Data Visualization Critique
You will be responsible for posting to the Canvas Discussion Board four examples of data visualization being used “in the wild.” This can come from a news source, social media, think tank report, YouTube commercial, academic article, or virtually any other source that has some relevance to your life. Your assignment is to post a link to the source and critically reflect on the visualization using some aspect of either: 1) D’Ignazio’s framework for implementing feminist data viz (see reading & discussion for Jan 13); 2) Cairo’s framework for evaluating the qualities of great visualizations (see reading for Jan 15); or 3) some amalgamation of the two frameworks. Prior to the first due date, we will practice evaluating data visualizations using this framework so that you have a general idea of how to approach the assignment. I expect your reflections to be about 500 words (~1 single-spaced page).
Feminist Data Visualization
Krause cites D’lgnazio with the following quote:
“Feminist standpoint theory would say that the issue is that all knowledge is socially situated and that the perspectives of oppressed groups including women, minorities and others are systematically excluded from ‘general’ knowledge.”
Krause argues that:
“Data collection, analysis, and visualization are not neutral. All we can do is be honest and ensure we are transparent about our choices and limitations.”
D’lgnazio offers three recommendations for incorporating feminist approaches to data viz:
1. Come up with new ways to depict the limitations of a visualization.
2. Reference the material economy behind the data.
3. Facilitate dissent.
Each post is worth five points. You will receive full credit if you post a link and provide a ~500 word (or longer if you desire) narrative that represents a meaningful attempt to reflect on the example using some aspect of Cairo’s framework. Two points will be given to those who simply post a link without a narrative, and three points will be given to those whose narrative lacks any meaningful reflection. My standard for what constitutes “meaningful” will increase as the quarter progresses, as I will be looking for growth in the depth with which you are able to critically reflect on the technical and contextual details of data visualization.
The following represent suggestions for how you might structure your critique:
1. Start by discussing the background of the visualization, such as information about the publisher, source(s) of data used, and the general context of publication (i.e., What was happening in the world that motivated or gave meaning to the visualization?).
2. Who appears to be the target audience(s) for this visualization? What qualities about the visualization and its context (i.e., where it is published) support your conclusion?
3. What story – or stories – is the author of the visualization intending to tell? What insights were gained by visualizing the data in this way that text alone could not accomplish?
4. What are some alternative ways to interpret the visualization that may not have been intended by the author? In what ways, if any, is the visualization potentially misleading?
5. How, if at all, might the interpretation of the visualization change depending on who is reading the visualization? What factors might influence these changes (e.g., the identities of the reader, level of education, cultural familiarity)?
6. What else strikes you about the visualization? How might Cairo and/or D’Ignazio suggest that it be improved based on their respective frameworks for creating visualizations?
I do not expect that you will go into the same level of depth for all of these questions. Instead, I offer them here as a guide to help you structure your analysis. Ultimately, I am looking for you to move beyond simply looking at data visualizations and begin the practice of reading them.