A few years ago, data scientist Alex Lundry gave a fantastic presentation describing the ways data visualization is being used by political parties to push their own agendas. He showed a Republican visualization of the House Democrats' health plan — an infographic full of sinuous pipes and literal red tape, smattered with ugly unreadable fonts and unwelcoming 8-bit color palettes — next to a Democratic visualization of the same plan, which instead looked like an Easter basket, a perfectly designed and welcoming bundle of pastel circles.
The difference between the two visualizations, which present the same information, was striking. It's clear that the spin we're accustomed to hearing from politicians is now something we're going to be seeing from politicians as well
The most troubling part of all this is that "we the people" rarely have the skills to see how data is being twisted into each of these visualizations. We tend to treat data as "truth," as if it is immutable and only has one perspective to present. If someone uses data in a visualization, we are inclined to believe it. This myopia is not unlike imagining the red velvet cake we see in front of us to be the only thing that could have been created from the eggs and milk we mixed together to make it. We don't see in the finished product the many transformations and manipulations of the data that were involved, along with their inherent social, political, and technological biases.
Click or touch to see larger image. View the interactive version here.
source: New York Times
The Times begins with the "raw" data* from which you can compare the Republican interpretation (red-colored glasses) and Democrat interpretation (blue-colored glasses). As the window slides to either set of glasses, we're greeted by that party's familiar talking points, accompanied by the interpretation of the data that supports that view. The beauty of this visualization is not in either the Democrat or the Republican end products, but in the concise way it draws our attention to the process of visualizing data to suit our own ends.
Taken in isolation, either final visualization gives us an answer. Taken together, the opposing visualizations force us to ask questions.
Beyond raising awareness about political bias in data visualization, this piece employs a technique that many visualizations can benefit from: comparison. For example, while I can see how well I kept to my own budget by visualizing my monthly expenses, I see a different picture when I visualize my expenses against those of others in my demographic. Similarly, visualizing my spending by type vs. by time of day gives me entirely different views of the same data. The power in each of these examples comes from seeing the data from many perspectives, which altogether form a more informed view than any one individually.
I hope that, as we move into a world where people will increasingly be exposed to shiny infographics and visually-stunning data interactives, that more pieces remind us of the process and motives behind them. Maybe as data visualization becomes more democratized we'll learn this lesson through doing, or perhaps The New York Times and others will still have to remind us. Either way, I hope others will help bring to light what's going on behind the scenes so that we can take the task of visualizing data on with our eyes open.
*Let's leave aside the biases and assumptions in the data itself, e.g. how do we define unemployment?
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