This week’s reading really made me question what the bounds of what we can consider a data visualization. Initially coming in, my concept of a data visualization had three qualities:

1. The source of the data must be capable of being transformed into something numerical, or categorical.
2. The visualization itself requires some sort of translation between the data source to the final product that results in abstraction.
3. It is the final product, the visualization (the representation of a story) that holds the greatest value because it can give us new insights into the data

One example that made me question my assumption was the use of photos as data visualizations.  The example used was photos as a means to reveal how the virus has changed our social landscapes. Looking back to my three qualities: the source of data, the changes in social landscapes, are being transformed and translated into RGB values when they are captured as images, and it is indeed the final image that holds some insight into the data we captured. The assumption that photographs makes me question is if data visualizations necessarily have to be abstractions of the data itself? Photographs take away this layer of abstraction (to graphs, lines, etc.) and quite literally capture capture all data points as photos of light. And if photographs can be considered data visualizations, then would all other types of images (i.e. drawings) also be considered data visualizations? Or is what makes a data visualization not necessarily the medium through which it is captured but rather the data itself and what can be interpreted from it? Thus, what forms or kinds of data are and are not capable of being turned into visualizations; or are all individual pieces of data, both qualitative and quantitative capable of being turned into visualizations once they are aggregated and put into relationship with other data points?

Another example I was thinking about was with data visualization activity “Mapping Ourselves,” where individuals used data visualizations as forms of reflection on their networks of care. It’s with this example that I question the value of a visualization beyond just insights into the data itself. Here, the value and purpose of creating data visualizations is not necessarily the visualization itself but being able to use the process of visualization as vehicle for their own “data processing.” Participating in these data visualizations also allows individuals to be part of the “imagined communities,” which is crucial during these periods of isolation; it reminded me of the example of the Datafication of Health where individuals on the Quantified Self used their own data as not just a means of empowerment but also community formation.