Examples for visualization calculations


Some of the projects have really transformative methods of turning one set of data (i.e. borrow direct data) into another set of seemingly unrelated data (environmental impact of borrow direct). It is often a good idea to show these calculations in your visualization, because the innovation is part of the charm of the visualization.

So how exactly do you put mathematical calculations in visualizations without seeming too wordy and complex? Here are some examples from Jessica Xu, an alum of the class and math visualization extraordinaire from 2014.

Type by Type

A measure of comparison

What if I made it myself



I thought this map was a really good example of starting with an idea, getting into the weeds with the data, and finding out what happens. The results could have been uninteresting, but as it turns out, they create a very cool map and illustrate something neat about a piece of information (zip codes) that are not frequently thought about.

You can read more about the visualization here.

The Shifting Mississippi

In a report for the US Army Corps of Engineers, in 1944 Fisk had completed a mammoth effort entitled, “Geological Investigation of the Alluvial Valley of the Lower Mississippi River” (downloadable here). It was his best attempt at tracking the meanderings of the Mississippi’s present and past bends, visualized as gorgeous maps.

Of course, mapping over 2,000 miles of river and its previous structure based on the available physical evidence is a tall order (though Fisk completed the project in three years, which is unbelievable), so many of the colorful curves in Fisk’s maps are a combination of speculation, interpretation, and extrapolation. Nevertheless, his maps are an incredible visualization of a living river.



“Out of your Hands”- physical predictors of illness

The design of the first headline (explaining that certain physical characteristics increase or decrease the risk of certain ailments) made it difficult to understand the first graphic immediately, but the other 2 visualizations (by gender and race) are better displayed while being especially problematic for the simplified world of information design. With no other data visible, the user has no insight into the science behind what might make a man, woman, or person of a particular race more likely to contract a certain illness that others, creating the potential for bias, stereotyping, and discrimination.