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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Interaction Without Interactivity

Last week I sat in on a guest lecture by Xaquín G.V., a visual editor at the New York Times. He showed a variety of interactive projects rich in hooks. One article from his time at the Guardian asked readers to create a stable coalition government by dragging and dropping political parties. Another interactive was a surprise at the end of an article about the gender pay gap, showing how much more money a man would have made than a woman in the time since the page was opened.

Screen Shot 2018-10-15 at 11.12.27 PM

Hook is an accurate term: as a reader, I immediately wanted to play with these visualizations. As a designer, I immediately wanted to make interactives like them. Unfortunately, I haven’t learned how to build interactive visualizations yet. So I started to wonder: how can I achieve a similar effect in static visualizations?

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Time and Space

I’m up to my ears in student loan data at the moment—not my own this time, thank God—and trying my hand at the peculiar alchemy of data visualization. A group of loans becomes a list of numbers, becomes an aggregation, becomes an angle or a color or a position in space. Encoding turns a thousand bills at a thousand kitchen tables into a digestible summary.

This week, I’ve also been thinking about how we encode attention: trading in space and time to communicate when an audience should stop and think. Take this graphic, part of a New York Times feature on the survivors of the Las Vegas massacre:


Here, space breaks one number down to its components: not to transform them in some way, or to compare between them, but to convey that there are individuals within an aggregation. The print version of the story traded inches of column space for individual figures of each victim:


The graphic doesn’t communicate any information beyond the labels: 456 injured, 413 shot, 58 killed. Instead it creates a space for reflection on the individuals within those numbers. The graphic isn’t space-efficient, because efficiency isn’t the point.

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