Open AccessA method to estimate population densities and electricity consumption from mobile phone data in developing countries
Author(s) -
Hadrien Salat,
Zbigniew Smoreda,
Markus Schläpfer
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0235224
Subject(s) - mobile phone , phone , computer science , population , proxy (statistics) , context (archaeology) , consumption (sociology) , data collection , data quality , business , geography , telecommunications , statistics , marketing , environmental health , service (business) , machine learning , social science , philosophy , linguistics , mathematics , archaeology , sociology , medicine
High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a popular proxy to evaluate the density, activity and social characteristics of a population. They offer additional advantages: they are updated in real-time, include mobility information and record visitors’ activity. However, we show with the example of Senegal that the direct correlation between the average phone activity and both the population density and the nighttime lights intensity may be insufficiently high to provide an accurate representation of the situation. There are reasons to expect this, such as the heterogeneity of the market share or the particular granularity of the distribution of cell towers. In contrast, we present a method based on the daily, weekly and yearly phone activity curves and on the network characteristics of the mobile phone data, that allows to estimate more accurately such information without compromising people’s privacy. This information can be vital for development and infrastructure planning. In particular, this method could help to reduce significantly the logistic costs of data collection in the particularly budget-constrained context of developing countries.