Admittedly, a portfolio of visual works is not something I have had to consider up to this point in my ongoing will-they-won’t-they relationship with graduate school.
I started my search on the Information is Beautiful Awards website, which ironically gave me more ideas of what I wanted to avoid in my portfolio than what I would like to adopt. Their showcase is appears to be a floated list of blog entries for each visualization, however the result is much more cluttered than I would like:
One glaring issue I find with this approach is the disparate heights for the thumbnails. In the above example, nearly a third of the screen real-estate is dedicated to the middle visualization due to its vertical orientation, which detracts heavily from the other visualizations showcased on the page. Another issue is that the text area at the bottom of each post is a fixed height, which results in some posts seeming “squashed” due to the orientation of the thumbnail. An easy solution to this would be fixed thumbnail sizes for each visualization.
Another issue I have with this example is that the blurb beneath each thumbnail typically explains more about the dataset than the visualization itself. In the case of a data visualization portfolio, I think this section would be better suited as a place to explain what tools or software was used to create the visualization, with more information about the data on a separate webpage. Even if not the software used to produced the visual, this might be a place to explain particular features of the visualization, or particular design decisions that went into making the visualization.
Going outside the realm of data visualization, I think a “portfolio” that does this well is the demo reel for the design and development company Support Class. In their demo reel they provide visual examples of their work as well as itemized lists of the particular tools or considerations they had to make when creating their designs.
One portfolio I found that more closely mirrors my ideal portfolio comes from Visual Cinnamon. Their fixed thumbnails more closely mirror what I would like my portfolio to look like, and the mouseover effect that lists the visualization titles and tools used is a nice touch. The minimal screen space dedicated to the fixed header is also a design choice I am a fan of.
One change that I would like to make, and one I will probably adopt in my own portfolio, is limiting the number of visualizations per row to eliminate clutter. In this case, I would like to see the number of thumbnails limited to two per row, with the size of the thumbnails increased to show only one row per screen.
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.
Both of these line graphs were rendered using the same Python libraries. How can I better use these tools to create visual appealing and engaging visualizations?
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.