The VIS/InfoVis/VAST conference has been as usual a great event, with lots of good presentations, events and interesting people to meet. The conference venue was really nice (trivia: the Hyatt Hotel is the place where Governor Schwarzenegger lives when he is in the capital) and I definitely enjoyed some Californian sun and was really pleased to find old and new friends.
Despite the high-level technical program and the abundance of good presentations my best moments have been the InfoVis keynote and the VAST keynote respectively by Matthew Ericson, from NY Times, and by Stephen Few, from Perceptual Edge. I was really happy to see these two guys describing, from very different perspectives, things on information visualization as done in the real world: for people who don't want to spend excessive efforts to understand what a visualization means and by people who are not traditional visualization researchers/developers. It was a breath of fresh air for my mind.
I'll start with Ericson's talk here in this post. I intend to write something about Few's talk too in another post.
Matthew Ericson described quite in detail the work they do at NY Times to produce effective visualizations that are informative and easy to understand at the same time. I was impressed by the quality of their work and the heterogeneity of the people in the group. And I was also impressed by Mr. Ericson's argument that they consider themselves first of all "journalists" rather than designers. As such, their primary purpose is to tell a story to the reader. Looking at the graphics produced it's impossible to remain indifferent, the eye and the mind are suddenly engaged, there are stunning visualizations, complex and simple at the same time. Each piece is extremely rich: annotated with concise and well placed text notes, multiple tiny views arranged in a way that the whole set tells a story, pictures and/or diagrams added when/where needed.
I also liked the concept of "honest portrayal". Tufte and others have for a long time warned us about the dangers of visualization; for the very fact that it is so potent in conveying information, it can also be used to send wrong or partial messages. Mr. Ericson goes a little bit further, in my opinion, saying that it is important to keep always an eye open to that fact that visualizations may convey partial truths and, more important, that often in order to convey the whole picture a single visualization is not enough, it is necessary to present the data under different perspectives. The example of the US 2004 elections made the case clear.
The picture is not necessarily "wrong" or purposely "false", but still it contains a partial story that can be misinterpreted: the amount of red in the map is enormously higher than the amount of red because it represents only two values: Bush (red) vs. Kerry. But the picture does not tell anything about margin of votes ... and neither about the population density! Here is how the maps have been reworked and assembled in a full story (click on them to see a big picture).
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Another element of interest of the NY Times people is how fast they can produce these graphics. Ericson explained that they work in very very tight schedule because they have to follow the news when they are hot and cannot wait weeks or months to produce a story.
What remains totally obscure to me is what kind of tools these people use to produce such a beautiful and complex graphics in such a short amount of time. Especially because the kind of visualizations, charts, and diagrams they design are not at all trivial and I would bet that most of the time they have to mix the outputs of various tools. Being able to turn data into pictures in such a short amount of time looks to me some kind of magic.
In short what I learned from the talk is that if we want to reach the large public with visualization we have to take care of every detail and present a beautiful, rich, engaging and self-explained piece of work. Sure, this does not take into account how people would interact with interactive visualizations when provided with them, but still I have the feeling that the same principles remain: design complex and composite solutions to provide depth and richness and, at the same time, strive like crazy to make it simpler, simpler, and simpler. This is what most of the time people need to reason about their data.
