Our reading for this week centered around how data visualization specialists employ strategies and techniques that, at their most innocent, can cause misinterpretation of what is presented, and at their most malicious, can actively deceive their audience.
I was fascinated by the details of human perception covered by Healy's 'Look at Data' introduction, and the extent to which our perceptive strengths and weaknesses affect a visualization's ability to communicate its message clearly. A great example of this effect, and one that I admit was not one I had realized or focused much on in the past, is in color palette. I have always chosen color palettes for visualizations based on aesthetic preference, but never realized the perceptive effect these colors can have on interpretation. In displaying a varying degree of palettes that do a 'good' job at accurately representing sequential and categorical differences, it is easy to see how nonuniform jumps in color differences could lead to value judgements on information that is not actually present in the data being visualized.
Healy's detail of the experiment showing our cascading ability to correctly estimate the differences between two values based on the visualization used was exceptionally useful. Healy stresses the strengths and weaknesses we have in identifying differences - what stuck with me the most is the idea that having a shared or common scale (over length encoding, for example) is supremely helpful.
Healy talks about data ink, and this point is hammered home by Bergstrom and West (which, as a short aside, I found terribly entertaining reading). Through this reading we see many examples of violators of best practices laid out by the authors, and how these examples breed misperception. While everything these authors laid out makes complete sense, I would love to see some more examples that aren't horrendously bad but rather walk the line between acceptable and problematic, so we can become more nuanced and detailed in our process of avoiding such pitfalls as designers.
From the class I'm curious to know the answers to two questions:
(1) Do we believe in Bergstrom and West's (somewhat_ hard-and-fast rule that bar charts (or any chart displaying a value by area) should ALWAYS include the zero axis? Are there any examples where this may be appropriate?
(2) I found it interesting (and surprising) that Healy gave some credit to Nigel Holmes's 'Montrous Costs' visualization. What is out sentiment - do we agree that this visualization is a little more memorable (in a positive way), or that visualizations that sometimes are a little more lax on maximizing data ink are a more digestible?