This post was contributed by Picture as Portal® cofounder, Tami Tolpa. Tami has a Master of Fine Arts degree in Medical Illustration from the Rochester Institute of Technology.
Data visualization (data viz) is not new. It’s a tried and true means of portraying large amounts of information, and it has been around for a long time. Need proof? Check out the awesome picture below.
This diagram was created in the 1850’s by English social reformer, statistician, and founder of modern nursing Florence Nightingale. It shows the causes of mortality in the British army during the Crimean War, and the stark reality that far more soldiers were dying from infectious diseases in the hospitals (gray wedges) than from war wounds on the battlefield (pink wedges).
I love data visualization because it enhances our understanding of data by transforming numbers into meaningful pictures. And because it’s been around for so long, we have a good understanding of what does and what doesn’t work in data viz. In our S.P.A.R.K. online course, we teach visual communication principles that are incredibly useful when creating effective and impactful data visualizations. In this first of three posts on data viz, I’ll show you how I’ve applied some of the S.P.A.R.K. principles in my own data viz work. Let’s get started!
Making the data visualizations
Below is a typical set of data visualizations created in Microsoft Excel. While there are many packages that people use to make data visualizations—Excel, Tableau, R, LaTex, MatLab, Adobe Illustrator, etc.—the principles of visual communication are fundamental no matter what you use. The same goes for creating infographics. That’s why we don’t teach software in S.P.A.R.K. We teach core principles of visual perception and visual communication that apply broadly to illustrations, infographics, data visualizations, etc.
In this visualization, we’re looking at the number of dogs observed in 2 different parks over 6 hours. The intent is for the audience to compare the data from Park A with the data from Park B. As a designer, my task is to make the work of comparing the 2 sets of data as easy as possible. I did this by using several of the principles we teach in S.P.A.R.K.
Refining the data visualizations using S.P.A.R.K. principles
First, I changed the bar graphs to line graphs. The goal of this data visualization is to show how the number of dogs observed changes over time. Line graphs are better than bar graphs for showing a pattern of change across a variable—in this case, time.
Second, I placed the 2 line graphs on one set of axes to increase their proximity. This not only makes the work of comparing the data set easier, it also helps us perceive them as related. In addition, I placed the labels next to their lines rather than in the title or in a legend off to the side. Here again, proximity helps us associate the line with its meaning.
Finally, I kept this visualization clear by using best practices for color and text. I chose blue for both lines in the graph. This makes use of the principle of similarity. The lines don’t represent different things; they’re the same thing under different conditions. Making the lines similar colors once again makes it obvious that they’re related. I chose a dark and light shade of the same blue with adequate contrast for readers with color vision deficiencies.
I also changed the orientation of the text on the Y-axis from vertical to horizontal. Reading text at odd angles is difficult. The reader already has to use cognitive effort to interpret the data, so it’s good to make basic tasks like reading labels as easy as possible. Placing Y-axis labels vertically has gone unquestioned, but a best practice of keeping them horizontal is emerging.
I made all of these changes using Excel, the same software used to make the original bar graphs.
For more information and instruction about the principles of proximity and similarity; how to understand and master using contrast; and best practices for color and text, check out our course S.P.A.R.K. | 5 strategies for the visual communication of science. Stay tuned for the next 2 posts about data visualization.