kip/bot/blog

A colleague (Ed) walked into my office today saying something about “becoming a doctor” when he came through the door. Slow on the uptake, I needed the explanation that this was a reference to Field of Dreams, specifically the scene where Burt Lancaster, playing Moonlight Graham had to leave the eternal youth of the field to save Kevin Costner’s kid who was choking on a hot dog. All of which brought to mind the tidbit I had to tell Ed: Moonlight Graham was a real player and the story was true!

I always assumed that W. P. Kinsella, the author of Shoeless Joe the novel on which the movie was based, was a baseball nerd who browsed the sadly no-longer needed Baseball Encyclopedia and found that one line of data that inspired a story.

I owned a copy of Baseball Encyclopedia and got goosebumps when it occurred to me to look up Moonlight Graham and see if he really existed. There it was. This guy got to put on a uniform, get on the official scorecard, maybe even took the field, but didn’t get to bat. Out of that line of zeroes, a string of non-data, really, Kinsella imagined a whole potential person and life story. Dig it.

Nice Ignite talk by Alex Lundry, who, according to a quick Google hit, does a lot of market and political research and is a consultant to the GOP, has a really great Ignite talk about data viz, visual thinking, and some politics.

Nice Ignite talk (”enlighten us. just be quick about it” by OReilly) about the basics of data visualization. The presenter, Matthias Shapiro, gives some nice conceptual frameworks to work with: pick your metric, ask a specific question, choose the dimensions (time, location, network, color, time).

Quick post from Michael Surtees has a nice line:

Unfortunately a lot that passes for data visualization isn’t much more than data fire works. It makes an impressive pop but fades into darkness. Entertaining but not really informative.

and some good links to other, true, data viz stuff.

I like this video (found via Flowing Data) quite a bit, especially the reminder about the fragility of the atmosphere and curve of the horizon line. But is this data visualization or is it fact marketing? Data visualization should take data points and reveal patterns unseeable (or hard to see), or coax a story out of a perceived bunch of noise.

Vizworld just posted an interview with Edward Tufte which is a nice reminder of first principles of data visualization:

Not much has changed since Tufte began offering the Presenting Data And Information lecture years ago, other than a fourth book and a couple of new examples, but not much has to change when the point is returning to the first principles of information design: make wise comparisons, show causality, employ multiple variables and, above all, focus on the content. This point was driven home for me early on in the lecture as I internally formulated a question on one of my favorite topics: “How will the techniques presented in this lecture help me better represent 3d digital cities?” As if my mind had been read, the answer came: “Don’t ask how visualization techniques can help display data. Ask how data can be best represented.”

I like that it’s a statement of positive principles — show causality and comparisons, seek out complexity and richness, etc. — rather than the anti-prescriptions that are often associated with Tufte (avoid chartjunk, eschew Powerpoint).

Ben Fry, creator of Processing (or Proce55ing for those that remember) and data viz guru at MIT, has an absolutely fascinating visualization of how Darwin changed the text of “The Evolution of Species” in the thirteen years following its publication.

The labels across the top are chapter numbers, the dashes underneath represent text from the book which you can see on mouse-over. The color bars indicate the different editions.

I called it fascinating on first look, but should probably be more measured or specific. I hate when we fail to distinguish between fact illustration (making a single thing visual) and data visualization (revealing previously unseen stories through a rich visual worth looking at several times). This falls somewhere in between. The final state of the chart, after the 6th, and lengthiest, revision does tell a story:

The most obvious part of the narrative is the addition of an entire section and extensive revisions to the final section in the 6th edition, indicating a structural bolstering of the argument and possibly responses to ten years of critique. The speckle patterns, small bits of color, show a lot of tinkering/revising in the first three editions. These all support Fry’s introductory point:

We often think of scientific ideas, such as Darwin’s theory of evolution, as fixed notions that are accepted as finished. In fact, Darwin’s On the Origin of Species evolved over the course of several editions he wrote, edited, and updated during his lifetime

I’m wondering, though, whether this illustration tells the story better than the text?

What does make it fascinating overall is the ability to mouse over the sections (the small gray and colored stripes) and read the text underneath. Might be a better tool (if the stripes were a little bit bigger and easier to mouse over) than it is a data viz.