Introducing GapminderVR: Data Visualization in Virtual Reality Introducing GapminderVR: Data Visualization in Virtual Reality
I am a big fan of sites such as Gapminder and Our World in Data. Such data visualization sites are like intellectual pornography. You want to know... Introducing GapminderVR: Data Visualization in Virtual Reality

I am a big fan of sites such as Gapminder and Our World in Data. Such data visualization sites are like intellectual pornography. You want to know which countries are doing better? Which continents drink more alcohol? How is alcohol related to GDP? Have people getting fatter recently, or is that a long trend? You don’t need to read thick books, you can just browse graphs.

I’m also a big fan of virtual reality (VR). In February 2016, I placed a bet against Greg Linden to the effect that by 2019, we would sell at least 10 million VR units a year worldwide. I might very well lose my bet but what is surely correct is that VR is part of our future. It is going to be everywhere, soon… where “soon” remains to be defined, but it is not 30 years.

What if you could mix data visualization and VR? What would happen? Could you make new things that nobody could think of before? I think so. That’s another one of my bets… but unlike my bet with Greg Linden, it is a bet I took by investing time and money in my lab.

To be fair, the idea that you could use data visualization in VR has been around for at least 20 years. It has gone exactly nowhere.

Why would it be different now?

The most obvious factor is cost. VR is much cheaper than it ever were, and it is getting cheaper by the day. And it is not just the hardware. The software is getting better and cheaper. This means that many of us can try new things and iterate much faster than ever before. If we can gather enough heads together, we will get somewhere.

If the work remains limited to a few academics, it is never going to take off. We need to engineers, designers and programmers to jump in.

In my lab, we decided to spend a few months building prototypes of what is possible in VR. We are publishing as demos two interesting cases:

  • Rail of time is a demo where you can control the time dimension with your feet (by walking). Walk forward and the time goes forward. Walk backward and the time goes backward. (YouTube)
  • Museum is a demo where you can visit a museum where the statues represent countries at a given time. The various attributes of the statues represent the various dimensions of the data. (YouTube)

If you have VR headset, you can try our demos in our site: GapminderVR.org. The name of the site is meant to pay homage to Gapminder and to the work of Hans Rosling. All our code is public and you can “steal” it. Or get in touch and we will help you. We hope to inspire future work. If you are interested in helping out, get in touch. If you can do better, please let us know about your work.

The design and programming work was done by Niko Girardelli. He is a super brilliant engineering student, and someone ought to offer him a six-figure job. Yet, to be fair, the programming is less demanding than you might expect. It is all JavaScript in the browser. And yes, the performance is decent. We owe a lot of credit to Mozilla and their work on WebVR. It is amazing.

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Original Source 
Daniel Lemire

Daniel Lemire

Daniel Lemire is a full professor in computer science at the University of Quebec (TELUQ). His research is focused on data indexing techniques. For example, he worked on bitmap indexes, column-oriented databases and integer compression. He is also interested in database design and probabilistic algorithms (e.g., universal hashing). His work on bitmap indexes is used by companies such as eBay, LinkedIn, Facebook and Netflix in their data warehousing, within big-data platforms such as Apache Hive, Druid, Apache Spark, Netflix Atlas, LinkedIn Pinot and Apache Kylin. The version control system Git is also accelerated by the same compressed bitmaps. Some of his techniques were adopted by Apache Lucene, the search engine behind sites such as Wikipedia or platforms such as Solr and Elastic. One of his hashing techniques has been adopted by Google TensorFlow. His Slope One recommender algorithm is a standard reference in the field of recommender systems. He is a beneficiary of the Google Open Source Peer Bonus Program. He has written over 50 peer-reviewed publications, including more than 30 journal articles. He has held competitive research grants for the last 15 years. He serves on the program committees of leading computer science conferences (e.g., ACM CIKM, WWW, ACM WSDM, ACM SIGIR, ACM RecSys).


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