Well, the world hasn’t ended, so here’s a look back at what happened in visualization in 2012, and a look ahead in case the world is still around a year from now.
2012: What Was
2012 was the year visualization in the news took off. Forget Flash and slideshows, the real breakthrough has been D3 and its developer and champion, Mike Bostock. Alberto Cairo also published his book, and is teaching thousands of people how to turn data into interesting stories.
And you know who called it a year ago? Hm? Who? That’s right, this guy:
If you don’t believe that data journalism will be big in 2012, I have one word for you: U.S. Presidential Elections. Polls, primaries, more polls, ads, counter-ads, and then election night.
The New York Times, which used to hide its interactive pieces online, has posted a collection of the amazing work they did in 2012, including such pieces as 512 Paths to the White House. Since Mike Bostock works there now, many of them were done in D3.
This marks a sea change not only in terms of interactivity on the web, but in how newspapers are starting to approach the issue of browser support. Rather than play to decade-old technology (remember IE6?), they now work on the cutting edge and provide limited fall-backs for people on prehistoric browsers. Technology moves on, and news media have finally started to get it. This isn’t just true of papers like The New York Times or The Washington Post, I’ve heard similar things from much smaller papers.
2013: What Will Be
So this time, I will not just predict that storytelling in visualization will be big, but outright say that I have every intention of making that happen. I will be at Computation+Journalism (on a panel with Alberto Cairo) in two weeks, then there’s the Tapestry conference, and right after I will be at NICAR (on a panel with Amanda Cox). Jock Mackinlay and I also have a paper on storytelling coming out in the special issue on future research directions for visualization of IEEE Computer (May issue), and I have a few more things in the pipeline.
Communicating data to people using visualization is an exciting and important direction for visualization, and one I deeply believe in. And with the growing availability of data, as well as journalists’ increasing ability to build exciting and informative pieces (and the continuing need for good, accessible information), we will see a lot more interesting visualizations this year and in the years to come.
In less self-centric future developments, Many Eyes is coming back! I know that IBM is rebuilding a new visualization group after shamefully neglecting and abandoning the one they had. They have finally come to their senses, perhaps after some nudging from yours truly, and are about to release a revamped version of Many Eyes. I believe the targeted release date is in March, if I’m not mistaken. Given some of the people involved, I’m cautiously optimistic.
Maybe it’s my new perspective in my new job, but I’m starting to lose patience with research that doesn’t even try to solve realistic problems. I’m not talking about theory, I’m talking about doing work for its own sake rather than because it actually addresses a problem or provides any kind of real insight.
And I’m not talking about purely applied work, either. Look around who is currently killing it with visualization: newspapers. They’re where it’s at. Not academia. The most exciting work last year was done by people who mostly don’t even have advanced degrees; and yet they also publish or perish (see what I did there?). Academics should take note, and they should be scared.
If you think I’m ranting, wait until I unload my actual rant on the topic here soon. But this is not a new thing. I actually mentioned this in last year’s posting. Academic visualization research needs to stop messing around and figure out where the interesting work is happening, and get into the game.
There is plenty of opportunity, too. BioVis is still up and coming, but it’s showing how application-specific work is done right. Graph visualization sucks. Interaction in visualization is mostly an afterthought, and terrible. Too much work is still tied to the idea that every individual data point needs to be shown, even if most questions are asked in terms of sets and subsets. And visualizing those isn’t exactly a done deal, either.
So let’s get started!