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 to problems in computational design. Our starting point is the so-called tangent-point energy, which provides an infinite barrier to self-intersection. In contrast to local collision detection strategies used in, e.g., physical simulation, this energy considers interactions between all pairs of points, and is hence useful for global shape optimization: local minima tend to be aesthetically pleasing, physically valid, and nicely distributed in space.

Be sure to watch the video demo.

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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 list of recommendations regarding timeline readability:

  1. Use linear vertical timelines for situations which require fast data lookup.
  2. Avoid spiral timelines when the task requires fast lookup.
  3. If you use a more creative, expressive shape, such as a spiral timeline, also include a tutorial or visual cues to assist the user in learning and understanding.

Also: it “heavily depends on the context.”

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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”, “exciting”, and even “emotionally touching.” At the same time, though, some participants found it confusing: “the dots flew everywhere.”

This is a dilemma. Do we make users happy, or do we help them be effective? After the novelty effect wears off, will we all wake up with an animation hangover and just want our graphs to stay still so we can read them?

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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 impacted by these models (sometimes unjustly). If what we mean by “visualization literacy” is narrow enough, or rare enough, then we’re already setting ourselves mental upper bounds for the number of people we’ll impact with our work.

This was an interesting perspective. I’m used to listening to or reading from people on the presentation side of visualization, in which case it’s your job to raise literacy. You should imagine what others are thinking and explain any points of possible confusion with annotation and intuitive visual encodings.

Don’t ever use “people won’t understand it” as a crutch.

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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 Kosara, and Enrico Bertini, aims to explain the findings and the studies to a more general audience. (The UW Interactive Data Lab’s feed comes to mind.) Maybe the “read later” becomes read.

I’m looking forward to learning more. These projects have a tendency to start with a lot of energy and then fizzle out, so I’m hoping we can nudge this a bit to urge them on. Follow along here.

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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 the most important one to make. However, deciding what to visualize is often equally, if not more, important, than deciding how to visualize it. Take this simple example. Sometime a graph provides better answers to a question when the information is expressed in terms of percentages than absolute values. I think it would be extremely helpful if we could better understand and characterize the role data transformation plays in visualization. My impression is that we tend to overemphasize graphical perception when content is what really makes a difference in many cases.

Getting to that what often requires iteration between the analysis and presentation facets of visualization. I spend about the same time on the analysis side as on presentation, and that’s only because I’m more fluent with my analysis tools. I don’t have to spend a lot of time reading documentation. The amount of production during the analysis phase is definitely much higher.

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Most cited research papers

Top papers

In the department of comparing large numbers to objects and situations that are slightly more relatable, this graphic from Nature explores citations in research.

The bar on the left shows the height of a theoretical stack of papers that represents the first page of every paper cataloged in Web of Science. It would almost reach the height of Mount Kilimanjaro. The breakout stack is a zoomed in view of the 14,351 paper pages with at least 1,000 citations, and finally, the magnified orange section represents the top 100 papers. Also a flying bug.

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