Data Visualization and Knowledge Design

Data visualization is not only a way for exploring the dataset and doing data discovery, but also a way to present the outputs of the research by displaying capta in a way that supports and amplifies a cognitive understanding of the dataset/corpus (Card et al. 1999: 7). What is also known as visual analytics is a “science of analytical reasoning facilitated by interactive visual interfaces” (Thomas and Cook 2006: 4), a concept that can be translated with the notion of in-formation design (Meirelles 2013). Once knowledge is collected and discovered in the form of data/capta, in order to become understandable, it has to undergo a structural design, which esta-blish a point of access to the “information architecture” (Morville and Rosenfeld 2006) of the da-taset/corpus previously selected. In this phase a level of interpretation cannot be avoided. As Drucker reminds us, in Digital Humanities,

to expose the constructedness of data as capta a number of systematic changes have to be applied to the creation of graphical displays. That is the foundation and purpose of a humanistic approach to the qualitative display of graphical information. Read that last formulation carefully, humanistic approach means that the premises are rooted in the recognition of the interpretative nature of knowledge, that the display itself is conceived to embody qualitative expressions, and that the information is understood as graphically constituted.

(Drucker 2011, online)

On the one hand, during data collection, researchers tend to move in the domain of a sus-pension of judgment on the actual content and meaning of the dataset, by limiting themselves to defining a pertinent question and verifying the correctness of the information as preliminary ac-tions to consequent exploraac-tions of the data. On the other hand, with data visualization they are called to actively position themselves by visually expressing a perspective on the corpus, and suggesting possible interpretations - such as distant reading does. As Drucker explains, “Informa-tion graphics are visualiza“Informa-tions based on abstrac“Informa-tions of statistical data. All informa“Informa-tion visualiza-tions are metrics expressed as graphics. Visualizavisualiza-tions are always interpretavisualiza-tions—data does not have an inherent visual form that merely gives rise to a graphic expression.” (Drucker 2014: 7)

In this sense, visual displays can be used to guide the analysis and orientate possible in-terpretations about density in production in a certain time frame, networks of distribution and main industrial players. Data visualization and knowledge design are therefore means for pur-suing a methodology based on thick description (Geertz 1973). If, together with cultural ecology, we consider culture as a large-scale system of relationships, thick description through visual de-sign is one of the ways to accomplish an interpretative inquiry of such “a stratified hierarchy of meaningful structures.” (Geertz 1973: 7) In addition to offering an interpretation of culture, in-formation visualization can be deployed for a variety of contextual purposes: to simply record or store information in the form of data, metadata or multimedia objects over the course of the

re-search, to facilitate the retrieval of such information, to support collateral inferences or provide models for understanding complex cultural dynamics. As Toby Segaran and Jeff Hammerbacher frame it, data visualization can be thought of as a story: “The main character is the user, we can go two ways. A story of charts and graphs might read a lot like a textbook; however, a story with context, relationships, interactions, patterns, and explanations reads like a novel […] We want something in between the textbook and novel when we visualize personal data. We want to present the facts, but we also want to provide context, like the who, what, when, where, and why of the numbers.” (Segaran and Hammerbacher 2009: 7)

While large corpora bear complexity and contain unrelated data, data visualization helps framing a more intuitive reading of the dataset and contributes in defining relationships, trends, patterns, and a meaningful narrative. It is in this spirit that the Digital Humanities scholar Jeffrey Schnapp proposed the notion of knowledge design (Schnapp 2014), in order to explore the poten-tialities for creating new forms of knowledge within humanistic inquiry through the use of digital data, digital media, digital tools, digital environments and ultimately digital visualization. In a way, data visualization is therefore a return to the origins of the research and the questions I in-tended to answer, as to the positioning of the anthology form from the history of U.S. television up until its role in the digital landscape. For the first part of my analysis, I wanted to visualize the diachronic evolution of television anthologies in the United States, by looking at their density in specific decades. This led me to opt for an interactive timeline, designed to visualize data related to timeframes, on which I added complementary information relevant to my project. Starting from the final database with the data collected, filtered and cleaned, I generated an Excel’s

spreadsheet, which was then visualized on Timeline JS, an open-source tool for the visual repre-sentation of temporal data developed by the Northwestern University’s KnightLab.

A visual chronology of the anthology form in U.S. television can take several graphic shapes: from a simple list of titles , to the more detailed tabular form converted into a Microsoft ³² Excel file (see fig. 6), and eventually transformed into a timeline visualization . ³³

Figure 6. The final dataset as visualized in tabular form (Excel file format).

! “Anthology Series.” Wikipedia. Retrieved September 5, 2019. https://en.wikipedia.org/w/index.php?title=Antho

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-logy_series&oldid=916126630 ; “Category:American Anthology Television Series.” Wikipedia. Retrieved September 5, 2019.


https://en.wikipedia.org/w/index.php?title=Category:American_anthology_television_series&oldid=889985317.

See:

https://cdn.knightlab.com/libs/timeline3/latest/embed/index.html?source=1PwFUbBvYgipzBKb6FDTDh73-33

ZAZ5dpIjWDS5mUfOdc8&font=Default&lang=en&initial_zoom=2&height=650.

Exporting the Excel arrays into Timeline JS is a way to convert the tabular form into a more meaningful visualization able to effectively represent temporal data. As in other projects in digital humanities, the data visualization I will present in paragraph 3.3. reaches its scope of going beyond a simple analytical operation by offering a visual model that is: i) dynamic, as culture also is, showing a landscape subjected to contextual mutations, evolving practices and new findings; ii) interactive, since the interface allows for personalized exploration and the visual design space enacts a customized experience; iii) scalar, in order to welcome perspectives at dif-ferent scales and a level of modularity in the reading (close, distant or mesoscopic); iv) open, in the sense that the resulting visualization is available online through open access for public use and reuse.