Choosing the Right Home Location Definition Method for the Given Dataset

  • Iva BojicEmail author
  • Emanuele Massaro
  • Alexander Belyi
  • Stanislav Sobolevsky
  • Carlo Ratti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9471)


Ever since first mobile phones equipped with Global Position System (GPS) came to the market, knowing the exact user location has become a holy grail of almost every service that lives in the digital world. Starting with the idea of location based services, nowadays it is not only important to know where users are in real time, but also to be able predict where they will be in future. Moreover, it is not enough to know user location in form of latitude longitude coordinates provided by GPS devices, but also to give a place its meaning (i.e., semantically label it), in particular detecting the most probable home location for the given user. The aim of this paper is to provide novel insights on differences among the ways how different types of human digital trails represent the actual mobility patterns and therefore the differences between the approaches interpreting those trails for inferring said patterns. Namely, with the emergence of different digital sources that provide information about user mobility, it is of vital importance to fully understand that not all of them capture exactly the same picture. With that being said, in this paper we start from an example showing how human mobility patterns described by means of radius of gyration are different for Flickr social network and dataset of bank card transactions. Rather than capturing human movements closer to their homes, Flickr more often reveals people travel mode. Consequently, home location inferring methods used in both cases cannot be the same. We consider several methods for home location definition known from the literature and demonstrate that although for bank card transactions they provide highly consistent results, home location definition detection methods applied to Flickr dataset happen to be way more sensitive to the method selected, stressing the paramount importance of adjusting the method to the specific dataset being used.


Mobility Pattern Home Location Human Mobility Twitter User Mobile Phone Data 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Iva Bojic
    • 1
    • 2
    Email author
  • Emanuele Massaro
    • 1
  • Alexander Belyi
    • 2
  • Stanislav Sobolevsky
    • 1
  • Carlo Ratti
    • 1
  1. 1.Senseable City LabMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.SMART CentreSingaporeSingapore

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