Identifying Important Places in People's Lives from Cellular Network Data

By Sibren Isaacman, Richard A. Becker, Ramón Cáceres, Stephen G. Kobourov, Margaret Martonosi, James R. Rowland, Alex Varshavsky. International Conference on Pervasive Computing, 2011

423 citations recorded by the source corpus.

Subfields: Epidemiology, Transportation

View Paper: https://www.semanticscholar.org/paper/031bc61d0f6068da01235364a43e9fb1ca7f9866

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Citations in the corpus

Human mobility modeling at metropolitan scales (2012, methodology): “A growing body of work has shown that information derived from anonymized CDRs can accurately characterize many aspects of human mobility [3, 11, 13, 16, 17, 18, 31].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, methodology): “Distance between the most two distant visited locations has been used in (Isaacman et al., 2011) as a measure of daily range of mobility.”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, background): “If timing of the call activity is provided, one possible refined definition would be to identify as home location the tower cell with the largest activity during nighttime, and the work location as the one with the largest activity constrained to daytime (with variations of the definition of these i”
A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015, background): “…, estimates population densities 8,9 , models cities structures 10 , predicts socio-economic indicators and outcomes of territories 11 – 13 , and models the spread of diseases 10,14 – 17 (See Blondel et al. 18 for a comprehensive review of recent advances in studies using mobile phone datasets).”
DP-WHERE: Differentially private modeling of human mobility (2013, methodology): “We further used this information to characterize commute distances, to quantify carbon footprints, and to create WHERE models [16, 19].”
The Mobile Data Challenge: Big Data for Mobile Computing Research (2012, background): “Inferring the meaning of the most signiﬁcant places that a user visits frequently is a relevant problem in mobile computing [13].”