Coffee, then more coffee: a refreshing redesign of my National Coffee Day graphic

Partway through the semester, I celebrated National Coffee Day by visualizing my coffee consumption (so far!) that semester:

(A bigger vector version is on my website.)

Early in November, I got a cool surprise when Raven McKnight made her own version of the coffee graph:

Bigger version:

raven-mcknight-version.png

I am a huge fan of this remake: it solves several of the problems with my original design, and provides a very familiar snapshot of student life.

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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?

holocaust-shoes-close.jpgSource.

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.

 

holocaust-shoes-farSource.

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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:

match_drip_chart-nskill-long-01.png

(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?

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IMG_9502

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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Thoughts on the semester project: why protests?

On January 21, 2017, I put on my second-warmest hat and took to the streets of D.C., along with 200,000 of my closest friends. There was an extraordinary sense of connection, not just to the people around me, but to my family around the country who sent me pictures from their marches, and to the 424 other Women’s Marches happening at the same time.

I felt that sense of connection over and over again while protesting. And this year, I found a data set from Count Love that catalogued protests in America from January 2017 through the present. I’m spending an entire semester analyzing and visualizing that data.

Why protests? As I mentioned, I have some personal experience with the matter. Before going to grad school, I did my fair share of shouting and sign-waving in D.C. And the more protests I attend, the more I realize how little I know. The Women’s March was very different from counter-protesting pro-lifers at a women’s health clinic, which was very different from standing outside the Capitol building late into the night, dreading the vote on repealing Obamacare.

All those protests happened in the same place, about a consistent set of liberal positions. If there is that much variation in my not-very-varied experience, I can’t imagine how different protests are across the full landscape and ideological spectrum of this country.

I want to find out.

People protest because they care. I want to know what drove people to the streets. I want to know about nation-wide movements, and moments in local politics that never spread beyond one town or city.

I have four goals in this piece:

  1. Look at trends across the entire country
  2. Examine a few examples of local protests
  3. Invite readers to explore protests in and around their homes
  4. Draw on that sense of connection to build an aesthetic for the piece

Aside from lurking technical problems, I suspect my biggest challenge will be keeping the data art from unduly influencing the data analysis, and keeping the analysis from draining all the expressiveness from the art. But the only way to find out is to move forward with the project so: off I go!

Profiling protest data (or, what I did on my summer vacation)

This summer, I joined UVA’s Data Science for the Public Good program as a graduate fellow. I learned a ton, and I can’t speak highly enough of my experience there. One of the first lessons: ten weeks of data science sounds glamorous, but it’s four weeks of data profiling for every four weeks of data wrangling for every two weeks of data analysis.

There’s nothing new I can say about data wrangling. However, I want to take a moment to sing the praises of data profiling. Data profiling is a systematic way to dig into your data and evaluate fitness-for-use, beyond measures of central tendency and the first/last 5 rows. The data profiling method I learned at UVA has three pillars: completeness, uniqueness, and validity. I added an additional step: auditing for accuracy.

When you profile your data, you’re going to find stuff that looks weird! By investigating the weird stuff, you’ll get a real feel for your dataset’s texture and quirks, and a sense of what you can expect when working in it later.

I’ll show what can be learned from completeness, uniqueness, validity, and accuracy, with examples from the Count Love’s protest data. R code snippets are included where appropriate, and my full profiling script is available on github.

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