The chart reveals total Twitter followers of Democratic candidates who have more than 500K followers (this is the bubble size,) and how little or how much the candidates' followings overlap . You can find it in a story by Gus Wezerek and Oliver Roeder. Here's what they did:
The people following candidates on Twitter are those who want to receive a steady stream of information about at least part of the 2020 campaign. Understanding how that tribe operates can tell us something about an influential slice of the electorate. So off our web-scraper went, dredging up every follower of the 20 Democratic presidential candidates who FiveThirtyEight considered “major” in early May, when we ran our script.1 The result was a data set with almost 20 million entries, which you can download on GitHub.
That's right; the data is available on GitHub, where the authors authors also wrote: “If you use this data and find anything interesting, please let us know. Send your projects to @guswez or @ollie.”
There are many kinds of nonfiction books. Some feel like walking through a historical building following a fixed path with the help of an experienced tour guide who points out whatever we should pay attention to. That type of book takes you from point A (not knowing) to point B (knowing a bit more).
Other nonfiction books are meandering, playful, disjointed. They aren't intended to be structured like linear narratives with a beginning and an end. They feel more like if the aforementioned tour guide met you in the lobby of the building, opened all doors inside it, and told you: “I'll give you a brief introduction to understand where you are. After that, explore at will; you can find more information about the wondrous objects inside this building in labels next to them. Feel free to read some and ignore others.”
The goal of this type of book is not so much to teach; it's to inspire you and give you leads to new ideas, references, readings. If you remember the island of knowledge metaphor at the beginning of The Truthful Art, books of this kind may not expand your personal island, but they reveal promising directions to do so. Maybe coin the term “hyperbook” to refer to them (after hyperlink).
Barbara Tversky's recent Mind in Motion: How Action Shapes Thought belongs to this second type. After a few chapters that sounded a bit too basic, I felt enthralled by the breadth of the book. I underlined many passages, took copious notes on the margins and on the first blank pages, and wrote down the titles of many papers and books mentioned. If you work in design, or if you're interested in the inner workings of the brain, Mind in Motion is for you —maybe not because of its content per se, but because of the many thoughts it'll inspire.
Here are some relevant passages (apologies in advance for not transcribing them, but I read print books.) The main point of Mind in Motion is that spatial thought is the foundation of abstract thought:
Much —if not all— thinking is action exerted over mental objects:
Because of how useful they are, spatial skills should be more broadly taught. This includes graphs, maps, diagrams, infographics, and the like, a point I've made in recent talks and that I suggest in How Charts Lie:
The book offers suggestions on how to get started with that educational program:
The first half of Mind in Motion ends with a reflection about the relationship between perception, imagination, and action. The more we perceive, the more we can imagine; the more we can imagine, the more we can do —and the more we do, the more we can perceive:
The second half of the book is even more relevant to visualization and explanation graphics designers. Here's a passage about the importance of context and purpose; Tversky doesn't mention the audience as part of that context, although I think it's implied that that's the case —how much or little you guess your audience knows about the topic of your graphic should influence the way you design it:
Some thoughts on the design of information graphics, ending with some elementary rules of thumb, which may sound familiar to some readers of this blog:
And comments about whether visualization conventions are always really just conventions, which is why it's important to think carefully before going against them:
Tversky also describes multiple experiments related to the effectiveness of graphics, explanation diagrams, maps, animations, and many other representations, and she extracts general design lessons from them. I'd get the book just because of these and the references related to them at the end. Enjoy.
Jeff Heer has published the massive slide deck he prepared for a capstone talk at Eurovis, which you can watch here; there's also an related paper. Jeff has promised to write an article summarizing his main points. Here are a few personal takes, paraphrasing a bit:
(A) Visualization on its own isn't enough; it's always part of pipelines and processes. Therefore, it doesn't make sense to practice or study it in isolation. The future of visualization research and practice is in interdisciplinary synthesis, and “the practice of principled interdisciplinary thinking is our greatest asset”. Bravo:
(B) If we miss the focus on interdisciplinary collaborations visualization can go awry in different ways. The visualization process has many steps, and mistakes may appear in any of them. The challenge is that professionals from different backgrounds are capable of detecting problems in some of the steps below, but no one can detect problems in all steps if working alone. Quote: “We need analysis support tools & methodologies for end-to-end analysis, not siloed ‘statistics’ or ‘visualization’ tools”:
(C) Visualization has an accessibility problem: we need to explore sonification, physicalization, and other forms of encoding information. Being too late to it, this is something I'm becoming increasingly interested in; remember TwoTone:
(D) Multimodality —using visuals, text, sound, touch, and so on, either simultaneously or supplementing each other— is a world to be explored:“given a formal visualization specification, how might we re-target a design to other modalities?”
(E) There are tons of known unknowns and unknowns unknowns in visualization. If you are a researcher, Jeff's slide deck is an inspiring and endless source of ideas.
(F) My favorite part: Jeff takes Richard Hamming's “the purpose of computing is insight, not numbers” and Ben Schneiderman's deservedly famous “the purpose of visualization is insight, not pictures” mantras and proposes a new one: “The ultimate subject of the visualization research community is people, not pictures.” I'd write “should be” instead of “is”, but I'd ask for an applause anyway:
Last night my seven-year-old-daughter finally understood what I do for a living. For years she thought I “design maps” —which isn't incorrect, but it's not the whole story— and she knew I also create histograms like the ones she's used in school to measure the distribution of heights of students in her classroom. But I don't think that she fully realized that “information graphics” is a field and a job until we read Yo y el mundo (Me and the World), a book by Spanish authors Mireia Trius and Joana Casals, together.
Yo y el mundo is an Information is Beautiful-like book —an assortment of fun infographics— specifically for children. Each spread is devoted to a topic, and the variety is astonishing. You'll find graphics about what people in different countries eat for breakfast, the cities with the worst traffic in the world (Miami, where we live, is the 10th,) in what months children are born more often, the most spoken languages in the world, where children have more or less homework, and many others. The book is even useful if you want your kids to understand how important it is to source graphics; all sources are credited in an appendix at the end.
So, recommended reading even if you don't speak Spanish. You can figure out the content of the graphics regardless. I know it because I gave a copy as a gift to a friend of mine —a native English speaker— and his daughter immediately seized it and spent hours poring over it.
It might help to think about these kinds of critiques as public conversations. These conversations can benefit the visualization creator by providing them with feedback and alternative ideas, and they can also help people who are viewing these visualizations. In any conversation, tone and form matter—how you say something can be as important as what you say. We should think of critique not as a simple take-down of someone’s work but instead as a way to build up someone’s work and an ongoing evolution of the field.
(Full disclosure: at the bottom of his article, Jon thanks me and my student Alyssa Fowers for providing some feedback; I suggested the lines above.)
My view: once you make a visualization public, you become part of a conversation that also involves reactions to your work. You can't expect anyone to ask for your permission —as some designers have suggested in social media,— before making comments about your work; these comments are also part of that conversation.
At the same time, critique ought to be constructive, prudent, respectful of the philosophical principle of charity —and itself open to critique. No opinion, no matter how well argued, is ever the last word on any matter, but part of an ongoing and endless dialogue intended to benefit everyone who designs or simply enjoys graphics. Critics and designers —roles we all assume in different circumstances— need to accept that knowledge doesn't reside in individual brains, but is distributed, and that we human beings aren't very good at reasoning on our own. Recommended readings about this: The Knowledge Illusion, The Enigma of Reason, and One Nation, Two Realities, which is the most important book I've read so far this year.
In the past, some famous critiques appearing in visualization books were nasty and destructive. I know from experience that snark can be very satisfying for the critic —there are people who seem to just want to collect scalps, and they still have substantial fanbases who mindlessly repeat their witticisms verbatim— but it's useless for everyone else. Our first impulse toward what we don't like is to be dismissive. Curb it.
If you've attended the latest version of my public talk(you can still invite me; I don't charge for this talk), you likely remember that I begin by explaining that I've become wary of certain myths and clichés in visualization and infographics such as “a picture is worth a thousand words”, “the numbers should speak for themselves” or “show don't tell”. The latter happens to be the title of a workshop that I've to co-taught at the Malofiej infographics conference. In the talk I often say that I prefer “show and tell” because before readers can see, they often need to be told how and what to see.
In a previous post I referred to the fact that visualization is based on a vocabulary of marks and symbols and a grammar that informs how we transform them to encode data and how we layer them. This gives visualization its flexibility and power. However, we need to acknowledge that sometimes it's not clear how to read a graphic, particularly if we haven't seen it before. If you use visualization in innovative ways and don't explain how to read it, your graphic may be ambiguous or hard to understand. Annotations matter.
Take this enormous and mesmerizing full-page visualizations published by The New York Times today:
Here are some detail photos:
I'm a big fan of Stuart Thompson's experiments in the NYT op-ed pages, and this project is so beautiful that it made me stop and read it —and I immediately got confused. At first, I assumed that line length was representing time from present. I then realized I was wrong: if that were the case, the isolated lines on the upper-left corner of each graphic shouldn't be that long, as they represent acquisitions in 2019.
It took me a while to realize that the feature that is encoding time isn't line length, but depth: lines here are roots of a tree; therefore, what is encoding time from is the distance from an imaginary horizontal ground line (where Google and Facebook are on each graphic) to the tip of each root.
I've been reading and making visualizations for decades; if it required so much effort on my part to grasp what I was seeing in these graphics, try to imagine the frustration that people with less experience may feel.
What about the horizontal sorting of the lines? The online version of the project includes a critical feature that is absent on print: a X-axis scale that reveals that horizontal position corresponds to the number of acquisitions per year:
There's another myth I've been fighting against for years: “readers must be able to understand any visualization in a few seconds.” This isn't true. It's also self-defeating, as it may lead us to create simplistic graphics, and to employ just common graphic forms such as bar or line graphs. Visualizations ought to be complex sometimes —just because the stories they tell are also complex— and, like good writing, they demand your time and attention to yield insights. Also, it's appropriate to experiment with novel graphic forms if only to expand our (and our readers') visual vocabulary.
However, we could make people's lives a bit easier by telling them what they're seeing (show AND tell!) This is just a suggestion: what if we added a small how-to-read-this-graphic sidebar somewhere?
I've always had mixed feelings about dot density maps because I find them ambiguous, and my guess is that they confuse many readers. Dots are often used in graphs, charts, and maps to accurately locate individual observations and phenomena, but that's not the case here. If you read a dot density map that way, it'll look like there were fatalities everywhere in Florida, and that lightning strikes become much less deadly as soon as you cross the border with Georgia or Alabama.
In a dot density map, though, each dot represents one observation, but dots aren't located where those observations were made; instead, dots are distributed to maximize coverage and, if the placement algorithm is well designed and manually tweaked, it'll avoid absurd placement —such as dots over lakes, rivers, or unpopulated regions.
What are the alternatives? I wouldn't recommend a choropleth map, as it's appropriate only when data is standardized —rates per 100,000 people for instance,— not when we visualize raw counts. Maybe a proportional symbol map would be the right solution. Or, if revealing geographic patterns isn't the goal of the visualization, a simple bar graph sorting from highest to lowest values would do.
(Another question about the map above is why states like Nevada, Idaho, or Nebraska are empty; that could be due to errors and inconsistencies in data gathering or to other glitches, such as the fact that lightning deaths are rare, and therefore a decade is a relatively short time period. This older map of deaths between 1959 and 2014 shows just 18 fatalities in Nevada.)
Visualization is based on a vocabulary and a grammarthat make it flexible and enable the endless creation of new graphic forms or novel and creative uses of existing ones; to see what I mean, see Maarten Lambrechts's Xenographics catalogue.
The graphic below, which appears in a story about how extreme rains have affected the Corn Belt, belongs to the latter group. It's a grid of time-series graphs revealing that the cumulative percentage of acres planted per state this year (black lines) is lower than previous years' (yellow lines); at the same time, it's also a cartogram, as graphs are positioned corresponding to their geographical location, and their size is proportional to the number of acres predicted to be planted in 2019. Tim Meko, one of the authors of the piece, calls this a “corntogram”:
We looked beyond who currently represents each congressional or legislative district and created our own Heat Index — a measure of whether each district generally favors Democrats or Republicans in statewide elections. Statewide elections are those from president down to the top courts in Texas — races decided by all Texas voters, and not just some of them.
Judging by the multiple visualizations, it seems that many districts are still “cold” —won by more than 25 percentage points, and therefore safe for whoever holds their seats,— but an increasing number are becoming warmer —won by margins lower than 10 percentage points. If you live in Texas, at the bottom of the story you can input your own address and find your district. I'd like to see something similar being done about Florida (paging Caitlin Ostroff.)
Robert Kosara writes about two recent papers about circular part-to-whole charts. I foresee there'll be a lot of discussion in the visualization world about this paragraph, which sounds sensible to me:
The visualization community may not like pie charts, but in the real world they’re hugely popular and very common. Rather than sneering at them (and the people who use them), why don’t we try to understand them better? In particular, the design space of part-to-whole charts is almost entirely unexplored. The only other chart that’s used for this purpose out in the world, the treemap, hasn’t been studied for this purpose much (if at all). And it seems to actually do worse than the pie chart (and the moon pie).
Two graphs from the papers (1, 2), representing errors when estimating percentages by slice or segment, are particularly striking; if I had to describe them I'd say that pie charts and moon charts (Robert's term) seem to be OK, but concentric bubble charts are terrible —this corroborates a hunch I've had for more than a decade,— and treemaps have their uses, but simple part-to-whole comparisons may not be one of them: