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Choosing the Right Home Location Definition Method for the Given Dataset

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Social Informatics (SocInfo 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9471))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Iva Bojic, Emanuele Massaro, Stanislav Sobolevsky & Carlo Ratti

  2. SMART Centre, Singapore, Singapore

    Iva Bojic & Alexander Belyi

Authors
  1. Iva Bojic
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  2. Emanuele Massaro
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  3. Alexander Belyi
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  4. Stanislav Sobolevsky
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  5. Carlo Ratti
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Corresponding author

Correspondence to Iva Bojic .

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Editors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Tie-Yan Liu

  2. Singapore Management University, Singapore, Singapore

    Christie Napa Scollon

  3. Tsinghua University, Beijing, China

    Wenwu Zhu

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Bojic, I., Massaro, E., Belyi, A., Sobolevsky, S., Ratti, C. (2015). Choosing the Right Home Location Definition Method for the Given Dataset. In: Liu, TY., Scollon, C., Zhu, W. (eds) Social Informatics. SocInfo 2015. Lecture Notes in Computer Science(), vol 9471. Springer, Cham. https://doi.org/10.1007/978-3-319-27433-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-27433-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27432-4

  • Online ISBN: 978-3-319-27433-1

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