Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 254-258 | Cite as

Visual Analytics Methodology for Scalable and Privacy-Respectful Discovery of Place Semantics from Episodic Mobility Data

  • Natalia Andrienko
  • Gennady Andrienko
  • Georg Fuchs
  • Piotr Jankowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)

Abstract

People using mobile devices for making phone calls, accessing the internet, or posting georeferenced contents in social media create episodic digital traces of their presence in various places. Availability of personal traces over a long time period makes it possible to detect repeatedly visited places and identify them as home, work, place of social activities, etc. based on temporal patterns of the person’s presence. Such analysis, however, can compromise personal privacy. We propose a visual analytics approach to semantic analysis of mobility data in which traces of a large number of people are processed simultaneously without accessing individual-level data. After extracting personal places and identifying their meanings in this privacy-respectful manner, the original georeferenced data are transformed to trajectories in an abstract semantic space. The semantically abstracted data can be further analyzed without the risk of re-identifying people based on the specific places they attend.

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References

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Natalia Andrienko
    • 1
    • 2
  • Gennady Andrienko
    • 1
    • 2
  • Georg Fuchs
    • 1
  • Piotr Jankowski
    • 3
    • 4
  1. 1.Fraunhofer Institute IAISSankt AugustinGermany
  2. 2.City University LondonLondonUK
  3. 3.San Diego State UniversitySan DiegoUSA
  4. 4.Institute of Geoecology and GeoinformationAdam Mickiewicz UniversityPoznanPoland

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