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Reflections on Visualization Theory (Data viz readings, week 1)

In this first set of reading, I learn that the principles of simplicity, accuracy and more are as true in data visualization as they are in a text story.

Edward Tufte, Visual Explanations, “Images and Quantities”

In Tufte’s first paragraph of Visual Explanations, he discusses the importance of readability — a concept I see as having a strong parallel to the usability so often discussed in a more modern era.  He breaks down it down into three types of depicting quantities: direct labels, encodings (scales of color) and self-representing scales. I like his breakdown, as I’ve seen examples of all of theses, whether on news sites, or t0 illustrate points in scientific journals when I was doing medical reporting.  I attempted to do an encoding on my recent border crossing graph, but was unhappy that using color intensity to express data made everything so light that it was difficult to distinguish between colors. I would add the caution that encodings are best used when the entire graph is just representing various intensities of that one variable.

Tufte also writes that maps are just another type of graph.  Statistical graphics are those that don’t just give the data, but spatially arrange the data on a straight one-dimensional line.   No comment here, except to say that so far everything makes logical sense.  And the main takeaway seems to be a maxim true in written journalism as well, “Keep it simple, stupid.”

At the same time, we should make sure to include all the facts and necessary details, which means including labels when they are not self-explanatory.  Tufte critiques a computer visualization for looking pretty, but not putting its data into the proper context.  Another maxim: Content is king.  Without interesting information, the coolest icons, colors and animations do nothing.  It’s nice to hear that it’s a professional point, but before I would have just clicked off of something I didn’t understand.  And in the news business, or any business, that’s not something we want.

Ben Fry, Visualizing Data, “The Seven Stages of Visualizing Data”

Here’s Fry’s list:

  1. Acquire
  2. Parse
  3. Filter
  4. Mine
  5. Represent
  6. Refine
  7. Interact

All of these could probably be applied to writing as well.  I think it’s interesting that the first six of these elements are equivalent to reporting, and figuring out which quotes to use.  It strikes me that in both text and graphic work the actual creation is the end-step, but if you’ve done the previous work well, the piece should just write (or draw, or code) itself.  Fascinating.

One part that’s different about interactive data visualizations is the user’s ability to manipulate. This is a major focus of Fry’s introductory chapter.  Instead of guiding them through a story by taking them where you want them to go (they read your words in your order), here it’s your job to set up so they can make their own discoveries.  Creating visualizations for the Internet is all about putting the power back in the hands of the user.  Sometimes with text, I feel it’s a bit too easy to become a bit power-happy, after all, you’re telling the reader what they need to know.  Part of what I love about interactivity is that it’s democracy at its finest.  User, here’s the data so you can understand it, but there’s a nearly infinite number of ways you can explore it. Have it, and revel in the true sense of democracy.  That’s not withstanding the issue of whether the Internet is really democratic because not everyone has access, but that’s a topic for another time. Letting users drive our stories to a greater extent brings us back to why journalism was started, and its relevance goes beyond interactive visualizations, and provides a lesson I take to heart as a journalist, no matter what platform I’m using.

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Which states have been hit hardest by unemployment in the last decade? » »