Here’s a preprint of our paper on selecting tick labels for axes which will appear in this year’s InfoVis! Source code of the implementation will be made available before the conference. We’re hoping to get this implemented in a number of common plotting libraries. I already have a partial matplotlib version working. I would also like to have one for ggplot. Other suggestions are welcome.

The abstract:

The non-data components of a visualization, such as axes and legends, can often be just as important as the data itself. They provide contextual information essential to interpreting the data. In this paper, we describe an automated system for choosing positions and labels for axis tick marks. Our system extends Wilkinson’s optimization-based labeling approach to create a more robust, full-featured axis labeler. We define an expanded space of axis labelings by automatically generating additional nice numbers as needed and by permitting the extreme labels to occur inside the data range. These changes provide flexibility in problematic cases, without degrading quality elsewhere. We also propose an additional optimization criterion, legibility, which allows us to simultaneously optimize over label formatting, font size, and orientation. To solve this revised optimization problem, we describe the optimization function and an efficient search algorithm. Finally, we compare our method to previous work using both quantitative and qualitative metrics. This paper is a good example of how ideas from automated graphic design can be applied to information visualization.

Will Wilkinson points to the Gallup-Healthways Well-Being Index which purports to measure overall health (“not only the absence of infirmity and disease, but also a state of physical, mental, and social well-being”) at the congressional district level for the United States. Will hypothesizes that Utah’s high score may be due to “a skoche of culture-driven upward inflation” (Mormons overstating their happiness).

Fortunately, the components of the Well-Being Index are reported as well. Two components, Life Evaluation and Emotional Health, measure self-reported happiness. If Will’s hypothesis were correct, we would expect these components to account for a disproportionate share of Utah’s overall index. In the scatterplots to the right, the three Utah congressional districts are highlighted in orange. Contrary to Will, Utah is above average only in the Work Quality component. On all the others, including Life Evaluation and Emotional Health, Utah is average or below average.

Wellness data in Excel, since I couldn’t figure out how to get it from the Gallup-Healthways site. The visualization was done in Tableau.

For Jeff’s class I created an interactive visualization of the American Time Use Survey. I got sick last week so didn’t have a lot of time to work on it. As a result it turned out somewhat derivative of the Baby Name Voyager and other stacked area plots.

That said, I think it lets you find some rather interesting patterns in how people use their time. Most noticeable is the extra hour or so that people sleep in on the weekends.