Infographics are a powerful source to portray vast chunks of information. Some are accurate while others are deceiving, due to an error left unnoticed by the designers or an intentional choice to persuade people. There are some mistakes I realized from analyzing several infographics that people need to avoid when visualizing data.
The Lie Factor principle states, “The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the quantities represented”. It helps to point out an error in the Rubber-band Scales graph that compares the income of doctors to other professionals from 1939–1976. The graph depicts a linear growth in income for doctors but the years, represented on the x-axis, have varying intervals between them. More precisely, the widths of the bars are the same and every bar is seen to span over a year label on the x-axis. It seems to indicate that the interval between each bar is by a constant of one, which is not the case. Thus, the graph is not as accurate as it looks. It is good for an interval between axes in a graph to be a constant factor to allow for accurate mapping of data.
Color-coding of an infographic also plays a huge role on how people will interpret it, especially if its sole purpose was to relay information through color. The color-coding in this picture of cartel territories in Mexico makes some areas indistinguishable from the others. This is because different versions of blue were used on the whole map distinguish territories and some of these variants look the same in the human eye. It’s advisable to use completely different colors to color-code an infographic to allow for easy interpretation of information.
Aside from highlighting some mistakes to avoid when visualizing data, Lin’s lecture also talked about some ethical decisions that need to be made when someone wants to visualize humanities. A major concern Lin points out is that some data visualizations are vague in the way they relay information. In particular, they don’t categorize information in a way that appeals to different sets of things or individuals. For example, instead of designing an infographic showing the unemployment rate of 335 million people in the US, it’s better to break the information down by race, gender, state, etc, to get a good sense of who needs more help. Information as such helps researchers and other people in the DH community to point out problems in society that needs to be addressed.
