InfoViz Week 4
By: Kandarp Khandwala
Tags: Data Visualization, Design

InfoViz Week 4

Data-Ink, Chartjunk and Graphical Design

The concept of data-ink, though apparently obvious, was highly illuminating due to its simplistic formulation that emphasizes the visual balance of data v/s everything else. Never before had I thought about say, grids, being visual clutter, most likely because (as Tufte reasons) those are quite helpful when plotting by hand, but not in print. The example with null data-ink ratio also made it resoundingly clear that trend lines may not accurately reflect the data (since not a single point necessarily lies on one). Nonetheless, I have quite a few objections this time around, beginning with a want for unambiguous principles. For example, the oft-used phrase “within reason” is subject to interpretation and has not been substantiated by a framework of reasoning that could be applied to a wide range of graphics. Next, he argues about portrayal of symmetric pieces of information being redundant, however, without any convincing study of how people perceive it after trimming a particular half. There is only a fleeting mention elsewhere, that eventual familiarity with “new” designs (such as the half face) will make them seem just as reasonable. Similarly, when erasing or consciously repeating data representation (an example of the latter being the display of surface ocean currents), what is a good metric to decide the extent to which one must erase/repeat? A more formal treatment to describe what is jarring (or conducive) will really help here.

Moving on, we are given a look at the various ways in which injudicious use of ink leads to the equivalent of fashion disasters in data graphics. At first, the cynicism was exhausting, but the dizzying optical art made it seemed rather restrained! In recent times, though, with widespread popularity of “flat” design, we encounter chartjunk much less often, which is an interesting scenario of more general design sensibilities having a positive impact on visualization. Tufte also proposes ways to make grids subdued yet more effective, which is curious and even surprising as the variation of color (even shades of gray) and line thickness is omitted from previous chapters on graphical excellence, probably out of spite for the aesthetic. Again, while the improvements are visible to me, a more concrete rationalization of say, contrast perception among people, would drive the point home. Finally, I’m probably guilty of making a few duck-graphics myself, as a child with newfound access to plotting tools on a computer and the inclination to decorate excessively. There is however, no excuse for when such things find their way in professional publications and that has been well articulated and affirmed by Tufte.

Rounding up the principles proposed in the previous chapters, Tufte tries to create better iterations of existing data graphics. My impression of the redesigns is simply this: is it a matter of taste or can we find a basis in more precise theories (of perception, etc.)? I would do some redesigns differently, or not change them as much. A common recourse that can be noticed is how in the concluding paragraphs, Tufte takes a milder stance and in this case, calls the exercise an “experiment”, caving in to admit a notion of beauty that underlies even statistical graphics.