Deceptive visualizations

We started by visualizing the CO2 emission of 2013 of different countries through color coding a world map, where we used a gradients of light to dark red to indicate the amount of CO2 emission. The argument we were trying to make is that China and the U.S. are the two largest contributors to CO2 emission.

As a next step, we refined the map by using the sum of CO2 emissions from 1990-2013 (as far as the dataset extends) to give a more precise representation of our arguments. The refined version, however, doesn’t have much difference compared with the original graph. The only major perceivable difference is that the US becomes darker, indicating that they become an even larger contributor to global CO2 emissons than prior, second only to China.

We flipped the color gradient as a deceptive tactic. Now, U.S. & China are the lightest shade of red and the rest of the country is a dark red. Since red is typically to denote warnings or dangerous behavior, this map is less alarming than when U.S. & China are the deepest shades of red.

As the second half of the assignment, we changed our color code from red to green, which is typically associated with environmentally-friendly behavior. At a quick glance, a user may think that since U.S. and China are the darkest green, they are the most-environmentally friendly. In reality, they release by far the most CO2 emissions of any country. We hope that common sense might overrule these deceptive tactics, but unfortunately, we could probably fool some people.

The simple choice of color, as we also learned about in the Tufte reading, makes a significant difference in our visualization. Just by using a different color scheme, or reversing a color scheme, it is possible to create deceptive visualizations.