I really enjoyed this week’s readings, especially the first Chapter of Kieran Healy’s book Data Visualization. I don’t want to be too humanistic about it, but I found it remarkable, that the visual honesty, clarity and mindfulness he is advocating for is also represented in her text, for example when he explains the limits of the scope of her text as well as ongoing research in the introductory paragraphs of Chapter 1.3 & 1.3.1.
Overall I think the text is a good addition and contextualization of our discussion in class and is well structured to consult for later reference.
It hasn’t been the first time I was introduced to the Gestalt principles, but Healy’s explanation and visualization was by far the most concise and still fine grained I’ve seen so far and it stuck with me, because I realized how subtle differences not only affect communication effectiveness and aesthetics but also the integrity of the whole act of communication. Even though I was aware that the category of “honesty” would be a new dimension to design for me, I am surprised how fragile and vulnerable it seems to be and therefore demands a great deal of attention. I think it’s largely the influence of Bergstrom & West's passionate Calling Bullshit essays that most of my thoughts and questions circle around the theme of integrity.
I think when visualizing data we should be mindful of the fact, that the result will never be neutral and regarding the topic of our dataset, our audience and our client’s or our own intentions we could take responsibility and try and communicate design decisions (more) openly. We could even discuss if that can be a reaction to the post-empirical condition we’re confronted with at the moment. To give a bit of context to my following and maybe odd thoughts I wanted to give a short preview to the discussion of Laura Kurgan’s text in the Major Studio 1 course. Here’s a quote from Bruno Latour that she references regarding the crisis of representation:
But it might also be the case that half of such a crisis is due to what has been sold to the general public under the name of a faithful, transparent and accurate representation. We are asking from representation something it cannot possibly give, namely representation without any re-presentation, without any provisional assertions, without any imperfect proof, without any opaque layers of translations, transmissions, betrayals, without any complicated machinery of assembly, delegation, proof, argumentation, negotiation and conclusion. (Latour, 2005, p. 26)
Kurgan advocates for a new understanding of truth, that might be helpful when thinking of new ways to communicate the limits of data visualizations. Referring to her previous juxtaposition of a photograph of earth taken from space and a rendering of it from collected satellite data she suggests that this example
helps us understand what has become of truth in the era of the digital data stream: it is intimately related to resolution, to measurability, to the construction of a reliable algorithm for translating between representation and reality. (Kurgan, 2013, p. 12f)
tl;dr
Even if this is might be a bit stretched and Kurgan’s suggestion does not directly apply to human designed visualizations, it leads me to the following questions (sorry for the long introduction):
- Kieran Healy mentions several times that we cannot take for granted that everybody understands how to read a scatterplot or the less common types of charts. Would it help to include something like a manual, in cases when we are not completely sure whether the audience is used to interpret the kind of visualization we use?
- Many of the good visualization examples include the source reference to the original data set. But to prevent the misleadings like in the New York line chart suggesting declining support of democracy could we communicate in one way or another how the data was treated to create the graph?
- Regarding the inclusion of the “zero” and the importance of the aspect ratio of a graph, would it be an option to allow the user, where applicable and the conditions, to choose whether he wants to see a version with the “zero” or even alter the aspect ratio? – Of course it is still an option to set boundaries for this user interaction.
- When talking about persuasive methods in information aesthetics, how would you describe a fairly neutral “style”? Can we avoid that these “style” conventions can also be tempered with, in a sense that the design suggests integrity but is actually deceptive?
References
Bergstrom, C., & West, J. (n.d.-a). Tools - Misleading axes on graphs. Retrieved September 8, 2018, from https://callingbullshit.org/tools/tools_misleading_axes.html
Bergstrom, C., & West, J. (n.d.-b). Tools - Proportional Ink. Retrieved September 8, 2018, from https://callingbullshit.org/tools/tools_proportional_ink.html
Healey, K. (2018). Data Visualization. Retrieved from September 8, 2018, http://socviz.co/lookatdata.html
Kurgan, L. (2013). Close Up at a Distance. New York: Zone Books.
Latour, B. (2005). From Realpolitik to Dingpolitik, or How to Make Things Public. In B. Latour & P. Weibel (Eds.), R. Bryce & et al. (Trans.), Making Things Public: Atmospheres of Democracy. Cambridge: MIT Press.