Graphs and plots are "visual representation(s) of data, not way to magically transmit pure understanding"--though there is an implicit understanding of this fact within me, I was newly struck by the truth of this statement while reading Healy's introductory chapter in Profiles of Badness and Bergstrom and West's essays.
Many of the variables mentioned in her chapter are understandable intuitively. In other words, it makes sense that ordered and unordered data have different optimal channels of for mapping them; it makes sense that including or excluding a zero point on the x-axis will affect the perceived amplitude of change in the y-axis data points. Coming from a neuroscience background, I am fully aware of the ways in which visual perception alters the reality of what we are presented in the visual field. But to be faced with a comprehensive explanation of all of the ways in which perception plays into interpretation of data magnified the sheer multitude of factors that actually contribute to (or take away from) good visualization.
Whenever I had to make plots for scientific data, I mainly worked with my intuition--what looks and feels right? What communicates the data most clearly, and gives the most amount of information? I realize now that so much more goes into the creation of a good visualization; every detail in it must have a reason for why it has been placed there, why it has been formatted in that way. It is not enough that the visualization looks aesthetically pleasing, or the data is being presented "accurately". It relies both on good aesthetics and good data presentation, but ultimately culminates in the interpretation the viewer makes of it. We must think carefully about what can influence this interpretation, and make sure that our presentation of data does not create bias or misinformation.
Questions:
- As data visualization becomes more complex as technology advances, there is more freedom in the way data can be visualized and interacted with. Is there any work/research being done on best practices when making visualizations that are interactive (i.e. How much of interactivity is "fluff"? How much contributes to deeper understanding of the data being presented? How is it impeding understanding by presenting too much information?)
- Healy states that we cannot rely on viewers to know how to accurately read a scatterplot (or that the percentage of people who can accurately read one is lower than we think). How do we make sure that viewers have a uniform baseline understanding of the visualization types that we choose to use? How do we do that without imposing our own biases/interpretations of data?