3 Key Takeaways from Mobile Tartu 2018

The biennial Mobile Tartu conference took place from June 27–June 29 in Tartu, Estonia. The international conference brought together participants from all over the world. The three-day event kicked off with an activity-packed PhD seminar at Mooste Manor where the participants got to know one another and enjoy the first sessions of the conference. The second day took us back to Tartu, to the open space of Omicum, where the sessions continued on the wide range of smart ways to use mobile data to learn more about people and their mobility. The third and final day wrapped the event up with mobile positioning data (MPD) use cases from Positium LBS and explored using this type of data for tourism statistics.

This year’s event was held in memory of Prof. Rein Ahas – the heart and soul of the University of Tartu Mobility Lab and a founding father of Positium LBS. Organising this event for mobility and mobile data professionals was a passion for him, and the community Rein had built over the years gathered to commemorate his legacy. A true visionary and scholar as well as an inspirational human being – he is truly missed.

The conference touched on several issues currently topical in mobile location data research. Speakers also demonstrated fascinating use cases and their latest research in the field. The ideas shared sparked many a fruitful discussion both during and after the sessions.

Here are our three key takeaways from the Mobile Tartu 2018 conference:

  1. Sharing is caring. Every Mobile Tartu proves that many researchers globally have access to MPD, yet they almost always start to build methods from scratch, making it difficult to compare examples from different countries. The presentations demonstrated interesting approaches, some of which have already been tested successfully.
  2. Call for standards. There are no standard benchmark datasets for mobility. Mobile Big Data aptly described by volume, variety, veracity and velocity is an asset to mobility studies. To make the data used by researchers comparable there should be a base level of ETL (Extract, Transform, Load) practices for MPD.
  3. Privacy, privacy, privacy. Analysing large-scale mobility data is now quite ubiquitous over different research teams. New data legislation in Europe with the General Data Protection Regulation has created the need for taking measures to protect the privacy of individuals. On the other hand, in the research domain, privacy issues have been discussed over a long period of time already and now the question is what could be done with the data, not if something should be done at all. It does not mean that privacy is not important, but the focus has shifted to the content.

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