A method to estimate population densities and electricity consumption from mobile phone data in developing countries.
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 informati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0235224 |
| _version_ | 1818833742181957632 |
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| author | Hadrien Salat Zbigniew Smoreda Markus Schläpfer |
| author_facet | Hadrien Salat Zbigniew Smoreda Markus Schläpfer |
| author_sort | Hadrien Salat |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T02:23:45Z |
| format | Article |
| id | doaj.art-5166ac810860481290289797543d081c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T02:23:45Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5166ac810860481290289797543d081c2022-12-21T20:40:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01156e023522410.1371/journal.pone.0235224A method to estimate population densities and electricity consumption from mobile phone data in developing countries.Hadrien SalatZbigniew SmoredaMarkus SchläpferHigh 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.https://doi.org/10.1371/journal.pone.0235224 |
| spellingShingle | Hadrien Salat Zbigniew Smoreda Markus Schläpfer A method to estimate population densities and electricity consumption from mobile phone data in developing countries. PLoS ONE |
| title | A method to estimate population densities and electricity consumption from mobile phone data in developing countries. |
| title_full | A method to estimate population densities and electricity consumption from mobile phone data in developing countries. |
| title_fullStr | A method to estimate population densities and electricity consumption from mobile phone data in developing countries. |
| title_full_unstemmed | A method to estimate population densities and electricity consumption from mobile phone data in developing countries. |
| title_short | A method to estimate population densities and electricity consumption from mobile phone data in developing countries. |
| title_sort | method to estimate population densities and electricity consumption from mobile phone data in developing countries |
| url | https://doi.org/10.1371/journal.pone.0235224 |
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