There are few ways of presenting information that are as elegant and as well-suited to their purpose as maps. And because map visualizations immediately place data into a known context, like the map of a country or a state, they are appealing and easily understood.
But there is trouble in map visualization paradise. Geographic data can be misrepresented just like any other kind of data. And one of the most common distortions is one committed by Mediacloud, a Harvard University project, in this map of media coverage.
This cross-border viz infraction caused the Interpol division of the Viz Police to swing into action. Tableau Software Engineer Dirk Karis asks the question:
What country does the BBC cover most?
That viz makes it look like US is #1, Australia (or maybe Russia ) #2. I’m pretty sure the correct answer is the UK, but it’s really hard to see. A Tableau circle plot would make that jump out at you.
What’s going on is that the data (in this case, the amount of coverage) is encoded in color (darker green means more coverage) and plotted on a map. So far, so good. But then the outline of each country is filled in with that color, which all of a sudden makes large countries seem to have higher values than smaller countries. To Dirk’s point, the US is large and dark green in the BBC plot, but the UK is tiny and dark green. Which gets more coverage? Based on the visual presented here, most people will think it’s the US.
This isn’t just an academic question at Tableau. We build our geographic coding to use points, not filled maps, and we often get questions about adding filled maps as another option. While filled maps work well when the data represents densities, on all other occasions a filled map distorts the data. With a point you can more easily compare data points without having the view confounded by size of a given geography. Here’s an example:
As Tableau presents geo data, big states don’t get preferential treatment. Rhode Island and New Hampshire still matter. Live free or die!
The viz police are forced to cite Harvard’s mediacloud project for distortion of data in the third degree- that is, we give them credit for the infraction being unintentional.