Mobile phone call data as a regional socio-economic proxy indicator
Journal
PloS one
Date Issued
2015-04-21
Author(s)
Šćepanović, Sanja
Hui, Pan
Nurminen, Jukka K
Ylä-Jääski, Antti
Abstract
The advent of publishing anonymized call detail records opens the door for temporal and
spatial human dynamics studies. Such studies, besides being useful for creating universal
models for mobility patterns, could be also used for creating new socio-economic proxy indicators that will not rely only on the local or state institutions. In this paper, from the frequency
of calls at different times of the day, in different small regional units (sub-prefectures) in
Côte d'Ivoire, we infer users' home and work sub-prefectures. This division of users enables
us to analyze different mobility and calling patterns for the different regions. We then compare how those patterns correlate to the data from other sources, such as: news for particular events in the given period, census data, economic activity, poverty index, power plants
and energy grid data. Our results show high correlation in many of the cases revealing the
diversity of socio-economic insights that can be inferred using only mobile phone call data.
The methods and the results may be particularly relevant to policy-makers engaged in poverty reduction initiatives as they can provide an affordable tool in the context of resourceconstrained developing economies, such as Côte d'Ivoire's.
spatial human dynamics studies. Such studies, besides being useful for creating universal
models for mobility patterns, could be also used for creating new socio-economic proxy indicators that will not rely only on the local or state institutions. In this paper, from the frequency
of calls at different times of the day, in different small regional units (sub-prefectures) in
Côte d'Ivoire, we infer users' home and work sub-prefectures. This division of users enables
us to analyze different mobility and calling patterns for the different regions. We then compare how those patterns correlate to the data from other sources, such as: news for particular events in the given period, census data, economic activity, poverty index, power plants
and energy grid data. Our results show high correlation in many of the cases revealing the
diversity of socio-economic insights that can be inferred using only mobile phone call data.
The methods and the results may be particularly relevant to policy-makers engaged in poverty reduction initiatives as they can provide an affordable tool in the context of resourceconstrained developing economies, such as Côte d'Ivoire's.
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