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.

In this, the last of my three blog posts about data viz, we walk through another data visualization I developed for Cultivate Learning at the University of Washington. Founded by Dr. Gail Joseph, Cultivate Learning aims to elevate the field of early childhood education by establishing itself as a bridge between research and practice.

The visualization in this post supports a report on teacher coaching practices and outcomes. It was developed with Cultivate Learning’s Research and Evaluation team. Below, you’ll see how I used strategies from S.P.A.R.K.| 5 strategies for the visual communication of science to go from words and numbers to a picture. We hope you’ll agree that it is far easier to understand the picture than the tables at a glance.

Table showing enrollment and drop outs in an education certificate program in Washington state
Map showing enrollments and drop outs in an education certificate program in Washington state.

Making comparisons easier following best practices in data visualization

The objective is to show enrollment in an early childhood education certificate program by region and to include some data about the enrolled students. We specifically wanted to include which certificate program the students were in (EC vs. ELO) and whether they dropped out. 

The regional origin of the data is relevant, so it’s helpful to display it on a map rather than in a table. The map shows Washington State, divided up into 6 regions consisting of several counties each. Another advantage of the map over the table is that both cohorts are combined into one visual. This makes comparisons between cohorts easier to see and understand.

The audience for this data visualization is the Washington State Department of Children, Youth, and Families (the source of funding for the research project). This audience already understands the overall goals of the project. So, this visualization highlights the important results throughout the state without going into a lot of detail about the different regions or the types of students depicted.

You’ll notice that the 6 regions are important, but not specific counties. So the counties are not labeled. Nonetheless, the lines that separate the counties are still included here; these boundaries are familiar to the audience, and including them provides context. In S.P.A.R.K., we teach that providing familiarity and context can help your audience connect with your data and your message.

Shapes are symbolic of the students enrolled in the programs. Solid and open circles represent EC and ELO certificate students, respectively. I varied the size of the circles to show whether the students dropped from the program or stayed in. 

I used color to show both similarity and contrast. Color unifies all the counties in a region and links each region’s labels and data with its location on the map (similarity). Color also differentiates each region, labels, and data from others in the map (contrast).

Finally, I used lines to connect the map to the labels and data for each cohort. Through these lines, the viewer can quickly relate the regions to their labels with little mental effort. 

For more information and instruction about how to use the principle of similarity; how to provide context and use symbols; how to understand and master using shape, size, and line; and best practices for color, check out our course S.P.A.R.K. | 5 strategies for the visual communication of science.

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