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. (more…)
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