Lin’s lecture on Data Visualization (D.V.) was enlightening; I enjoyed her D.V. approach as a mixture of understanding and exploration. The idea of making public data truly public through less specific and more accessible graphs is an important concept for Digital Humanities projects. Our goal in DH projects is to tell the general message and avoid suffocating the reader with unnecessary mathematical precision. Moreover, I learned a lot from Lin’s explanations of exploratory versus explanatory data visualizations, and I think that those concepts will be helpful from now on.
As I consider D.V. best practices for DH projects, I will make sure to keep in mind the rhetorical effect of my choices when representing data. Colors and elements’ positions play an essential role in passing on the desired conclusion. Moreover, I learned the importance of creatively examining the data and experiment with several different graphs and approaches after selecting a relevant question. Furthermore, I will remember that the exploration step is just for understanding the data. The content generated in this step should be carefully clean up and polish to show the whole story in the data at the explanatory stage.
After the talk, I went over the Keeping it Honest: How Not to Lie with Pictures assignment. I had some fun exploring r/dataisugly and found a particularly intriguing bad data visualization about “Billionaires per Capita”. The picture is below, does not make any sense and it is a great example of what not to do when constructing data visualizations.
Nonetheless, I also found examples of data visualization that were not obviously wrong; they contained mistakes that I did not think of before and that I will try to avoid. For instance, if I had to chose between creating a visually appealing 3D graph and keeping the right magnitudes/scale, I would choose the latter, as I would want it first to portray reality. Surprisingly, I also found good ideas and creativity between the bad data visualization images – sometimes an illustration can be way more adequate than a scatterplot or histogram.
