One of the basic tenants of writing and publishing of social science research is that your language should be as accessible and approachable as possible; for example, your thesis about disparities in educational achievement would probably not carry much weight if it couldn’t be interpreted by a non-academic audience. Social sciences can However, it seems that the same considerations have not been made for data visualizations interpreting social science data. Although social science research is meant to address complex issues with intricate and interesting minutiae, our visualizations are rarely treated with the same level of depth or care. Fields like Social Network Analysis, in which complex connections between actors become collapsed into an elaborate needlepoint of nodes, strike me as especially guilty.
This network visualization I created that semester comes to mind. This data represents copanelist data from the 2017 Animal Rights National Conference, and although the visualization makes sense within the context of an academic paper, it leaves much to be desired as a standalone visualization. For example, how is a viewer supposed to discern which nodes are important? In a network of this size, how can I add legible node labels in a static image? How can I relay what node size represents without a paragraph of context? Does the physical distance between nodes have a quantitative significance in this model, and if so how can I illustrate that to my audience? These are adjustments and considerations that I will be carrying through my work this semester.
Admittedly, some of these limitations are consequent of the software in which this visualization was rendered. However, I do not see that as an inevitability. As social scientists I think we underestimate the visualizations tools available in packages like Python, R, or SPSS, or are too scared to take a deep dive into these languages to press their capabilities. This is something I would like to address in order to streamline the workflow from data analysis to data visualization.
This is the point at which I see interactive visuals as a necessity. Using the above example, a flash-based network visualization that allowed the user to adjust labels or node sizes may be able to address some of these confusions; even better, a visualization that creates force-directed models based on different centrality measures in real time. Another interesting visual tool could be overlaying pictures or logos over individual nodes to address the issue of node labeling. These are the kind of use case limitations I see as a consumer of data visualizations that I would like investigate and address in my own work.
My goal for this course is to strike a balance between interesting visual storytelling and interpretive clarity with my visualizations. Particularly in regards to interactive data visualizations, I want to be able to relay complex information and offer tools to help clients or viewers interpret that information using different metrics they may find most suitable. Most of my data visualizations have been drafted with print or academic writing in mind, and as a result lack the panache and intuitiveness that I would like to see in visualizations meant for web publication. I would like to take a deep dive into packages like Tableau or Keshif to see how my work could benefit from a more interactive approach to data analysis and presentation.