Many of the visualization methods we’ve been looking at this semester fall under the umbrella of distant viewing, and spatial data is no different. Rather than looking solely at the minute details that make up a data point, we’re allowed to take a huge step back and see what patterns, if any, emerge. Because the purpose of this type of visualization is to help illustrate these patterns, it can be versatile as both a research method and a way to express the results of that research. The process of placing textual data about locations into a representation of three-dimensional space can unearth a number of unanticipated complexities within the data used. This is also a very highly visual and contextual medium of representation, so while we did look at some literature on the topic, it made more sense to see as many examples as we could in order to compare and contrast.
Béatrice Joyeux-Prunel’s work with the Artl@s was a great introduction, since with her chapter “Digital Humanities for a Spatial, Global, and Social History of Art” we got an inside look into the processes and principles that went into the final product. I appreciated the very balanced look she took in the process of mapping; she advocates for mapping as a research method and not necessarily as a standalone product, and gives guidelines for making sure that the purpose of your maps is clear when they are reduced to static images. However, I’m not so sure the final result of the Artl@s project we looked at (Geography of the Parisian Art Market) was all that successful as a final product. The process of conducting this research may have been illuminating, but as we spoke about in class, the final map does a poor job of elaborating on its purpose, function, and ease of use. Having an adjustable timeline was a necessary feature, but the lack of data at each gallery location makes in-depth analysis difficult. This is especially true when juxtaposed with projects like Mapping Gothic France, which contains exhaustive documentation on each of the cathedrals included in the project. I thought that this project was very well scoped, which seems to be a key to success for digital visualization projects. They knew that location mapping was not the only point of the project- it was just one aspect of physicality, which was what they actually hoped to convey. Rather than 1900+ sparse data points they included ~200 thorough data points, which in conjunction with the time afforded to work on the project set it up for a great final product.
The shift from standard art historical thinking to cartological thinking also appears to have been a difficult one for both the Artl@s project and the Jaskot and Knowles project we looked at. For Jaskot and Knowles, even coming up with a shared vocabulary for architectural historians and historical geographers was difficult. In the article “Architecture and Maps, Databases and Archives: An Approach to Institutional History and the Built Environment in Nazi Germany,” Jaskot and Knowles describe their experience mapping Auschwitz-Birkenau. One important factor they discuss is the decision-making process to narrow down more generic information on construction into definitive data points. Disallowing these ambiguities feels counterintuitive to standard research methodologies, but in order to represent information in this very binary system, it was necessary. Jaskot asks a question in this article that I think is a much more important question than it seems at first glance: “How accurate did our digital models have to be to provide acceptable answers to our questions?” Rather than letting their judgement calls on preciseness sit as end-all-be-all determinations of fact, they were able to allow ambiguity back in by asking how much of that preciseness is actually reflected in the final product. And, since this project ended up acting as more of a research tool than a public-facing visualization, this still ended up surfacing some interesting questions and observations about the spatial and bureaucratic elements of the camp.
After weighing all of the examples we looked at, we created our own (much, much simpler) spatial maps. I was very intrigued by the narrative framing of StorymapJS, but didn’t think I had enough time to really give a project in it the detail it deserves. So, I looked at Google Maps instead. For my map, I took a look at tourist traps and roadside attractions; I think they’re such an interesting phenomena (and a wealth of weird vernacular art!), and I wanted to (hopefully) illustrate how the development of highways created and shaped many of these little kitschy spots.
I unfortunately didn’t get super far into the highways part of my map, but fortunately Google maps does display highways. Most of the 20 locations I chose are either in urban metropolises or right off of major interstates, and I was a bit surprised that this held true for both new and old attractions! I’m sure maps like this have been made a dozen times over, but I definitely would like to continue with this little project. I was also curious to see if specific types of attractions were concentrated in particular geographic zones, but I think I would need more data points to really analyze that. For instance, Georgia seems to have a lot of oddities museums and West Virginia has (at least) two cryptid museums- it would be interesting to interrogate why that is.
Jaskot, Paul B., and Kelly Ann Knowles. “An Approach to Institutional History and the Built Environment in Nazi Germany.” Getty News, February 15, 2017. https://www.getty.edu/news/dah_jaskot_knowles/.
Joyeux-Prunel, Béatrice. “Digital Humanities for a Spatial, Global, and Social History of Art.” In The Routledge Companion to Digital Humanities and Art History, 1st ed., edited by Kathryn Brown. Routledge, 2020. https://doi.org/10.4324/9780429505188-10.
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