Tuesday, September 27, 2011

garage sale signs and data viz: the power of preattentive attributes

I was jogging the other morning and ran by a woman hanging a sign for a garage sale. Her advertisement was penned on a piece of yellow 8x11 paper, uniformly golfball-sized letters describing the detail. In short: someone would pretty much have to stop their car, get out and walk up to the sign to know what it said. And after doing so, would need to read the entire sign to find out the most relevant parts of the detail: if it was in an area of interest, or at a time that would suit.

This was obviously a poor sign. The only thing it had going for it was that the yellow paper was eye catching. But I imagine that only those in search of garage sales would think of stopping to pay it more attention; the sign was clearly not going to be read by the majority of passersby.

This led me down a thought path: what makes a good garage sale sign? I had a hypothesis. After arriving home, I looked up images of garage sale signs with my favorite search engine. Here's a sample:

It seems to me that one of the things that makes for a good garage sale sign is one of the same things that makes for a good data visualization: strategic use of preattentive attributes.

"Preattentive attributes" in the world of information visualization is a fancy descriptor for aspects of a visual that hit our iconic memory. Iconic memory is what happens in our brain before short term memory kicks in, before we even really know that we're thinking. Iconic memory is tuned to pick up preattentive attributes: things like color, size, added marks, and spacial position [learn more].

In the lessons I teach on data visualization, I discuss using preattentive attributes mainly with two goals in mind: 1) directing the audience's eye and 2) establishing a visual hierarchy of information. In both cases, the point is that if you use preattentive attributes well (especially color), your audience can't help but focus on the important part(s) of the message. By playing on their iconic memory, you're making it so they are seeing what you want them to see before they even know they are seeing it. Which is a crazy powerful thing.

I have a good Google before-and-after example that's been genericized that I'll post later this week. If you're too excited to wait, I'll be discussing it (and more on preattentive attributes) at the Visual.ly meet up on Thursday in Mountain View [see details].

Tuesday, September 6, 2011

visual.ly meet up

If you live in the Bay Area (or have plans of being there in late September), you may be interested in the visual.ly meet up taking place on September 29th (sign up is here; do it soon if interested, as spots are filling up quickly). I will be one of the speakers and will discuss leveraging preattentive attributes to make great data visualizations, highlighting an example from our research on Google's People Analytics team. Hope to see you there!

Thursday, September 1, 2011

visualize this

Nathan Yau writes one of my favorite data visualization blogs, FlowingData. His recently published book has been sitting on my shelf untouched for much too long. Earlier this week, I decided to remedy that.

His book is Visualize This. Subtitle: The FlowingData Guide to Design, Visualization, and Statistics. It's written in the first person and is super accessible, full of examples and anecdotes to make the lessons real. The book includes references to a lot of publicly available data and also has links to each dataset used, so the reader can follow along through the steps that are explained.

After starting with an introduction on telling stories with data (obviously near and dear to my heart), the book jumps into the practical question of how. There are step by step instructions for scraping data from websites, using Python to reformat it, and the strengths and weaknesses of various out of the box applications and programming languages for analyzing and visualizing data.

By his own words, Nathan's book is "example-driven and written to give you the skills to take a graphic from start to finish." It accomplishes this goal. The middle chapters each focus on a different kind of visualization problem: visualizing patterns over time, visualizing proportions, visualizing relationships, spotting differences, and visualizing spatial relationships. Yau follows a thorough, hands on approach. For example, in the chapter focused on time series, he goes through what to look for, the best types of graphs to use in different scenarios, how to load the data into and plot in R, and how to fine tune the visual using Illustrator. Relevant statistical methods are incorporated as makes sense, for example, smoothing and estimation.

While there is some very solid foundational material, the majority of the book is focused on the practical question of how to actually analyze and visualize the data. It seemed to me most tailored to the person who is looking to move beyond Excel and the like and get started using R and Illustrator (with some time devoted to interactive graphics as well).

Throughout, Nathan's graphics are beautiful and accessible - great examples of effective data visualization. He follows the rules he sets forth in every one:

  • explain encodings,
  • label axes,
  • keep your geometry in check,
  • include your sources, and
  • consider your audience.

The final chapter focuses on designing with a purpose. He says he always assumes that people are showing up to his graphics blindly and puts the onus on himself as the designer to prepare the audience with the relevant context and insights. "After you learn what your data is about, explain those details in your data graphic. Highlight the interesting parts so your readers know where to look. A plain graph can be cool for you, but without context, the graph is boring for everyone else."

Well said, Mr. Yau!