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PERVASIVE
2011
Springer

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

7 years 9 months ago
Identifying Important Places in People's Lives from Cellular Network Data
People spend most of their time at a few key locations, such as home and work. Being able to identify how the movements of people cluster around these “important places” is crucial for a range of technology and policy decisions in areas such as telecommunications and transportation infrastructure deployment. In this paper, we propose new techniques based on clustering and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically meaningful locations such as home and work. Starting with temporally sparse and spatially coarse location information, we propose a new algorithm to identify important locations. We test this algorithm on arbitrary cellphone users, including those with low call rates, and find that we are within 3 miles of ground truth for 88% of volunteer users. Further, after locating home and work, we achieve commute distance estimates that are within 1 mile of equivalent estimates derived from gover...
Sibren Isaacman, Richard Becker, Ramón C&aa
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where PERVASIVE
Authors Sibren Isaacman, Richard Becker, Ramón Cáceres, Stephen G. Kobourov, Margaret Martonosi, James Rowland, Alexander Varshavsky
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