Idealism and pragmatism in visualization

Some reactions to the Washington Post chart on the right, designed by my former student Luís Melgar, reminded me of a passage from The Art of Insight that I shared the other day. This morning, a few readers of this chart asked in social media: “Why isn't time time on the X-axis?” implying that there's something wrong with that, as it breaks some convention or rule.

A better question might be: “Can you understand the chart anyway?” I bet you can. It may take a few more seconds than usual, but does that make a significant difference when you might gain something else? What makes a graphic good isn't whether it tries to approximate an ideal of truth, beauty, goodness, or excellence; rather, it's whether actual people can make sense of it, learn from it, or enjoy it.

A follow-up question could be: “Do you think that sometimes there are good reasons to flip the axes of a time-series chart?” There might be. In this case, I believe, the reason is rhetorical: to emphasize the left-right partisan divergence. We may agree or not with the choice of flipping the axes, but it's a choice that can be justified, discussed and eventually tested.

The following is the entire page from The Art of Insight where I talk about the difference between idealist and pragmatist discourses in visualization. I've shared this very early draft on Mastodon, so let me know what you think there, if you wish:

My conversation with Ed Hawkins made me think about the ways that visualization professionals and scholars talk about what we do, and why we do it. The first thought that came to my mind relates to a question I often get in classes and workshops:

What are the rules of data visualization?

I was born in Galicia, Spain, and we Galicians are well known for replying to questions with even more questions, so I often ask in return:

What are the rules of writing?

I find these questions nonsensical. Beyond a certain and flexible observance of the symbols and the grammar of the language we employ, there are really no universal rules for writing that are applicable to all kinds of writing regardless of purpose, context, or audience. Why should it be different for information design —which includes data visualization?

This misconception about the existence of rules in data visualization is in no small part due to the inheritance of what I call  “the Tuftean consensus,” after Edward Tufte, author of several very influential books, the best among them being The Visual Display of Quantitative Information (1983).

In The Infographic: A History of Data Graphics in News and Communications (2020), Murray Dick describes various discourses that have shaped how professionals and scholars talk and think about data visualization. The first is the functionalist-idealist discourse; Tufte is the most widely read exponent of it, although Murray also cites several other statisticians, mathematicians, and cartographers, such as Jaques Bertin, author of the foundational Semiology of Graphics (Sémiologie Graphique. Les diagrammes, les réseaux, les cartes, 1967.)

This discourse has been dominant in visualization for decades, although its influence is declining due to the rise of alternative ones. The functionalist-idealist discourse, Dick explains, conceives of visualizations “first and foremost as a scientific methodology” and as a form of “visual logic, and the rigorous application of a monosemic system that depends on a priori rules (present in standards, and in conventions, such as the use of grid lines, legends, labels, etc.) These provide a means by which signs may be used to connect propositions in a logical sequence.”

According to Dick, to someone who favors the functionalist-idealist discourse “graphics necessarily deal in complex, multivariate ideas and they must explain clearly and efficiently: “telling the truth about the data.” And then he adds an important line: “The notion that designer and audience may not share a common and irreducible understanding of what “the truth” means is not countenanced.” That’s why Tufte can proclaim in his books that “if your statistics as boring, you’ve got the wrong numbers,” or that “the only worse design than a pie chart is several of them.” 

Saying that pie charts are bad regardless of the context in which they are used makes sense only if you are an idealist, as idealists usually make up ideals of Truth, Good, and Beauty first—Tufte never uses the term “rules”, but he has defined a series of uppercase “Principles of Graphical Excellence,”— and then they proceed to judge any visualization according to those principles, and not to what their intended readers obtain from such visualization: insight, understanding, or even joy.

The key words in the previous paragraph are “making up”. Much of the advice that has been passed for rules or principles of visualization in the past few decades is, in reality, the preferences —sometimes based on empirical evidence, but too often merely idiosyncratic and even arbitrary— of a handful outspoken and authoritative figures, sometimes labelled as “thought leaders.

I respect the functionalist-idealist discourse, as I see its value, and I’ve learned a great deal from reading Tufte, Bertin, and many others in that tradition, but I prefer to talk about visualization in a different way. In The infographic, Murray Dick says that the discourse I employ is pragmatic; this book is no exception. He is correct; in fact, such label encapsulates the essence of my ideas.

Instead of a hierarchical, top-down professional landscape where “thought leaders” assemble lists of rules or principles and cast wisdom on the masses, I've came to instead favor a horizontal field driven by conversations among kind and collegial peers—a community of friends, in the ancient sense of friendships of utility and pleasure. I also believe that we should stop thinking about and teaching visualization in terms of rules, and more in terms of ad hoc justification and reasoning. 

This reasoning should be informed by aims, constraints, trade-offs, outcomes, and by an ever-imperfect but ever-evolving body of empirical evidence; or sometimes simply by personal experience and taste. We should also keep in mind that a key goal is to somehow benefit an audience —which could be just the designer of the visualization alone— in one way or another; “benefitting,” by the way, doesn't necessarily mean getting the more information as possible in as little time as possible.

In other words, my discourse isn’t just pragmatic. It’s also pluralistic and hedonistic.

 

The training wheel approach to teaching visualization

Every semester I teach my regular introduction to information design and data visualization class (syllabus here.) Most students are data scientists, statisticians, engineers, interaction designers, plus a few communication and journalism majors.

At the beginning of the semester, many students are wary about their lack of visual design and narrative skills, and they are often surprised at how fast they can improve if they are willing to engage in intense practice and constant feedback. I'm not exaggerating when writing “intense”: an anonymous former student perfectly described the experience of taking my class in RateMyProfessors: “SO. MUCH. WORK”.

Indeed. The only way to learn a craft is to practice the craft nonstop.

My classes consist of three parts:

First month: lectures, readings, discussions, and exercises to master concepts, reasoning, and software tools. I don't grade these exercises, I simply give credit for completion, but I hint what grades students would receive if I did grade them.

Second month: Project 1. I give students a general theme and a client. This semester I chose The Economist magazine's Graphic Detail section, so a requirement for the project was that students tried to mimic its style. Once a week during this second month I give each student individualized advice on their progress prior to the deadline. I don't give most feedback after they turn their project in, but before.

Third month: Project 2. I give students complete freedom to choose a topic and a style. I also provide weekly feedback, but it's briefer and more general than on Project 1.

I sometimes think that my classes are similar to how we, people from older generations, learned to ride a bike. You can certainly try to do it without training wheels; it's faster, but it might also lead to crashes. Or you can begin with two training wheels —month one, where I guide students by the hand,— then one wheel —month two, although I still give tons of feedback,— and then no training wheels —month three, where students are almost on their own.

Below you can see some examples of what students can accomplish in just a couple of months of hard work. None of these are perfect, but I'm happy with the results. 

Note: I'm scaling down my presence on Twitter. You can add this blog to your RSS reader (let's go back to the good old days!) or follow me on Mastodon. If you don't have a Mastodon account, here's a guide to get started.

Read some updates on my work here.

Graphic by Runyu Da

Graphic by Luís Ángeles

Graphic by James McKenney

Graphic by Livia Brodie

Graphic by Luisa Gómez

Graphic by Nia James

A few key paragraphs from 'The Art of Insight'

Here's the rough draft of a few key paragraphs from The Art of Insight:

I respect but also disagree with the Tuftean and Bertinian functionalist-realist discourse in visualization. In The infographic, Murray Dick says that the discourse I employ in all my writings—this book isn’t an exception— is pragmatist. He is correct; in fact, such label encapsulates the essence of my ideas beautifully.

Instead of a hierarchical, top-down professional landscape where self-proclaimed “thought leaders” cast their wisdom on the masses, I favor a horizontal world driven by conversations among kind and collegial peers—friends in the ancient philosopher’s sense of such term. I also believe that we must stop thinking about and teaching visualization in terms of “rules”, and more in terms of justification and reasoning.

This reasoning is driven by goals, constraints, trade-offs, and outcomes, informed by ever-imperfect and ever-evolving empirical evidence, conventions, and even personal experience and taste, and aimed at benefitting an intended audience.

I call this the hedonistic shift.

 If you want to read a bit more, I've also shared the draft of the first page of the book exclusively on Mastodon.

Bloomberg visualizes the shrinking Mississippi river

Chloe Whiteaker shares the latest project by Bloomberg Visual Data, which explains what's happening to the Mississippi river:

“The Mississippi River — the immense, quiet highway that courses down the middle of America, moving critical food, wood, coal and steel supplies to global markets — is shrinking from drought, forcing traffic to a crawl at the worst possible time.”

The story contains several intricate visualizations, such as this “arterial sankey” —that's the term Chloe used— diagram. Three of these visualizations resemble rivers, which I guess is appropriate for the theme.

The most striking graphics in the story to me are also the simplest and most straightforward: a line graph of the increasing cost of shipping grain down the river, and a map of drought in the United States that also locates the Mississippi basin. Take a look at them.

UPDATE: Justin McCarty says that the main graphic in the story reminds him of an amazing 1960 visualization.

Note: I'm scaling down my presence on Twitter. You can add this blog to your RSS reader (let's go back to the good old days!) or follow me on Mastodon. If you don't have a Mastodon account, here's a guide to get started.

Read some updates on my work here.

Who's the audience for our graphics? Not the people who need them the most

Disinformation in the United States is an asymmetrical phenomenon: prevalent and central to the political right, and peripheral elsewhere. Liberal and left-wing disinformation exists, but it's not as dominant, virulent, or violent.

It you are a conservative, the previous paragraph might make you cringe and stop reading; you might feel prompted to call me a left-wing partisan and ignore anything else I have to say—even if I'm hardly on “the left” on several matters.

You'll also ignore the multiple studies and books that warn against this phenomenon, calling them “biased”. This includes my own How Charts Lie.

That's the problem with stories such as this investigation by The New York Times. It describes how conspiracy theories about the attack on Paul Pelosi spread on the right-wing alternative reality, fueled by politicians, online influencers, media personalities, and even the thin-skinned new owner of Twitter.

Who's the audience for this type of investigative reporting? Who will read it and explore its beautiful graphics, such as the long beeswarm plot that reveals the ebb and flow of conspiratorial narratives?

I bet it won't be the audiences who need to read it the most. They'll dismiss it before even taking a look at it—precisely because it was published by The New York Times.

Instead, the audience for stories like this is me —and most of you, I guess. But we aren't the ones who need to be told that it's scary that half of the U.S. population is being fed a systematic diet of ignorance, fear, and hatred. We know that already. Just yesterday I saw the famous podcaster Joe Rogan offering his massive platform to a white supremacist who is also one of the main superspreaders of disinformation against LGBTQ people in this country. Shame on Rogan; he ought to know better.

None of this is a reason to stop doing research, writing, denouncing, and visualizing relevant subjects such as disinformation. But it is a reason to think about how to reach seemingly unreachable or unpersuadable audiences. Maybe through education, new platforms, and new voices, but I'm hardly optimistic.

Note: I'm scaling down my presence on Twitter. You can add this blog to your RSS reader (let's go back to the good old days!) or follow me on Mastodon. If you don't have a Mastodon account, here's a guide to get started.

Read some updates on my work here.

Beauty as experience

There's a lot that I consider beautiful in The Art of Insight. Not the writing, which is casual, almost pedestrian, as in all my previous books, but the people I talked with and the work I'm showcasing.

My current understanding of beauty isn't classical, Platonic or Aristotelian —uppercase Beauty as a property of things,— but down-to-earth, pragmatic, relational, pluralistic —lowercase beauty as individual experience. I've read books that challenge such assumption. Among the best is Chloé Cooper Jones's Easy Beauty, a breathtaking memoir that I strongly recommend. Get it. You can thank me later.

Here's a dialogue:

“Isn’t beauty in the eye of the beholder?”

“I don’t think anyone who says this knows what it means.”

“No?”

“Or rather, it has a meaning no one believes. It’s a silencing sentence, one that reduces rather than explores one of the most exhilarating human experiences. The experience of beauty. What a shame”

And a reflection:

“[There's a] difference between defining beauty and defining what beauty does in the body. The latter question belongs to the realm of aesthetics, the study of bodies in proximity to beauty.”

(To me there isn't such a difference.)

Note: I'm scaling down my presence on Twitter. You can add this blog to your RSS reader (let's go back to the good old days!) or follow me on Mastodon. If you don't have a Mastodon account, here's a guide to get started.

Read some updates on my work here.

Back to blogging—with some updates

Hi everyone, it's been a while.

I'm back to blogging. How often? I don't know. We'll see how things go.

A few updates:

BOOK WRITING

I was supposed to finish writing The Art of Insight more than a year ago. My editors at Wiley have been extremely patient while I dealt with some serious personal matters.

In my nearly defunct Twitter feed I recently joked that The Art of Insight is my The Winds of Winter: it's not just that it's delayed, or that it's unclear when it'll be finished; it's also that the more I write, the more I think it'll defy expectations. I like that. To give you an idea, a key section is titled 'Data Hedonism'.

The good news is that I've conducted all interviews I had planned —more than twenty,— they are transcribed already, and I'm writing at a fast pace.

BOOK EDITING

I feel that the The Art of Insight will be my last book dealing exclusively with visualization. I'll probably write more books in years to come, but they'll be about adjacent subjects. I want to write about probability and uncertainty, about notions of normality and normativity, and about amalgamation paradoxes and ecological fallacies —yes, I believe that there's an entire book waiting to be written about those.

Instead of writing about visualization myself, I'll keep encouraging other much more interesting voices to do so, coaching them, and even editing their work if they decide to publish it in CRC's AK Peters Visualization Series. I co-edit that series with Tamara Munzner.

If you have an idea for a book, let us know. We're here to help. You'll be in excellent company, sharing shelves with the likes of Neil Richards, Jen Christiansen, Nigel Holmes, Bongshin Lee et al., Samuel Huron et al., and many others.

THE PODCAST

Simon and I are releasing an episode of The Data Journalism Podcast roughly every three weeks. It's fun.

TALKS, CONFERENCES, AND WORKSHOPS

I've decided to stop traveling for work as much as I used to. If you want me to present at your conference or workshop, by all means contact me; just be aware that I'm being extremely selective with what I accept. If I can't make it, I promise I'll recommend someone better than me for you to invite.

I'm also working on bringing back some my own conferences, such as the Digital Humanities+Data Journalism Symposium. For those of you who miss the Malofiej Infographics conference, there might be news about that soon.

OPINION PIECES

In recent weeks I've published a couple of opinion pieces, one at The Washington Post, another at The Bulwark. I'll be blunt: if nothing changes, the United States will eventually become an illiberal, quasi theocratic country, an American version of Viktor Orbán's kakistocratic Hungary. Some cheer at such prospect; I loathe it, perhaps because I've read too much about late 19th and early 20th century history and political thought.

Writing these articles is emotionally draining, and I can only do it in the spare time left after I take care of my kids, classes, some consulting, and book-writing. However, I also think that it's the best way I can contribute to the fight against the ethical and ideological rot that is metastasizing in this country.

GAMES

Once The Art of Insight is finished, my next project will likely be a tabletop game that I began developing two years ago.

See you soon.

Announcing the Data Journalism Podcast

Simon Rogers and I have been working together for years, collaborating with data designers from all over the world in a long series of visualizations (see here and here;) we created that initiative because we share an interest in both data and in journalism.

It was only natural that this interest would eventually lead to the Data Journalism Podcast, which we've just launched. The teaser for the first episode is already available, and the podcast will be soon downloadable through Spotify, iTunes, Google Play, and other platforms.

As I say in this first program, this is an informal experiment we'll do every now and then in our spare time as an excuse to chat with people whom we admire. We're both amateur radio hosts, so the podcast will also be a learning experience for both of us.

In other words, forgive the glitches in this first episode. I hope you'll enjoy it!

Talking about data visualization in Al Jazeera

Al Jazeera's Tariq Nafi interviewed Mona Chalabi (who appears making one of her paintings!), myself, and some other people about how data visualization affects our perception of reality. Watch it below; the segment begins at around 14':

Data Citizens, a new lecture series

I think I should apologize for not updating this blog more often. The semester has been extremely challenging and, on top of that, I'm at last working on my next visualization book—more news about that soon. I also keep drawing while-in-Zoom-meeting sketches, which are part of a semi-secret long-term side project not related to visualization—although it contains plenty of charts, timelines, maps, and illustrations.

Anyway, the topic of this post: the University of Miami's Institute for Data Science and Computing, where I'm director of visualization, data communication, and information design, has just launched a new free distinguished lecture series with the title Data Citizens, which I co-organize. As some of my previous conferences (Data Intersections, for instance,) its goal is to be a multidisciplinary gathering that invites people from many different realms and disciplines.

The next virtual lecture is on Thursday, December 10th , and it's by Deborah Stone, author of the recent book Counting: How We Use Numbers to Decide What Matters; I read it when it was just a draft and mentioned it in an earlier post. Deborah's talk is titled 'There’s No Such Thing as a Raw Number' and it's open to the public. See more details here and register for free.

'How Charts Lie': a few clarifications and edits

(Update 01/08/2020: you can now DOWNLOAD most figures from the book in high resolution and in two different color schemes.)


CLARIFICATIONS FOR ALL EDITIONS

Page 44: where it says “people became richer or poorer” or “people in those countries contaminated more or less,” I think I'd add a qualifier such as “on average” just in case, as these are per capita numbers.

Page 73: the Richter scale is based on tenfold increments of wave amplitude. That's what I mean by “stronger”; in other words: it's not the possible energy released, which increases at higher rates.

Page 74: in the fictional example about gerbil population growth, I assumed that the parents die shortly after giving birth; otherwise, by the second generation we wouldn't have double the gerbils (8 children) but triple, a total of 12: 8 children and their 4 parents.

In general: whenever you see Kaplan-Meier charts in the book, assume that lines have been smoothed; actual Kaplan-Meier estimators create lines that look like staircases.

Page 129: where it reads “ex-Soviet” countries it should be “ex-communist” countries, which is more accurate.

EDITS FOR THE FIRST PRINT EDITION (hard cover)

If you read the first print edition of How Charts Lie you may notice a few printing and layout errors. These should have been corrected in the e-book already, and also for the paperback version, to be released in October of 2020. If you detect anything that looks strange other than these, please let me know.

On page 24 the transparency effects that should emphasize or hide parts of the charts disappeared between the galleys—where the graphic was perfect—and the final printing. Mysteries. Here's what that graphic should look like:

On page 45 the line corresponding to the United States didn't show when printed. Here's the graphic:

There's a minor issue with the gradient on the second bar of the chart on page 142: it doesn't fade to white. It should look like this:

A chart on page 116 is slightly misplaced.

On page 49, the second paragraph should read: “Imagine that a district's circle sits on the +20 line above the baseline. This means that Republicans lost 10 percentage points, which went to Democrats, for a total of +20 percentage point change in their favor (there weren't third-party candidates, I guess.)”

On page 92 there's a needless “is” in a sentence that should read “Unless a crime is premeditated...”

On page 104 there's an “s” missing at the end of “assess” (this one made me giggle.)

At the bottom of page 128 there's an “as” missing before “Assange”.

On page 157 there's a tiny label that should read MS instead of MI.

The last label on the Y-scale of the chart on page 172 should read “600” instead of “490”.

U.S. version of the 'What if all COVID-19 victims where your neighbors?' project

The Washington Post has just published the U.S. version of the project I mentioned in the previous post: what if all victims of COVID-19 lived around you? Working with The Washington Post graphics folks was an honor and a pleasure. Here are all people involved (I copied this from the project page itself):

This is the U.S. version of a project originally created in Brazil, as a partnership by Agência Lupa and Google News Initiative.

Art direction by Alberto Cairo. Data and storytelling by Rodrigo Menegat. Design by Vinicius Sueiro and Vallery Nascimento. Development by Tiago Maranhão and Vinicius Sueiro. Distribution strategy by Gilberto Scofield Jr. Editing by Natália Leal. Google News Initiative: Simon Rogers and Marco Túlio Pires.

Additional editing by Ann Gerhart. Copy editing by Anne Kenderdine. Additional data support by Dan Keating. Additional design and development by Lucio Villa, Matt Callahan, Simon Glenn-Gregg and Armand Emamdjomeh.

New project: What if all COVID-19 victims were your neighbors?

The latest project I've art-directed for the Google News Initiative is titled No Epicentro (“At the Epicenter.”) It asks: what if all confirmed COVID-19 victims in Brazil were your neighbors?

No Epicentro has just been published by our media partner, Agência Lupa, and was developed by data journalists Tiago Maranhão, Rodrigo Menegat, and Vinicius Sueiro, with advice from Marco Túlio Pires.

No Epicentro is available just in Portuguese for now—there'll be an English version soon,—but you can run it through an automated translator, and it's very easy to understand anyway (Update August 3: the project now has an English version.). Learn more about the project in this article and in the making-of; the data and code are free to download.

Our goal was to make data feel a bit more personal: 93,000 people, the number of confirmed COVID-19 deaths in Brazil at the moment of this writing, is a cold figure, something that makes the scope of the tragedy hard to grasp—please read Numbers and Nerves to learn about “statistical numbing”. But what if we force you, the reader, to imagine those 93,000 as people you know, see every day, or interact with?

Begin by typing any address in Brazil. This was mine when I lived in São Paulo:

No Epicentro then reveals more than 93,000 white dots—again, the number of COVID-19 deaths at the moment—inside a circle with your address as the center. The visualization uses census track data, so every white dot represents a person living around you; therefore, if you are in a densely populated area, the circle will be small, and if you are in the countryside, it'll be larger:

At any point you can generate a customized infographic with the map of the region where you live to share in social media:

Next, the visualization compares the number of COVID-19 deaths to the population of a city that is near you. In my case, it's Rio Grande da Serra. If all deaths had happened there, this city and some of its surrounding areas would have been wiped out:

No Epicentro ends with other comparisons, and it shows where COVID-19 deaths have actually been registered in Brazil:

Visualizing police killings in Kenya

Missing Voices is a collaboration between numerous NGOs that tracks police killings, enforced disappearances, and extrajudicial executions in Kenya.

The visualization firm OdipoDev, founded by designers Odanga Madung and Samer Costello, has created a data-driven story that displays their main figures (methodology page here). Missing Voices also contains a gallery of obituaries of every individual who's died at the hands of the police.

This is important work.

U.S COVID-19 tracker by ProPublica

The big U.S. newspapers get most of the attention when it comes to news visualization, but other players are producing excellent work, as well. ProPublica, a nonprofit investigative journalism organization I donate to every year, has a very good tracker of COVID-19 cases in the U.S, designed by by Lena Groeger and Ash Ngu. The geographically arranged animated arrows on top are lovely.

Psychopathic charts, lines that should be bars, and picking cherries

I was hoping to withdraw from the world during the Summer and devote time to activities that require deep concentration—reading, writing, designing, and also this,—but it seems that the run-up to the November election will bring a deluge of bad charts. I should have known better; How Charts Lie may need a sequel soon.

The following are just from today:

This is a truly psychopathic chart (UPDATE: Fox News has apologized):

Next, here's one of the best examples of a grossly misleading chart that, at the same time, isn't technically incorrect (this is the author):

The chart only looks like a V-shape because the data is encoded as a line, when a bar graph would've been more appropriate. The impression it creates is entirely different (source):

Finally, the President, always a reliable source of examples of convenient data cherry-picking, entertains us with this beauty (and it turns out that there is a huge glitch in the data):

Jon Schwabish has some things to say about it:

About that weird Georgia chart

Visualization social media has been busy mocking the following chart by the Georgia Department of Public Health. Pay attention to its horizontal axis:

I never attribute malice when sloppiness is a more parsimonious explanation. I guess that whoever designed this chart thought that sorting the bar groups from highest to lowest, instead of chronologically, was a good idea.

This is not wrong per se; it's possible to think of situations when it's useful to arrange your data like this during analysis. As it always happens in visualization, design choices depend on purpose.

In this case, though, the purpose is to show “the most impacted counties over the past 15 days and the number of cases over time,” so separating the counties and then sorting their bars chronologically seems to make more sense. Something like this:

Visualization books, including mine, spend many pages discussing how to choose encodings to match the intended purpose of every graphic, but we pay too little attention to the nuances of sorting: should we do it alphabetically, by geographic unit, by time, from highest to lowest, from lowest to highest—or do we need an ad-hoc criterion? Or should we make the graphic interactive and let people choose? As always, the answer will depend on what we want the reader to get from the visualization.

The problem with inconsistent and unlabeled scales

One of the strategies to come up with novel ways to display data is to combine existing graphic forms. This morning The New York Times published a story titled 'Most States That Are Reopening Fail to Meet White House Guidelines' that contains a series of square equal area cartograms that are, at the same time, trellis charts. The piece is really nice.

There's something about it that worries me a bit, though: charts don't have scales. Removing scales from graphics seems to be getting more popular lately among data journalists, and it works in some cases here: some of these charts have horizontal reference lines—see animation on the right—that help you get a sense of proportion and variation.

But the following set of line charts lacks any reference and, moreover, it seems that each one is based on a different scale: New York has more daily confirmed cases than Florida—thousands versus hundreds—but the last point on Florida's line is higher than New York's. New Hampshire has a 7-day average of around 100 cases; Maine has a bit more than 20. I understand that the goal of these graphics is to reveal upward and downward trends, not the case count itself, but I fear that this design choice may mislead some readers:

Here are those charts with scales:

What could be an alternative here, I wonder? It's tricky. There might not be an ideal solution, as it often happens in visualization; adding detailed labels would clutter these tiny charts. Perhaps not to show daily new confirmed cases, but some sort of index—percentage change based on a common starting point for all states,—or the variation in comparison to the previous day or week?

The Dawn of a Philosophy of Visualization

Cover illustration by Prisca Schmarsow of Eyedea Studio

My new article for Nightingale, the online magazine of the Data Visualization Society, has just been released. It's titled The Dawn of a Philosophy of Visualization, and it adapts the foreword that I wrote for a new book, Data Visualization in Society, published by Amsterdam University Press.

The book will be presented in a public e-event on Wednesday. Sign up for it here. To read the book, follow this link (it's open access.)

Latest project: Search waves during a pandemic

The latest project I've art-directed has just been launched. It's titled Searching COVID-19 and it was produced by Schema Design in collaboration with the Google News Initiative and Axios, which has published its own version.

The visualization consists of a dynamic beeswarm plot in which each bubble represents a query reaching the top searches in a U.S. state (states may appear more than once in the plot.) Bubbles ungroup little by little to reveal the patterns mentioned in the project's opening copy; for instance, in the early stages of a pandemic people often look for information about the disease itself—“what is” searches,—and later we clearly see an increase in searches for how to prepare for the disease or for its consequences—“how to” searches.

You can find many previous projects here, and this is a 2017 article about the collaboration.