Contributed by Jon Heggestad, SBU CDH graduate intern
In the opening lines of the English Studies special issue on data visualization in the humanities (2017), Stony Brook’s own Elyse Graham (English) defines this area of study as “a body of methods for exploring and explicating quantitative datasets,” adding that these methods have “grown in importance in the humanities with the rise of the digital age and, with it, the age of ‘big data’” (449). During my internship at SBU Library’s new Center for Digital Humanities, I’ll be exploring this field and developing data visualizations of my own as a means to both complement my current work and to familiarize myself in this emerging methodology.
In the description offered above, Professor Graham previews the two main ways in which data visualizations are used: for exploration (or discovery) and explication (making the implicit explicit). In looking to both uses of these methods, I consider how this field fits within a wider historical context.
An early example of what we might think of as a data visualization is John Snow’s map of London that helped to identify the source of the cholera outbreak in 1854. Highlighting the exploratory means of data visualizations, Dr. Snow mapped out the locations of those who had died as a result of the outbreak. In looking at the resulting illustration, he found that the majority of cases were centered around the Broad Street pump and determined this to be the source of contamination. Through the digital turn, his map has been reproduced by a group from Yale, using ArcGIS software.
While Snow’s visualization clearly relies on quantified data, NY-based artist Ward Shelley compacts enormous amounts of information without greatly focusing on numerical quantities. Many of his works artistically map out archives, cultural histories, and literary themes. The image above shows his 2011 “History of Science Fiction.” In exploring it, viewers/readers are able to see how the genre evolved, morphed, and split into new subgenres that ultimately broke away into categories of their own. In this way, Shelley’s visualization looks to time whereas Snow’s (with its more traditional map-like appearance) looks to space.
Although an earlier data visualization project by Stefanie Posavec (titled Literary Organism) was featured in Alan Galey and Stan Ruecker’s “How a Prototype Argues,” it is her later collaboration with Giorgia Lupi that I’d like to highlight here. In their co-authored text, Dear Data (2016), the two visual designers selected a weekly theme around which they then collected independent datasets; examples include the number of times they apologized in a week or the books each owned (Posavec’s contribution to this latter theme pictured above). Their findings were illustrated through unique infographics, drawn on postcards and then mailed to one another across the Atlantic.
This creative experiment immediately sets their methods in juxtaposition with the frequent accusation that data visualizations and other forms of distance reading inherently flatten the expression of complex information. Refusing this narrative around “Big Data,” however, Lupi and Posavec conceive of their project through a much more humanistic lens, suggesting in the introduction to their text that “we can use data to become more humane and to connect with ourselves and others at a deeper level” (pp. x-xi). In my own work, I plan to embrace their suggestion, looking to data visualizations as a means for a deeper, more humanistic (and often more accessible) connection to both my work and those it might impact.
Works Cited
Graham, Elyse. “Introduction: Data Visualisation and the Humanities.” English Studies, vol. 98, no. 5, 2017, pp. 449-458.
Lupi, Giorgia and Stefanie Posavec. Dear Data. Princeton Architectural Press, 2016.
Jon Heggestad
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