research

8 posts
What works in visualization, scientifically speaking

Steven L. Franconeri, Lace M. Padilla, Priti Shah, Jeffrey M. Zacks, and Jessica Hullman published in Psychological Science in the Public Interest an expansive review of what researchers know so far about how visualization works: Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially...

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Repulsive curves

Chris Yu, Henrik Schumacher, and Keenan Crane from Carnegie Mellon University are working on repulsive curves, which is a method to efficiently unravel curves so that they don’t overlap: Curves play a fundamental role across computer graphics, physical simulation, and mathematical visualization, yet most tools for curve design do nothing to prevent crossings or self-intersections. This paper develops efficient algorithms for (self-)repulsion of plane and space curves that are well-suited...

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Evaluating timeline layouts

To show events over time, you can use a timeline, which is often marks on a line that runs from less recent to more recent. But you can vary the shape. Sara Di Bartolomeo and her group researched the effectiveness of different layouts: Considering the findings of our experiment, we formulated some design recommendations for timelines using one of the data set types we took into account. Here is a...

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Animation in visualization, revisited a decade later

Rewind to 2006 when Hans Rosling’s talk using moving bubbles was at peak attention. Researchers studied whether animation in visualization was a good thing. Danyel Fisher revisits their research a decade later. While they found that readers didn’t get much more accuracy from the movement versus other method, there was a big but: But we also found that users really liked the animation view: Study participants described it as “fun”,...

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Defining visualization literacy

Michael Correll on the use of “visualization literacy” in research: If people (and, by some definitions, many or even most people) are chart illiterates, then we may feel tempted to write those groups off. We may prioritize the design of visualizations to help the creators of, say, machine learning models, from whom we can presume a sufficient level of visual and statistical literacy, rather than the populations who may be...

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Visualization research for non-researchers

Reading visualization research papers can often feel like a slog. As a necessity, there’s usually a lot of jargon, references to William Cleveland and Robert McGill, and sometimes perception studies that lack a bit of rigor. So for practitioners or people generally interested in data communication, worthwhile research falls into a “read later” folder never to be seen again. Multiple Views, started by visualization researchers Jessica Hullman, Danielle Szafir, Robert...

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Data visualization for analysis and understanding complex problems

Enrico Bertini, a professor at New York University, delves into the less flashy but equally important branch of visualization: analysis. Much of what Enrico describes applies to the other branches too, so it’s worth the full read: One aspect of data visualization I have been discovering over the years is that when we talk about data visualization we often think that the choice of which graphical representation to use is...

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