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Data Visualization with Micki Kaufman

1 Leave a comment on paragraph 1 0 First off, let me say that this was not a tutorial. I followed along on some of work Micki Kaufman did in Tableau and Gephi, but this session was not meant to be a tutorial. Instead, we learned from Micki’s tremendous experience the practices that make effective, scholarly presentations of data. I will highlight some of the things I learned and insights I had.

2 Leave a comment on paragraph 2 0 Micki spent some time on color acuity, which is obviously very important when using color to represent data. Apparently red and green make for an awful pair, and Excel often defaults to a red and blue that some may struggle with. The best pair to use is blue and yellow, as most people can distinguish it. When possible, using different textures and lines can also be very helpful.

3 Leave a comment on paragraph 3 0 Micki emphasized that we do not have to be experts in computer science to use advanced data-manipulation techniques and feel confident in our work. For one thing, she railed on “brogramming,” the idea that one needs to use command line to achieve anything. Instead, it is probably better to use GUIs that have been created for more general use. I do feel that with most software, limitations will eventually surface as you get really specific in what you need to do. Relatedly, one only needs to know so much of the backend and underlying principles to confidently make their argument. This reminded me of some of our early readings in Debates in the Digital Humanities, and I think some scholars may disagree with the adequacy of our tools.  Micki instead highlighted visual subjectivity and the need to be aware of how a visualization exaggerates or de-emphasizes phenomena.

4 Leave a comment on paragraph 4 0 For instance, a word cloud represents how often words are used in a dataset, but their proximity is entirely random, and this can have unintended consequences. Micki’s work obviously goes far beyond just measuring the magnitude of words. She demonstrated some of the techniques she has applied and experimented with, such as topic modelling, collocations, and concordance.

5 Leave a comment on paragraph 5 0 Some of Micki’s other points related strongly to our class discussions–her use of monochrome in one visualization because of severity of death her topic touches, The History Manifesto, and the hermeneutic, cyclical process of visualization. Perhaps the most poignant thing Micki said was that a groundbreaking analysis or visualization must at once be dramatically different and similar to what the observer expects. Replicating past arguments is hardly compelling, nor is finding data that supports a completely bewildering idea. Instead, there must a contextualized challenge to a belief other scholars’ hold in the field. It may seem reductionist to suggest how to make an impact with research, but Micki’s example did well to illustrate this point: if a young, 11th century scholar invented a new telescope, the learned astronomer would expect it to replicate his ideas about the universe, but show him something new.

6 Leave a comment on paragraph 6 0 We did get some instruction on using Gephi, which was helpful to me as I had previously been completely overwhelmed. I was eager to learn a practical method, a new technical tool. Instead I got substantial insight into how to best employ the skills I hope to learn. Micki is a very capable and clear presenter, so her ideas will stay with me until I have use for them. Meanwhile, I have signed up for Baruch’s mapping class and am looking forward to getting in-depth with a tool.

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Source: https://dhpraxisfall16.commons.gc.cuny.edu/2016/11/03/data-visualization-with-micki-kaufman/

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