Q&A Responses to my “Dispersion vs Disparity” Workshop @ Wellesley:
Last week, Jeremy Wilmer invited me to present my and Cindy Xiong’s recent research project to Wellesley’s Psychology and Data Science groups, highlighting how conventional charts and graphs showing social inequality can lead to harmful stereotypes about the people being visualized. In the talk, I presented our findings, but also the underlying psychology and some advice for designers. (For context, my 12-minute presentation for IEEE VIS 2022 covers the research itself).
During the talk, several interesting questions came up. I did a characteristically mediocre job of addressing them verbally, so I thought I’d revisit them here in writing.
Q: “[When visualizing social inequality], I’m guessing a balance between variability and simplicity may be hard to achieve?”
For most dataviz project, there’s usually a tension between showing too much (sacrificing approachability) and showing too little (sacrificing critical context). Finding the right balance is part of what makes data design so challenging… and fun!
As a field though, we often err towards showing too little. This is maybe a remnant of zombie ideas like data-ink ratios, that elevate unnecessary minimalism. Or in other…
