Working for the marrow: a review of Info We Trust

Adapted from correspondence with the author.

Grad school has made me into a mercenary reader. My habit is to tear the meat off of a book, throw the bones back, and move on to the next assignment. This is not a satisfying way to engage with RJ Andrews’s book, Info We Trust. The book isn’t meat–it’s marrow. It’s rich, it’s rewarding, and it requires a lot more work from me to be nourishing.

Many data visualization books read like textbooks. Funny, personable textbooks, but textbooks all the same. Info We Trust is more of a meditation. Gentle explanations of chart types meander through a speculative history of the human perception of up and down. Best practices for table design emerge from a section about the history of bureaucracy. There is no neat delineation between background, body, and exercises: it’s all of a piece. I suspect that is the point.

Info We Trust tells a grand story about civilization. The first three chapters are a human history of information, connecting data work today to the world before electronic record-keeping. I’m not immune to poetry, and I have a long-standing (if often neglected) love affair with history. In my experience, history is spiky with context, competing interests, and strange accidents. The narrative presented by Info We Trust is so smooth and straightforward that I find it suspect. As a heroic epic, it works. As a history, I’m not quite willing to take it on faith.

However, that’s also part of what I enjoyed about the book. In the chapter on storytelling, Andrews wrote, “Great stories are rich with opportunities for the listener to make connections on their own. These self-made connections help the story leap off the page and into the reader’s imaginative reality. The more the story becomes alive in the reader’s head, the more meaningful the story becomes.” If it isn’t obvious that I struggled with this book: I struggled with this book! But that struggle brought it to life. I came face-to-face with what I thought I knew, where I was willing to listen, and my own biases.

The second half of the book is rich with opportunities for positive connections, particularly in the chapters on museum design, storytelling, engineering, and advertising. Andrews opens doors to unexpected worlds, allows me to make my own connections, and lets me find value in my own way. In the cathedral case study, I got to see him draw those connections, too. The “we” in the title is not just a figure of speech. I felt like I was sitting in conversation with Andrews throughout the book. The extensive marginalia presented alternate-universe versions of that conversation, where we split away from the main narrative to wander down a different rabbit hole.

Info We Trust is a generous and deeply human reflection on data. There is plenty of concrete advice about visualization, but it is woven into the narrative, not plucked out, polished, and ready for use. Nor should it be. The field has plenty of technical manuals. It doesn’t have anything quite like this.

My habit as a reader is to ask, what is this book trying to do? What is it going to teach me? Andrews flips those questions back around: what am I going to do with the book? How am I going to learn from it? Info We Trust is not a list of best practices, an in-depth history, or an immediate return on investment. However, it is a refresh on the craft, a feast for the eyes, and an opportunity to think deeply by drawing connections. I’m grateful for the chance to wrestle with this text, and I expect I’ll return to the mat soon.

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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:

mapcompbox-01

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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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.

Continue reading “Mercy on our minds: lightening cognitive load with the known-new contract”

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:

swordgraphcomplete-01

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.

Continue reading “Sword Graphs Part II: Abstraction in Self-Encoding”