Visualizing Quantities, Categories, and Summaries
October 2021
This is the the second of four projects of the Visualize series. Our class was told to use earthquake data from the USGS and to design a visualization of the latest earthquake activity in terms of magnitude and how recent (1-hour, 1-day, 7-day, or 30-day. The challenge was not to use any maps for this exercise and to just focus on overall quantities, categories, and summaries implicit within the dataset.
Type: Static data visualization
Tools: Excel, Flourish, Figma
The Process
For this exercise, I wanted to look at 30 days of USGS earthquake data. The variables that I was most interested in were time, magnitude, and type. I would have really enjoyed looking at the longitude and latitude at another time so I could learn how to create a world map! After sketching a few ideas with my favorite variables in mind, I narrowed down my choices to either using a dot plot or a heatmap because they seemed more interesting than a bar or pie chart. Originally, I wanted to explore the average magnitude of each seismic type per day. I had finished working on the heatmap prototype using Flourish and quickly noticed a difference between explosions to earthquakes.
What caught my eye was that earthquakes seemed to have occurred consistently with roughly the same average magnitude, whereas explosions were less frequent but had a wider range of average magnitudes with especially higher averages on Oct. 3, 2021 and Oct. 9, 2021. How many earthquakes occurred each day compared to explosions? I switched out the average magnitude for the total number of occurrences in the heatmap. What a difference! Earthquakes happen hundreds of times more often than other seismic types every day. The heatmap was the most impactful visualization tool for this dataset.
What I Learned
I felt this was a good lesson in learning how to visualize different kinds of aggregates (sums, totals, averages, etc.) and prototyping quickly using Flourish for the first time. However, because the pace of the semester was quick, I wish I had more time to iterate on this further mainly for a couple of reasons. First, one of Ashley’s feedback was that it was easy to misread this data viz as a stacked bar chart instead of a heatmap. Second, having the same sequential color palette for two different aggregates was misleading and confusing. If I had more time, I would’ve wanted to think more carefully about my design and color choices and gather more feedback. Overall, I was still happy to have done the exercise and to play around with Flourish!