The VIS ’17 Conference was held almost two months ago in downtown Phoenix, AZ. This column is my woefully late recap of the meeting with a few reactions and thoughts about how it went this year. I was so busy upon returning to school after the conference that I just kept putting off writing this. (If I ever consider being an AC for the CHI Conference again, someone please slap me.) Now that final exams are almost here, I’ve finally gotten a little chunk of time to pull this together.
I really enjoyed the location of the conference this year. I’d never been to Phoenix before, so I wasn’t sure what to expect. The conference was held at the big downtown convention center with a lot of hotels nearby. Even though it’s in the middle of the city, it didn’t feel like that. It was relatively quiet in the surrounding area and it certainly was easy to get around. Plenty of restaurants were nearby too. One night the streets were buzzing as the Diamondbacks beat the Rockies in the NL Wild Card game, and all the fans streamed out afterwards. The home stadiums of the Diamondbacks (baseball) and Coyotes (ice hockey) are close by, and add to the atmosphere of this part of the city.
The convention center itself is quite large. As VIS has grown, we now must use facilities like it just to be handle the number of attendees. The rooms for paper presentations also were huge, perhaps even a little too big. Of course, we’re at the mercy of the layout of the venue on this, and no one wants meeting rooms that are too small. However, the sessions often felt a little sterile and impersonal to me this year. It seemed like relatively few questions were asked after talks, and I wonder if the room size and atmosphere somehow contributed to that, even a little.
Three topics/themes stood out to me this year. The first one was data science. In workshops, tutorials, and papers, the topic was everywhere. It seems like VIS is just mirroring what we’re seeing throughout academia now as more schools create Data Science degrees, programs, and even in some cases, departments. Visualization is only one piece of data science too, and sometimes a piece that is overlooked. Machine learning is clearly a large component of the data science equation, and it was ever present at the conference. It seemed like half of the VAST papers were about interfaces to machine learning algorithms and systems.
The second theme that grabbed my attention, particularly at InfoVis, was the growing presence of evaluation-focused papers. I guess this is to be expected – As our area matures and it becomes tougher and tougher to come up with new visualization techniques and systems, it shouldn’t be surprising to see more evaluation papers show up. InfoVis seems to feel a little more like CHI every year to me. (Not sure how I feel about that.)
The final topic I noticed this year was a simple one, word clouds. I couldn’t believe how many papers were about them! OK, OK, maybe that’s an exaggeration, but there was one paper session that seemed to be all about them. While they can be great for advertisements and fun, I always remember Jacob Harris’ great column and quote: “Every time I see a word cloud presented as insight, I die a little inside.” Anyhow, I did like the EdWordle paper and especially the interactive demo at http://www.edwordle.net/.
While I enjoyed many papers at the conference, a few stood out to me. Sandia National Lab’s work developing a data visualization saliency model is fascinating. The computer vision community has good models that can predict where people will look within a picture, that is, what parts of the picture will first draw a person’s attention. The Sandia team is working on developing a similar model for predicting the parts of an abstract data visualization that will draw focus. This model has some very different heuristics than what one finds with natural, photographic images. I also really enjoyed Jorge Poco’s talk and demo about extracting color maps form bitmap images of visualizations. It was fantastic how he and his colleagues can identify color legends and ultimately allow a person to change them, which would then be reflected in the image. I also enjoyed Dragicevic and Jansen’s replication study of whether charts persuade people to trust textual arguments more, and Lam, Tory, and Munzner’s paper about the challenges of moving from high-level analysis goals to low-level analysis tasks.
Giorgia Lupi’s closing capstone talk on “Data Humanism” was fantastic as well. Giorgia is one of the two correspondents in the Dear Data series of visual postcards about their lives. She sees data visualization becoming much more personal in the future and she advocates that people explore and draw with data to discover what it holds. Giorgia’s column in Medium is a companion to and highlights the key points from her capstone talk.
I certainly missed one thing from many of the talks this year, demos. Sitting in the few presentations that had one reminded me how well a demo can make the ideas of a paper more concrete and illustrate potential applications of the research. Jarke van Wijk’s papers of the past stand out in this respect to me – So often I remember thinking about them, “Wow, that’s cool.” Here’s hoping that more authors and presentations include demos in the future.
Attendance was down a little at the conference this year. I believe that just over 1000 people attended, while the previous few years were up in the 1200-1300 range. I’m hoping this was a momentary blip, perhaps due to the location (Phoenix) being a little out of the way for many people. I certainly see visualization continuing to grow as a topic, so I fully expect VIS to keep growing as well.
With that said, I do have mixed feelings about one side effect of the growth of the conference. Back quite a few years ago, the InfoVis Symposium was a single track. All attendees who were interested in that topic attended all those sessions together and effectively shared the same experience during the week. (The SciVis Conference, then called just “Visualization”, had multiple tracks due to its larger size, but I always stayed at InfoVis.) With today’s configuration of multiple tracks, panels, journal papers, and the addition of VAST, attendees are torn between and eventually scattered about many possible sessions at any one time, and they tend to gravitate their own existing interests. Papers that are up against other popular topics may receive relatively little traffic. The single track/shared experience model of the past promoted more exposure to papers and topics outside of a person’s comfort zone. I definitely feel that the single track helped our community prosper and grow. Its loss is an inevitable consequence of growth, which also has its benefits, but sometimes I long for the “all in it together” days of the past.
Next year we’re on to Berlin, VIS’s second trip outside of the United States, in what should be an exciting meeting. Before that, the AVI and EuroVis conferences fall in back-to-back weeks late next spring in Italy and the Czech Republic, respectively. Europe clearly will be the hub of academic data visualization research in 2018!
Next column: Some thoughts about insights from visualization.