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The newly released Orange D4D mobile phone data base provides new insights into the use of mobile technology in a developing country.
In this article we have presented how an analysis of the Orange D4D mobile phone data base reveals important patterns of communication infrastructure and mobile phone use in Cote d'Ivoire.
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Based on large-scale mobile phone data, mobility demand was estimated and locations of vehicles were inferred in the Boston area.
The gender composition of areas at a particular time of day would allow us to further refine recent development of time-dynamic census data based on mobile phone data [10].
Our results provide insights for the long-term goal of developing a real-time event detection system based on mobile phone data.
The estimation of trajectories based on mobile phone data is a topic which has been addressed by numerous researchers in recent years.
The authors are therefore able to analyze a bigger picture than other studies based on only mobile phone data and show that only studying mobile phone data may not be enough to capture a user's comprehensive profile.
All our predictions are made solely based on behavioral features extracted from standard mobile phone data.
Based on this evidence we develop a typology of mobile phone data sources, types, and end-uses, and a decision-tree for mobile data use, designed to enable effective use of mobile data for disease disaster management.
(Right) Population density estimates from mobile phone data.
This applies to geolocation information, operational mobile phone data, and also, social network crowdsourced information.
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