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

  • Sibren Isaacman
  • Richard Becker
  • Ramón Cáceres
  • Stephen Kobourov
  • Margaret Martonosi
  • James Rowland
  • Alexander Varshavsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6696)

Abstract

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 government census data. Finally, we perform carbon footprint analyses on hundreds of thousands of anonymous users as an example of how our data and algorithms can form an accurate and efficient underpinning for policy and infrastructure studies.

Keywords

Carbon Footprint Important Place Work Location Human Mobility Important Location 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sibren Isaacman
    • 1
  • Richard Becker
    • 2
  • Ramón Cáceres
    • 2
  • Stephen Kobourov
    • 3
  • Margaret Martonosi
    • 1
  • James Rowland
    • 2
  • Alexander Varshavsky
    • 2
  1. 1.Dept. of Electrical EngineeringPrinceton UniversityPrincetonUSA
  2. 2.AT&T Labs – ResearchFlorham ParkUSA
  3. 3.Dept. of Computer ScienceUniversity of ArizonaTucsonUSA

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