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Visual analytics methodology for scalable and privacy-respectful discovery of place semantics from episodic mobility data

: Andrienko, N.; Andrienko, G.; Fuchs, G.; Jankowski, P.


Appice, A.:
Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2015. Pt.3 : Porto, Portugal, September 7-11, 2015; Proceedings
Cham: Springer International Publishing, 2015 (Lecture Notes in Computer Science 9286)
ISBN: 978-3-319-23460-1 (Print)
ISBN: 978-3-319-23461-8 (Online)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2015, Porto>
Fraunhofer IAIS ()

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.