A survey of results on mobile phone datasets analysis

Abstract

In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.

Characterizing and predicting mobile application usage

“…The main difference between our work using Wi-Fi cloud data and other works based on cellular mobile data, is that our dataset corresponds to a local scale network (e.g., restaurants, malls etc. ), whereas datasets from cellular mobile networks focus on larger aggregation scales (wide area, metropolitan, nationwide) -see [11] and [5] for surveys. Consequently, we expect that our dataset can capture other kinds of mobile usage behavior.…”

Dynamic, Interactive and Visual Analysis of Population Distribution and Mobility Dynamics in an Urban Environment Using the Mobility Explorer Framework

“…The abovementioned numbers prove the satisfactory representativeness of the dataset to describe the entire population distribution dynamics, assuming that the mobile device distribution and motion dynamics patterns of large mobile communication companies matches quite well with the population distribution and motion dynamics patterns (see also Section 7). Wesolowski et al [24], Frias-Martinez et al [25] and Blondel et al (2015) [19] have observed that differential mobile ownership biases do not seem to have much effect on mobility patterns within particular regions. This suggests that the social bias of the clients of certain providers is a diminishing issue mainly because of highly competitive mobile services market and attractive packages.…”

“…Among others, a good overview of the results of mobile phone datasets is presented in Blondel et al [19], where the authors question the suitability of the mobile data, e.g., they argue that datasets are noisy and some links appear there by chance, while others have not been captured. It would thus be interesting to question the stability of the obtained results, provided that the real network is different from what has been observed in the data.…”

“…This would also require support of dynamically scaling systems like cloud computing to accommodate processing and storage demands at peak times. Another interesting research angle would be to combine mobile phone data with social network information to increase the reliability and information density of the resulting combined datasets [19]. Provides a survey of CDR used for exploratory social network analysis or mobile communities formed based on call records.…”

Transactional information is remarkably revelatory

“…As a result of always being with their owners, mobile phones provide detailed maps of users' lives and have become a valuable tool in computational social science research (4). Data from phones-specifically where people are-is being used to map people's movements during a disaster (5), to learn how to halt the spread of infectious diseases (6), and so forth.…”