Form and Function: Let Your Audience’s Needs Drive Your Data Visualization Choices

A former Urban colleague and I used to debate what’s most important when it comes to visualizing data: the data or the audience? It’s sort of a chicken-and-egg question because without any data, there’s nothing to visualize and thus no audience. But with no audience, why would we make a graph in the first place?

I think it’s probably often the case that researchers don’t take the time to think carefully about their audience. Maybe they think their graph is useful for everyone or that the people who are the most interested in the graph will come find it. The communication and outreach efforts are not always prioritized. This could be that researchers don’t have the experience or knowledge about how to communicate their work, or perhaps they haven’t built time into their grant or contract agreement to do so. Whatever the reason, it’s clear that far too many graphs don’t effectively communicate the ideas or concepts.

As you think about visualizing your data, I would encourage you to think very carefully about your audience. And to help you on your way, I want to propose the following broad schematic of the different types of data visualizations:

I’ve designed this space with two perpendicular lines or spectrums. On the vertical spectrum are the general forms of your visualizations, running from static to interactive. Static visualizations provide all of information at once and are not active or moving. Interactive visualizations allow a transfer of information between the user and the interface. Sitting somewhere between the two are animated visualizations; these types of visualizations — for example, movies or online slideshows — do not necessarily permit the user to manipulate data points to create alternative results, but they might enable the user to control the pace or transitions of the visuals.

On the horizontal spectrum is the function of the visualization. Here, visuals run from explanatory visualizations to exploratory visualizations. Explanatory visualizations bring the main results to the forefront or surface key findings — they convey the author’s hypothesis or argument to the reader or user. Exploratory visualizations help users interact with a dataset or subject matter to uncover the findings themselves. These visualizations may not surface specific key findings or arguments but may enable to the user to do so on her own.

The intersection results in four quadrants.

Source: Image via Kristina Szucs

On each graph, the left vertical axis shows a movie’s Rotten Tomatoes score, and the right vertical axis shows the profitability of each movie, the gap between its gross revenues and budget. She doesn’t make a specific argument or point out specific details; instead, she lets you explore the data to find your own stories, hypotheses, and ideas.

In the bottom half of this schematic, we turn to interactive visualizations.

Remember that these axes are spectrums, so that a visualization can land somewhere in the middle of this space, or bring different aspects of these quadrants together. An early example of an Urban project is 27 Weeks and Counting that works across quadrants, combining interactive and static visualizations, audio clips from interviews, pictures, and an in-depth, sophisticated research report to examine the impacts of long-term unemployment.

There is no “right” or “wrong” quadrant here. What you choose depends on what your audience needs to help them do a better job, find insights, or make discoveries. The next time you are thinking about your visualization, think carefully about who are you are trying to communicate with and how your visualizations will best serve your audience.

-Jon Schwabish

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Data@Urban is a place to explore the code, data, products, and processes that bring Urban Institute research to life.