Analysis of GSM calls data for understanding user mobility behavior

Barbara Furletti · Lorenzo Gabrielli · Chiara Renso · Salvatore Rinzivillo

25 citations0/0 files storedIEEE International Conference on Big Data (BigData)

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Human Mobility and Location-Based Analysis Privacy-Preserving Technologies in Data Data Management and Algorithms

Abstract

This information about our GSM calls is stored by the TelCo operator in large volumes and with strict privacy constraints making it challenging the analysis of these fingerprints for inferring mobility behavior. This paper proposes a strategy for mobility behavior identification based on aggregated calling profiles of mobile phone users. This compact representation of the user call profiles is the input of the mining algorithm for automatically classifying various kinds of mobility behavior. A further advantage of having defined the call profiles is that the analysis phase is based on summarized privacy-preserving representation of the original data. We show how these call profiles permit to design a two step process - implemented into a system - based on a bootstrap phase and a running phase for classifying users into behavior categories. We evaluated the system in two case studies where individuals are classified into residents, commuters and visitors. We conclude the paper with a discussion which emphasizes the role of the call profiles for the design of a new collaboration model between data provider and data analyst.

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References

2010Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome 2010Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones 2007Mobile Landscapes: Graz in Real Time 2010Generating Trajectories from Mobile Phone Data 2012Identifying users profiles from mobile calls habits

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