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:

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

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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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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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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:

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(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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Mercy on our minds: lightening cognitive load with the known-new contract

Lawrence Evalyn and I have an interdisciplinary friendship. I study research methods and data visualization; he studies eighteenth century literature and the digital humanities. I taught him about pivot tables; he taught me about sentence stress. I still think I got the better half of that exchange.

Sentence stress is my favorite tool for writing about complicated topics. Communicating complexity is also my goal in data visualization, so sentence stress is a natural complement to a conversation about data storytelling.

According to the concept of sentence stress, every sentence has two parts: the topic position and the stress position. A sentence’s stress position establishes a sentence’s main idea. It always comes just before a full stop. For example, “When the pirates come over, we played board games” emphasizes the board games. “At our board game night, we played with pirates” emphasizes the pirates.

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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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