Friday, June 21, 2019

Sources, methodology, and limitations in visualization

Luís Melgar, a University of Miami news visualization graduate, is currently working at Guns & America (Twitter), an investigative reporting collaboration between ten newsrooms all over the U.S focusing on gun culture and its consequences. Luís, in collaboration with reporter Alana Wise, has just published a story that quantifies gun shots near Washington DC schools.

There are some nice graphs and maps in the piece —I copied a couple below,— but what I'd like to call your attention to is the section at the bottom, which links to sources, discusses the methodology they employed in a lot of detail, and discloses limitations.

If, as Nate Silver has suggested in articles and recent tweets, journalism is to become a bit more empirical, increasing transparency and accountability by adding methodology sections to stories, rather than relegating them to sidebars or external pages that few readers visit, is a good first step. I know, I know, other publications —FiveThirtyEight itself, ProPublica, and the like— are already doing it, but I wish it'll become standard practice in all news media.

Thursday, June 20, 2019

Visualization style guides

I'm a fan of visualization and infographics style guides. In the past decade I've put together a small collection that includes gems such as old ones from the Dallas Morning News and the Los Angeles Times, and more recent ones, such as the Urban Institute's. Urban's Jon Schwabish as a pretty comprehensive collection, as well.

The most recent addition to my collection —thanks to Rafa Höhr for the tip— is the graphics style guide from The London Datastore, the data portal from the city of London. Written by Mike Brondbjerg, the guide (PDF) provides sound advice on typography, color, and composition. As other documents of its kind, it's a good starting point if you're a beginner in data visualization and you're still unsure about style choices. Also, don't miss the Further Reading section at the bottom of the guide's website.

Friday, June 14, 2019

A Venn diagram matrix by FiveThirtyEight

Jan Willem Tulp has called the latest xenographic by FiveThirtyEight a “Venn diagram matrix.” I wish that'd stick as a name for this type of chart (read about the term “xenographic” here):

The chart displays 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.”

Thursday, June 13, 2019

Spatial thinking, abstract thinking, and visualization

There are many kinds of nonfiction books. Some feel like walking through a historical building following a fixed path with the help of a 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 more).

Other nonfiction books are playful and meandering. They seem not to be structured like linear narratives with a beginning and an end. They feel 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 in the future. Maybe we should 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 rouse.

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.

Wednesday, June 12, 2019

The future of visualization lies beyond visualization

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 them myself, they are areas 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:

Tuesday, June 11, 2019

New book and new public lecture

My new book, How Charts Lie: Getting Smarter About Visual Information, is already available for pre-order through W.W. Nortonmy new publisher, Barnes&NobleAmazon, Amazon UKIndieBound (independent bookstores), and will soon appear in other retailers. Publication date is October 15.

Pre-orders matter A LOT for the success of a book, so if you like the work that I've been doing in the past years —free toolstutorials, online courses, or my previous books,— I'd like to ask for your support.

How Charts Lie is my first book for the general public, an explanation of how anyone, regardless of education or professional background, can become a more informed reader of graphs, maps, diagrams, and infographics.

For those who have asked: no, it's not the follow-up to The Truthful Art. As I mentioned in the Epilogue of The Truthful Art, the third volume in the Art series will probably be titled The Insightful Art, but it'll need to wait for at least another year or two, if not more.

Instead, How Charts Lie is a standalone book that, if you work with data and visualization, you can give as a gift to that friend or relative who doesn't understand what you do. I hope that it'll help the public to approach numbers and their visual representations more critically, but also with more interest, appreciation, and care.

That's why I once toyed with a different subtitle —and How They Make Us Smarter, because good charts that are correctly read may have that effect. Despite its title, the tone of the book is positive: it's not that charts lie per se, but that we tend to lie to ourselves with them, even when they are well designed. But we can learn.

Some other authors have already read How Charts Lie and provided early blurbs:

“What can I say? I'm a sucker for statistics explained in funny, engaging, and mathematically correct ways, especially when every now and then a line like "charts lie to us because we are prone to lying to ourselves" is thrown in with good humor. A must read for anyone who wants to stay informed.”
Cathy O'Neil, author of Weapons of Math Destruction

“I wish we lived in a world where you didn’t need to read Alberto Cairo’s How Charts Lie, a robust guide to self-defense against graphs and figures designed to mislead.  But here we are, and yes, you do." Jordan Ellenberg, author of How Not to Be Wrong: The Power of Mathematical Thinking

"This book offers a succinct, elegant, accessible look at the ways data can be represented or misrepresented and is a perfect primer for anyone who cares about the difference." Charles Wheelan, author of Naked Statistics: Stripping the Dread from the Data

 “Alberto Cairo has written a wise, witty and utterly beautiful book. You couldn't hope for a better teacher to improve your graphical literacy.” Tim Harford, author of The Undercover Economist and presenter of More or Less in the BBC

“This book will open your eyes to how everyone uses visuals to push agendas. A master visual designer, Alberto Cairo shows you how to read charts and decode design. After this book, you can’t look at charts with a straight face!.” Kaiser Fung, author of Numbersense: How to Use Big Data to Your Advantage

Alberto Cairo shares great examples of data visualization and storytelling for anyone who wants to dig into their data.” Dona Wong, author of The Wall Street Journal Guide to Information Graphics

“A picture may be worth a thousand words, but only if you know how to read it. In this book, Alberto Cairo teaches us how to get smarter about visual information by reading charts with attention and care. I found a lot to steal here, and you will, too.” Austin Kleon, author of Steal Like An Artist


I'll continue delivering my public lecture wherever I'm invited. The only major change besides its content —which will be closer to the new book— is the title: instead of Visual Trumpery it'll be How Charts Lie. Requirements remain the same:

• Send me an e-mail so we can chat about location and dates: alberto DOT cairo AT gmail DOT com.
• I won't take salary for the public talk.
• I only need you to cover a flight (economy is fine), hotel (I'm an easy guest), and minor expenses such as taxis and meals.
• Attendance to the talk should be free and open to anybody.

I'll announce these talks in my calendar, which I haven't updated in a while. I'll also post them in the upcoming website of the new book,

Me and the World, a book to explain visualization to children

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 drawn 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.

Monday, June 10, 2019

Thoughts and readings about visualization critique

Jon Schwabish has a good post about data visualization critique. This is a relevant paragraph:
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.)

Jon links to Fernanda Viégas's and Martin Wattenberg's already classic article, and to a more recent one —a writeup of a talk— by Elijah Meeks. I also recommend Alli Torban's post from a month ago.

My view: critique is vital. 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 proposed in social media,— before making comments about your graphics. 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.

We've all seen destructive and nasty critiques in the past. I know firsthand that snark can be very satisfying for the critic —there are people who seem to just want to collect scalps— but it's useless to everyone else. Our first impulse toward what we don't like is to be dismissive. Curb it.

Sunday, June 9, 2019

Show AND tell

If you've attended the latest version of my public talk (you can still invite me; it's free), you likely remember that I mention 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 public 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 type of 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 these 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 encodes time is the distance from an imaginary horizontal ground line —where Google and Facebook are on each graphic— to the tip of each root.

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:

I've been reading and making visualizations for decades; if it required so much effort on my part to grasp what these graphics show, try to imagine the frustration that people with less experience may feel.

Important aside: 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 design simplistic graphics, and to employ just common 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?

Friday, June 7, 2019

The ambiguity of dot density maps

Steve MacLaughlin sent me the following dot density map of lightning fatalities per state; it appears in the Wikipedia page about lightning strikes.

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 can be due to errors and inconsistencies in data gathering and access, 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.)

Update: here are two other maps, one adjusted by state population.

Thursday, June 6, 2019

The language of visualization is ever-expanding

Visualization is based on a vocabulary and a grammar that 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 Beltbelongs 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”:

The cartogram is paired with several other visualizations, including an actual map of the region:

Wednesday, June 5, 2019

Visualizing purpling Texas

The Texas Tribune has just published a nice scrollytelling data story about how electorally competitive Texas is really becoming. Here's what they did:
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.)

Tuesday, June 4, 2019

Concentric bubble charts are terrible; pie charts and moon charts may be OK

Robert Kosara writes about two recent papers on 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).
(Although I'd point out that stacked bar graphs are also used for that purpose.)

Two graphs from the papers (12), 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 pie charts (Robert's term, although I propose 'eclipse charts') 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:

Monday, June 3, 2019

Visualizing uncertainty

The theme of this year's VizUM, the University of Miami's visualization symposium, is the role of uncertainty in visualization. The date is Tuesday, November 12, and registration is free. I'll present about How Charts Lie and the comprehension gap between designer and reader:
Scientists, statisticians, designers, and journalists are often taught that “a picture is worth a thousand words”, that we should “show, don’t tell”, and that charts are “intuitive” and useful to “simplify” information. This talk explains why these myths, if taken at face value, are wrong and dangerous, and what we can do to help the public understand charts, graphs, maps, and infographics better. Contrary to what we commonly hear, there is always an uncertain gap between what we intend to communicate and what our readers end up seeing; bridging that gap should become a priority to any visualization designer.

Our star speaker, Northwestern University's Jessica Hullman, will talk about “Supporting Reasoning with Uncertainty Using Data Visualization”:
Charts, graphs, and other information visualizations amplify cognition by enabling users to visually perceive trends and differences in quantitative data. While guidelines dictate how to choose visual encodings and metaphors to support accurate perception, it is less obvious how to design visualizations that encourage rational decisions and inference. Jessica will motivate several challenges that must be overcome to support effective reasoning with visualizations. 
First, people’s intuitions about uncertainty often conflict with statistical definitions. Jessica will describe how visualization techniques for conveying uncertainty through discrete samples can improve non-experts’ ability to understand and make decisions from distributional information. 
Second, people often bring prior beliefs and expectations about data-driven phenomena to their interactions with data (e.g., I suspect support for candidate A is higher than reported), which influence their interpretations. Most design and evaluation techniques do not account for these influences. Jessica will describe what’s been learned by developing visualization interfaces that encourage users to reflect on their expectations and use them to predict and improve belief updating.

Join us in November, when Miami is sunny and warm (as usual).

Friday, May 31, 2019

In visualization, what you count and how you count it matter

A whole chapter in How Charts Lie is devoted to explaining that whenever we see a visualization, we should try to assess its source and ponder what is being counted and how. See this series of charts by Our World in Data, comparing causes of death in the U.S. with Google searches and with what “the media” reports on:

The scare quotes above are deserved. It's dubious, as Dan Nguyen points out in this thread, that The New York Times or The Guardian are representative of “the media” at large. “The media” is a fuzzy umbrella term that encompasses anything from national and local TV to online news startups —and, yes, also quality national newspapers. The two stacked bars on the right may look very different —even more skewed, perhaps— if we really surveyed the media.

On a side note, I agree with the authors of the report that journalism may bias our perceptions of the most common causes of death by overemphasizing unusual events, but I doubt there's an easy solution to that problem —or even that it is a problem at all. News media's preference for novelty and tragedy isn't really just news media's, but human nature's, and they reinforce each other in and endless loop.

Besides the real and intrinsic newsworthiness of unusual and tragic events —more about that in this article by Nieman and in the tweet below,— think about your conversations with friends and family: if you can choose between talking about the many people who die of heart disease or cancer every day, or the dozens who died in a recent terrorist attack, accident, or natural catastrophe, what do you think the result would be? And what would you read, listen to, or watch first, a piece about the former —the quotidian— or about the latter —the unique and dramatic?

Again, don't miss Dan's critique, particularly one of his latest tweets, darkly sarcastic but very on point:

Wednesday, May 29, 2019

The “me” layer in visualization

Since The Truthful Art was published I've been asking people attending my talks and workshops to add more “me” features to visualizations. Following Hadley Wickham's “A Layered Grammar of Graphics”, we could envision visualizations as consisting of stacking several layers: a scaffolding layer (axes, legends, and so forth,) an encoding layer (the actual representation of the data,) an annotation layer (explainers, footnotes, and the like,) etc. The “me” layer would be features that let readers see themselves in the data, or that let them manipulate the data to create scenarios that speak to them.

For instance, if you're visualizing the income of a country, you can simply show a histogram or density plot. But if before that you ask readers to input their own income, you can show them where they are in the distribution and in comparison to everyone else. That adds interest and likely increases engagement.

This is what WSJ's Yaryna Serkez and Theo Francis did in this beeswarm plot published more than a month ago. Write down your income and see how you compare to the typical employee of 1,000 companies in different sectors. Kaiser Fung has a nice post about this project.

Tuesday, May 28, 2019

Migration flows in the European Union

Roxana Torre (portfolio) maps migration flows within countries that belong to the European Union. Data comes from the European Commission's Annual Report on Intra-EU Labour Mobility. Spend some time exploring it.

This kind of flow chart is hard to get right, but I think that Torre's thoughtful decision of combining colors and subtle looping animations succeeds at making the graphic clear and enjoyable on a computer or tablet screen —mobile is a different story; you need to zoom in to see what's going on or select countries, and the hover function isn't available.

To overcome this last challenge I wonder whether it'd be good to add a complementary table showing, say, the top 3-5 destinations and the 3-5 origins of migrants when selecting a country. Something like the graphic below (the table would be displayed underneath the map on the mobile version).

(Update: Anita Graser suggests that total counts may not be the entire story. I agree; adding relative values —migration rates per 1,000 people or %— to the table is worth considering. They can be seen already in each country's tooltip.)

Monday, May 27, 2019

Reporting or no-platforming? Some thoughts on that controversial NYT graphic

On Sunday Karen Yourish and Rebecca Lai from The New York Times published a thorough analysis of all the insults Donald Trump has “hurled at 2020 Democrats”. Many reactions on social media have been rather negative, ranging from the tepid to the angry, with one of the hosts of Pod Save America calling the article “deeply stupid”.

The most reasonable critical responses wondered why it is necessary to give more visibility to the musings of someone who has repeatedly proved himself to be a divisive and infantile vulgarian.

I'm torn on this one. On one hand, I'm sympathetic to voices who explain that depriving the worst extremists of platforms is an effective strategy to improve the public sphere. Just read what happened to this nutcase and this grifter. Journalists need to think about their responsibility when giving voice to certain characters. On the other hand, silencing can easily go too far. If you cast a wide net and don't consider every case on its merits, you'll catch legitimate voices on the hard left and the hard right that deserve to be heard.

Moreover, we aren't talking about any attention-seeking extremist in this case, but about someone who happens to occupy the highest office in the United States. His words matter, they reveal his character, and journalists have an obligation to report them —albeit critically, as journalism shouldn't be stenography. I think that Karen and Rebecca did a good job at adding some value. Their main graphic —which resembles a bar graph— suggests who Trump is more worried about, and the timeline may reveal some intriguing patterns; for instance, I'd like to see someone taking these data and comparing insult days and times to appearances on cable TV, particularly Fox News.

Monday, May 20, 2019

FiveThirtyEight plots urban political segregation

FiveThirtyEight's Rachael Dottle has just launched a visualization-driven story that reveals the most politically segregated cities in the United States. Much has been written about the increasing rural-urban divide, but not enough attention has been given to more localized divisions within those areas.

The entire project is worth your time, and a good example of how to integrate words and visuals. I particularly liked the small multiple maps, and the scatter plot comparing political and racial segregation.

Monday, April 29, 2019

Charts that lie

A few days ago skeptical environmentalist Patrick Moore published a chart of temperature change since 1880 on Twitter that ruffled some feathers. Eric William Lin and others asked me to chime in. Here's one interchange:

That's right. There's an entire section about this same type of chart in my upcoming How Charts Lie. It's in a chapter about how to reason about the X- and Y-axis in graphs, and about scales and legends in general.

Here are the pages mentioned in the tweet, in case you want to learn why that chart is bad —long story short: a variation of a few degrees Fahrenheit may sound tiny, but in reality it's very significant, so it ought to be emphasized. Moore's scale isn't “realistic”, quite the contrary: it doesn't make any sense. These pages come from the latest draft of How Charts Lie, which is still being copy-edited, so forgive typos and clunky grammar: