Activity 13: Critiquing Data Visualizations

https://www.nytimes.com/interactive/2019/01/26/opinion/sunday/paths-to-congress.html

Before taking this class, I admit I would have been very inclined to take a data visualizations at face value. Data would at times feel like an unquestionable black box, and if I failed to understand a what a visualization was communicating, I took that as a personal fault rather than reason to question whether or not a visualization was good. But now, the questions come much more naturally: What is the argument of a visualization? By what means was the data constructed? What are its sources and variables? How well does the visualization fit its purpose? How intuitively does it convey its information and argument? And perhaps most importantly, what does the given visualization overlook or leave out? Does it actually contribute something to the analysis, or is it merely superficial?

A prime example of this process came in interrogating the above New York Times article. At a glance, this leaves the impression of a visualization that is well made (i.e. strong visual appeal) and fits the creator’s purpose. The author argues that there are certain patterns in the lives of people who become Congressional representatives which separate them from the American public, and we see in the visualization several points where the different representatives’ “paths” converge. But on closer inspection, the creator is trying to make it look like there is more substance than there actually is. The X-axis falsely implies a chronological order, when in reality, why “real estate” is closer to “military” than it is to “private law” on the X-axis is anyone’s guess. The Y-axis also has no clear meaning. Worse still, the visualization lacks data transparency: it offers only a mild disclaimer that items are not in chronological order in small print at the beginning, the methodology is stuck at the end in small print, whilst, despite allowing viewers to single out the “path” of an individual representative, the descriptive details like how long a said person was in a given line of work is left out. As far as this visualization is concerned, someone who worked on Wall Street for one year before quitting to get a Masters of Public Policy is the same as someone who got a Masters of Business Administration and then worked on Wall Street for 10 years. In a word: the author could have been far more honest with a different visualization. People can lie with data, either through convenient omissions or visualizations that obscure their flaws, making the ability to interrogate data all the more important as a historian or general consumer of knowledge.

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