The VIS 2016 Conference in Baltimore was held just over a month ago. During the conference, I jotted down a few thoughts and impressions that have served as the basis for this post. The goal here of particular papers, but is instead some high-level observations about trends and topics of conversation among attendees while there.
One big theme of the conference to me this year was visualization education and pedagogy. I think this thread piggybacked on the excellent Education Panel at VIS ’15 in Chicago. A key idea to emerge from that panel was the use of active learning methods and interactive exercises in visualization courses. At the panel, Marti Hearst from Berkeley and Eytan Adar from Michigan talked about their use of such methods in their respective classes. This fall I’ve tried to incorporate some of these kinds of activities in my graduate CS 7450 Information Visualization course. While my course still primarily follows a lecture/Q&A style (with plenty of videos and demos thrown in), I’ve sought to have at least one interactive exercise per class. In general, these exercises have followed one of two styles. First, I have the class generate analytic tasks or questions for the topic being covered that day. This is particularly effective in the section of the course where we examine visualizations of different types of data (time series, network, hierarchy, text, etc.). A second technique I’ve used is to give a small design challenge, and have students pair up and create visualization design ideas for about 10 minutes. Volunteers then show their designs and we discuss each’s pluses and minuses.
Getting back to this year’s conference, the education focus began with a workshop on pedagogical issues in data visualization. It was exciting to see so many attendees in this workshop, and most seemed to be teaching visualization courses at their respective schools. This theme continued in the main conferences at the meeting: InfoVis had a session with education as a primary theme, and VAST had a session where most of the papers were about visual analytics systems for analyzing and understanding data generated from MOOC classes. The majority of these papers were from Hong Kong University of Science and Technology.
A second theme of the meeting this year that I found interesting was simply “color.” From Theresa-Marie Rhyne’s tutorial to Brown University’s InfoVis paper about the Colorgorical system to the InfoVis Best Poster about Colour Palettes, color seemed to be a topic on everyone’s mind this year. Of course, that’s not surprising at a visualization conference, but it just seemed to have increased emphasis this year. I think that’s a great thing. It helps all of us visualization researchers to have visual perception experts teach us more about all issues color-related.
Another big topic of conversation at the meeting was the panel “On the Death of Scientific Visualization.” It’s been pretty obvious, both via number of submissions and attendance in the meeting rooms, that for a few years now interest in infovis and visual analytics have been expanding while that for scivis has been contracting. I don’t conclude from this that scivis is going away, however. The continued development of better techniques for scientific visualization is extremely important. I simply view this changing interest as being a function of the potential audience in these different subareas. The audience for scientific visualization is just that – scientists, for the most part. This is a relatively small set of people, but extremely important ones! The audience for infovis tools is much bigger, and in many cases, is the general public at large.
I think a huge turning point in these conferences was the InfoVis ’07 Conference in Sacramento. One session of the conference was titled “InfoVis for the Masses.” That was a theme echoing throughout the community that year as Hans Rosling’s GapMinder system and TED video had everyone talking, IBM’s ManyEyes system was extremely popular, and the NY Times had begun to excel at data-driven storytelling on their website. From that point forward, infovis grew tremendously in interest and popularity. So what I see with scivis currently is not at all the “death” of that field. I simply believe that InfoVis and VAST have grown tremendously and they each have a broader reach.
On Monday of conference week I attended the BELIV Workshop that focuses on evaluation-related issues in visualization. I’ve been fortunate to have attended every one of the BELIV workshops going back to the very first one in 2006 in Venice, Italy (not a bad spot for a meeting). I’ve long thought that the evaluation challenge – how do we tell why a specific visualization is more effective than another – is one of the very top open problems in visualization research. Unfortunately, many traditional HCI-based evaluation methods simply don’t get the job done of comparing visualization’s utility, appeal, and effectiveness. (This idea was at the heart of my value-driven evaluation paper from the BELIV ’14 workshop.)
Reflecting back, I have to admit that I’ve been a little disappointed in the paper contributions at BELIV over the past couple meetings. It just doesn’t seem like interesting, new, useful ideas are emerging on this topic. I think that’s partly understandable as this is a very difficult problem to address – That’s what makes it such an important, challenging open problem to our community. But hopefully we’ll see some innovative evaluation methods and new approaches develop over the next few years. This is a great problem for young researchers to take on.
My final thought about the conference this year emerged as I sat through one of the last paper sessions and struggled to understand the research being presented in it, much as I had done for many of the earlier sessions. While part of this might be explained by the quality of the talks themselves (Jean-luc Dumont’s captivating capstone talk emphasized that issue as did Robert Kosara’s blog on common speaking mistakes), I don’t think that was the primary reason. I simply see it as a natural maturing of the field. Many of the individual subareas of visualization research (geovis, text vis, vis for ML, network vis, biomedical vis, time series data vis, etc.) have matured significantly now and have their own rich body of existing papers. To make a new contribution in these areas, one needs to do some very advanced research. Hence, it shouldn’t be too surprising that it is difficult for someone not well-versed in all the subarea literature to have difficulty following the papers in that session of the conference.
I see this as a natural maturation of our field – Something that occurs in other domains as well and is simply difficult to avoid. It’s kind of too bad in a way though because I think it makes the conference papers as a whole a little less accessible to newcomers not having a deep visualization background or even us old-timers who haven’t kept up on a specific subarea. But it shows that as a community we are growing, making progress, and solving problems, all good things.
Well, those are some summary thoughts from VIS this year. I’m looking forward to next year’s conference in Phoenix, a city that I have never visited before. Ross Maciejewski tells me that the conference will take place in a nice area downtown and it will definitely be warm!
Next column: Being a good visualization paper reviewer