• DocumentCode
    1826532
  • Title

    Human mobility and predictability enriched by social phenomena information

  • Author

    Ponieman, Nicolas B. ; Salles, Alejo ; Sarraute, Carlos

  • Author_Institution
    Grandata Labs., Argentina
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Firstpage
    1331
  • Lastpage
    1336
  • Abstract
    The massive amounts of geolocation data collected from mobile phone records has sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, focusing in particular in the cases of urban commute and major sports events. We illustrate how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user´s location.
  • Keywords
    geographic information systems; mobile computing; mobile handsets; geolocation data; human mobility; human predictability; major sports events; mobile phone data; mobile phone records; mobile phone user location; social phenomena information; urban commute; Mobile communication;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
  • Conference_Location
    Niagara Falls, ON
  • Type

    conf

  • Filename
    6785874