Unbearable witness

Content warning: this post discusses some rough stuff, including suicide and the Holocaust. Please take care of yourself. If you are struggling, please reach out to The National Suicide Prevention Lifeline (1-800-273-8255) or the Crisis Text Line (741741 in the US, 686868 in Canada, and 85258 in Canada).

I don’t always know what to do with tragedy, but I know what to do with shoes. There are laces to tie and tongues to tug. When I see a shoe, I imagine it on my own feet. Would the leather bite into my ankles? How would the heels sound on a wooden floor?

The shoes are on display at the United States Holocaust Memorial Museum. They were confiscated from prisoners arriving at a concentration camp. When I visited the museum, I saw the shoes as shoes, and then I saw the empty spaces where their owners used to be. What would their voices sound like? How would it feel to hold their hands?


I have always seen the world as a place where the Holocaust happened. Jewish kids learn these things young. I also learned that if I wanted to engage with that part of my history, I had to find a way to dim down the horror. I wouldn’t grab a dish out of the oven without mitts. I don’t think about the Holocaust without a mental barrier.

Sometimes I open myself up to those feelings as a way to honor the dead. Sometimes an exhibit, like those shoes, peels me open. That’s not an accident. It’s a strategy.  Visualizing Information for Advocacy describes this as “bearable witnessing,” which “takes the viewer on a visual journey that slowly leads them in to the shocking part of the image.” Describing another exhibit about the Holocaust, the authors write, “The image of a pile of glasses becomes an analogy for something that may be too painful to look at directly.”

Contrary to the name, bearable witnessing isn’t bearable at all. I just don’t know that until I’ve borne it. And then it’s too late: I can’t distance myself from the shoes once I’ve imagined them on my feet.



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Paint drip charts (or, what IS that?)

At the end of my summer fellowship, I tweeted:

Tweet from @alyssafowers that reads, "It's POSTER TIME at my summer fellowship, which means that I hear "what IS that" every time someone walks behind my desk."

I can’t blame my coworkers for being confused when I was working on… this:


(A PDF version of the chart is here.)

I called it a paint drip chart. Or an, “everything sure is a mess, isn’t it” chart. Or a “distributions in three dimensions” chart.

I created the paint drip chart because I wanted to show proportions within individual cases. This chart is wild, absolute nonsense, and I’m very proud of it. However, it is also wild and absolute nonsense. So why on earth did I use it?

Making wacky graphs is fun. Using them to communicate is a risk. They take more attention to read, and there’s always a chance that the reader will give up entirely or misunderstand what the chart is trying to show.

I decided to use the paint drip chart for four reasons:

  1. The chart was a good match for the venue. I designed this graphic for a poster session and a presentation. In both scenarios, I was on hand to talk through the visualization and answer any questions. I usually go for clarity over attention-seeking, but…
  2. The chart is really good at pulling people in. At the poster session, the paint drip chart drew people across the room to ask me what it meant. During the presentation, audience members who had listened with polite interest started leaned forward, asking questions, and discussing policy implications.
  3. I was sharing the chart with data geeks. We presented at the National Center for Science and Engineering Statistics, and at an event called the Data Science for the Public Good symposium. I knew that my audience was used to interpreting data visualizations, so I gambled that they would stick with me through an explanation of the chart. They did.
  4. There wasn’t a simpler way to do it. I weighed the options and decided that showing matches within individual jobs was worth the demands of a complex chart.

Read on for more about the meaning of this paint drip chart, other versions that never saw the light of day, and instructions on how to make your own!

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Active legends

(part two of two)

In Glass Benches part one, I argued that it’s worth spending ink to help readers interpret unusual graphs. Plenty of designers have come to the same conclusions, and how-to-read sections aren’t uncommon in data visualization. See this heatmap from the Financial Times:

Screen Shot 2019-09-30 at 6.01.41 PM

The more intricate the visualization, the more complex the how-to-read becomes, like this cloud of mascaras from Sonja Kuijpers:

Screen Shot 2019-09-30 at 6.03.13 PM

Novel charts need explanation. But to be honest, I find how-to-read sections alienating. They take me away from the content of the visualization. Traditional legends doesn’t teach me anything about the topic at hand—they just teach me about the design of the visualization.

I’m a designer. I love design! But information design doesn’t mean information about design. It means designing to inform. I prefer active legends that demonstrate how to interpret the visualization by using part of the actual visualization. I also suspect that active legends are easier to remember than external legends. The reader uses an active legend to interpret real information, rather than passively observing it in a hypothetical situation.

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Glass benches: genre and minimalism in data visualization

(Part one of two)

I went to the campus art museum this weekend and found an unexpected puzzle. Which of these objects am I allowed to touch?



I go to museums a lot. In my mental model of a museum, anything with a little rope around it or a dark outline on the floor is an Exhibit and Not To Be Touched. By those rules, the bench is forbidden, but the plinth is fair game.

To the security guard’s amusement/alarm, I guessed wrong: I avoided the bench, and tried to put my sticky fingers on the shiny surface of the plinth. I was misled by genre.

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Coping with Minard’s non-Euclidean cartography

Charles-Joseph Minard (1781-1870) was a French civil engineer, visualization designer, and all-purpose nerd. He’s best remembered for the invention of flow maps, which show the quantities of materials, people, or traffic moving from one place to another. He also didn’t think much of North Africa, South America, or the existence of Ireland:


(Ireland added for emphasis.)

As Sandra Rendgen wrote in The Minard System,

“Minard quite deliberately and continually transgressed every idea of cartographic precision… his ‘non-Euclidean cartography’ is not the result of coincidence, incompetence, or mere negligence. On the contrary, we must consider it a clear decision on Minard’s part to treat cartography as an ‘auxiliary canvas’ on which his main story (i.e., the drama of the statistical numbers) unfolds.”

It’s easy to be appalled by Minard’s fast-and-loose approach to world geography. See this map of English coal exports in 1860:

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Left (or right) this way: pinpointing change with hedgehog maps

A rare and endangered group of maps emerges the morning after an election, only to disappear as soon as the news cycle moves on. You know, these guys:


Left to right, top to bottom: New York Times 2018, The Guardian 2018, New York Times 2016, Bloomberg 2018.

Wait! Come back! I miss you!

These maps punch way above their weight. I’d like to take a closer look at what they do and why they work. In the absence of another name, I am calling them hedgehog maps.

Hedgehog maps use arrows to indicate change, with the base of the arrow showing the location of the change, the length  indicating the magnitude of the change, and the angle indicating the direction. They’re distinct from flow maps, which use arrows to link two locations, and arrow plots, which uses the base of the arrow to indicate a starting value and the arrowhead to indicate a finishing value.

The result is striking, as intuitive as it is unquantifiable. Look at The Guardian’s map of changes in vote share during the 2018 midterm elections:

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Spinning the Concrete Dial with Hypothetical Outcome Plots

Blanket disclaimer: This is a post about animated visualizations, illustrated mostly by static screenshots. Please consider clicking through to see the actual visualizations! My screengrabs don’t do them justice.

I’ve been chewing on uncertainty visualizations since Matthew Kay’s excellent talk at Tapestry 2018. The recent release of the R package gganimate has also brought a number of animated visualizations across my feed, so let’s talk about an animated uncertainty visualization: hypothetical outcome plots (HOPs). What are they for, besides inspiring truly terrible puns?

One of the core functions of statistics is making inferences about a population based on limited information. Sometimes that means estimating a value (average sword price, for instance); sometimes that means modeling to describe the relationships between variables and to predict what might happen in the future. Those estimates and predictions look very precise when depicted as single points or figures. However, there is always uncertainty involved: other estimates that we could have gotten if we repeated the study, or a range of possible outcomes from the model. HOPs depict uncertainty by animating a sequence of outcomes that could occur, rather than showing a single number. (If you’d like to know more, I really can’t do better than Jessica Hullman’s original Medium post about HOPs.)

HOPs get a lot of press for making viewers encounter uncertainty, but that’s far from their only application. I think about HOPs as a kind of concreteness dial. They make estimations less concrete by forcing an audience to experience uncertainty, and they make processes more concrete by showing the different ways a model might play out.

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Framing questions and crochet hooks

Interlibrary loan has reclaimed my copy of Visualization Analysis and Design, so I’m on to the next book on my shelf: Information Visualization: Perception for Design by Colin Ware.

I stand behind Ware’s position that data visualization is a tool for cognitive work, an external aid that shores up memory and pattern perception. Our brains need tools to think through complicated information, the same way our hands need tools to weave cloth. I can see the numbers in a spreadsheet, but interpreting them is like trying turn a pile of yarn into fabric with nothing but my fingers. A simple tool like a crochet hook radically extends what I can do with raw materials.

I do, however, struggle with the profit model introduced in the first chapter. Ware writes that learning to interpret new graphic symbols comes with a cost, and that novel designs should be used only when their benefits outweigh the cost of learning to use them.

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Sword Graphs Part II: Abstraction in Self-Encoding

In Sword Graphs Part I, I introduced the concept of self-encoding with this chart:


The graphic is self-encoded because the images themselves represent a value, rather than that value being translated into a mark like a bar or dot. Information about the length of the blade is represented by the length of the blade: the sword encodes itself.

But why not go a step further and show actual photographs of the swords, or a step fewer and use the same generic outline for all of them? The choice of images in self-encoding depends on specificity and processing speed.

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Sword Graphs Part I: Self-Encoding

For your consideration, swords:


The sword graph was a bit of self-indulgent fun, but it did give me an opportunity to reflect on graph humor and the appeal of self-encoding. I created the term “self-encoding” to describe charts where the object being described represents (or encodes) itself, rather than being translated into a more abstract image like a bar or a dot. Self-encoding preserves important quantitative information (such as the length of a hilt) while also presenting additional qualitative information (the presence or absence of a pommel, the shape of the crossguard).

Sometimes self-encoding is just for fun. Consider these two classics of graph humor:

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