After reading Anastasia Salter‘s ProfHacker post on D3plus visualizations, I was intrigued by D3’s ability to create creative, interactive visualizations. I’ve been thinking about exploring other paths to visualizing my genre data beyond network graphs, and this seemed like an intuitive, clear approach. After adopting the example she recommended, I created the following graph of genres at rival New York theatres in the 1839-1840 season: The Bowery Theatre and The Park Theatre (see this post for another view of the data).
As in a network graph, the size of the nodes within the bubbles represents the number of productions of each genre for that theatre. While it does not show connections between theatres, it can help give us a sense of the generic fingerprint of each theatre.
The result isn’t earthshattering, but a) it’s more intuitive and effective than a bar chart, b) I did it in about 30 minutes, so c) it’s looking like a great gateway drug to the rest of D3!
For a more complete and interactive graph, see the code that I adapted from this D3plus example.
Following my previous post, which looked at Genre Networks in five antebellum New York City theatres, I have started working on a graph of actors and actresses through August-December 1839. Using the same theatres (The Park, the Bowery, The National, The National (at Niblo’s), and the Chatham), I built a different graph that attempts to measure what actors appeared with what actresses. I am interested in finding out how connected prominent actresses were to the male stars of the period and vice/versa. The idea for this project began while I was transcribing Odell and noticing that there was a high number of theatrical couples appearing together onstage. I was curious to see if having a husband/wife team onstage was an significant draw, so I recorded how often husband/wife pairs were present in productions compared to those same actors appearing with someone else.
From the data that I have used, the answer is highly variable. What I did find, however, suggests that a look at actor networks in the period can provide insight into the lives of performers that might be more obscure if we only looked at the data on a close basis. This is what I love about digital humanities work: it has the potential to provide a wider perspective that one might not otherwise notice on the day-by-day scale. However, as many people will say, “distant reading” is not an end-product, but a means to indicate directions for further work. In making this graph, I found some great clues that I look forward to researching in detail.
When I first saw Gephi (in a talk by Micki Kaufman on Kissinger), it completely blew my mind. Like many, I was wowed by the pretty graphs. There were shapes, colors, and who doesn’t like to see a lot of important-looking circles connected by all sorts of lines? Although I had little to no experience in Digital Humanities, I wanted to do that. Badly. And I did: I found out the first rule of gephi: it’s easy to make a pretty visualization that signifies almost nothing.
Well, there was a dissertation to finish, work to pursue, and a million distractions, so, while I concentrated on Digital Pedagogy, I never quite got back to Network Analysis for a while. Now, after a lot of reading in Network Theory, Social Network Analysis, and experimenting with software, I am beginning to use network visualization for good. This is the beginning of a research/visualization project that I’m working on currently to answer some historical questions about nineteenth-century New York theatre.