Comparing Voyant, CartoDB, and Palladio

Voyant, CartoDB, and Palladio are somewhat difficult to compare because although they can all be used to analyze the same basic dataset (in this example, the WPA Slave Narratives and a smaller subset of the narratives – those that were conducted in Alabama), each is best used to focus on specific aspects of that dataset. In order to compare these tools, I reviewed my previous posts on each of them (Voyant, CartoDB, and Palladio). In terms of interface and usability, all of these tools were accessible to the novice (me). This is somewhat of a sidebar: since I used these tools in the context of a class, I was given data to work with. Learning how to format and clean up data for specific tools would probably be really useful, and should maybe be part of this course or other courses in the program. I know there are tools like DataWrangler, but this is something I feel sort of lost with, and I think this played into my ability to work with some tools more effectively than others. That is, I understand how Voyant uses complete texts that have been OCRed; I understand what stop words are and how full-text search works, primarily because I am a librarian. I understand the data used by CartoDB mostly because I sat through six days of ArcGIS classes. Palladio eluded me because I lacked the same sort of background knowledge, despite Scott Weingart’s lucid introduction to networks and examples like Mapping the Republic of Letters, Viral Texts, Linked Jazz, and the London Gallery Project, none of which I had trouble understanding. I think part of this is because the Alabama WPA Slave Narratives didn’t seem like an obvious fit for network analysis for me, but not understanding how the data behind it worked further confused the matter.

Anyway, in reviewing my previous posts, it was apparent that I found Voyant to reveal the most interesting aspects of the WPA Slave Narratives. This may be due to two things: 1. In my own research, I primarily do textual analysis, and so what Voyant makes possible is a more expansive version of methods I already use; and 2. Because these are narratives, and the dataset is the full-text of the narratives, this is a richer dataset compared to what I used in CartoDB and Palladio. This doesn’t mean, though, that mapping and network visualization were not useful approaches to the WPA Slave Narratives. The CartoDB maps that showed where the interviews were clustered within Alabama and were the interviewees were enslaved and the CartoDB animated map that showed that time period in which the Alabama interviews were conducted were revealing in ways the texts in Voyant were not, even if that information was available in the texts or the texts’ metadata. I recall trying to work with the differences between male and female interviewees and the subject matter of their interviews in Voyant (I don’t recall if it was successful – much of what I did in most of these tools was not), but graphing interview topics against interviewee gender (and, for that matter, type of slave) in Palladio was immediately and obviously informative. Information about specific interviewers and who they interviewed, which I think was also available in either the texts or the texts’ metadata in Voyant, was also much more obvious in Palladio.

The tools complement each other because they reveal distinctive aspects of the WPA Slave Narratives. Voyant reveals patterns in words, language, and discourse. CartoDB reveals geographies and spaces. Palladio reveals relationships. That sounds banal and inconclusive, but I think it is appropriate given that I’m still at a point where I see these tools primarily as exploratory and want to be careful about stating what they can and can’t do do. Musher’s article on the context of the WPA Slave Narratives highlights the importance of understanding, appreciating, and respecting the context of the data you’re working with, as does Weingart’s post on when to not use networks. All of the projects we’ve looked at are very careful about historicizing and situating their digital projects and only using the methods and tools that make sense given the research question and data. As I alluded to in my definition of digital humanities, I think it’s important that the field as a whole push against dominant discourses of technological utopianism, and foregrounding context and contingency is one way to do that.

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