CSCE 679 - Data Visualization

Fall 2021

Howdy!

My name is Matthew Barry, and I am a second-semester Masters student in Computer Science (MSCS / Thesis option). I am also a full-time software developer at the Texas A&M Center for Applied Technology. I received my undergraduate degree in 2014, majoring in both Computer Science and Applied Mathematics.

You might say that I am a “data head:” as a mathematician and computer scientist, I have always enjoyed collecting, working with, and making sense of data. A few years ago I learned how to use the D3 JavaScript library for a project at work and was fascinated by all of the creative and eye-catching examples people had made. The visualizations themselves were quite artistic and fun to interact with even if I did not know anything about the underlying data. Soon after, I stumbled upon the r/dataisbeautiful subreddit that took this concept to the next level. As part of the same project, I also learned how to use QGIS to combine various geospatial data sources into new ways of seeing the world.

My goal for this class is to learn new ways to convey information clearly through intuitive and artistic visualizations to inspire that same enjoyment in—and helpfulness to—other people.

A Helpful Visualization

Source: Mike Bostock, D3 Gallery, Observable

This visualization is a Sankey diagram depicting the proportional flow of energy from its sources to its ultimate use. Such diagrams are very intuitive to interpret. For example, in this visualization, the largest source in this data set is nuclear energy, all of which is used for thermal generation, which in turn is used primarily to power the electricity grid infrastructure, but most of it is lost to heat. By comparison, liquid energy is comprised almost entirely of oil, most of which is imported, and is in turn used to power a vast majority of transit applications.

An Unhelpful Visualization

Source: u/kevpluck on Reddit, r/dataisbeautiful

This visualization is titled, “Atmospheric CO₂ since the last ice age.” It shows a 3D volumetric depiction of CO₂ concentration in the atmosphere. However, the information is conveyed in terms of density, which is difficult to comprehend: the apparent size of the visualization remains the same, but the “particles” get packed closer together as the numbers increase; so it is difficult to interpret how much additional CO₂ is in the atmosphere.

A similar visualization on Twitter by the same author uses red and green to show comparative concentrations between naturally-occurring and man-made CO₂. This is very difficult for people with red-green colorblindness to see.