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Episode #157: Alberto Cairo
177 Plays5 years ago
Welcome back to the PolicyViz Podcast! I hope you had a great summer and got some time to rest and relax. I’m really excited for this season of the show–I’ve got some great guests lined up to talk about their...
The post Episode #157: Alberto Cairo appeared first on PolicyViz.
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Transcript
Introduction & Show Support
00:00:11
Speaker
Hi, everyone. Welcome back to the Policy Viz Podcast. I'm your host, John Schwabisch. I hope you had a great summer and I hope you had a chance to rest, to relax and spend some time with friends and family. And I hope you're ready to listen to some more great interviews here on the Policy Viz Podcast. I've lined up, I think, what is going to be a really terrific lineup of guests.
00:00:31
Speaker
folks who are doing great work in the fields of data visualization and data science and presentation skills, even some folks who are working with sound in data visualization. So I'm really excited about that to bring all of the guests your way. Before we get into this week's episode, just a quick note that this show is supported by you, the listener.
00:00:53
Speaker
And if you would like to help support the show, either financially or just by spreading the word, that would be great. So if you're interested in financially supporting the show, you can go over to my PreachOn page and sign up and send a few bucks my way so that I can help pay for the sound editing that occurs, the transcription services, all the stuff that's needed to make this show what it is.
00:01:16
Speaker
If you'd like to support the show in other ways, I'd really appreciate it. Share it with your friends, put it on social media, let other people know about it so that they can hear all the lessons and skills and tools that they need to do a good job and a better job with their data and with their analysis.
Guest Introduction: Alberto Cairo
00:01:32
Speaker
So I'm really excited to launch this season with my good friend Alberto Cairo. You probably know Alberto's name if you're in the data visualization field. Alberto is a journalist and visualization designer. He is the night chair at the School of Communication at the University of Miami. And he's also the author of a new book, How Charts Lie, that is coming out in just a couple of weeks.
00:01:57
Speaker
Alberto spends a lot of time thinking about how people read visualizations and they perceive visualizations and how visualizations can mislead them. And that's what this book, How Charts Lie, is all about. So in this week's episode, Alberto and I talk about the book, why he wrote it, how some of the charts he had to describe and critique maybe made him a little
00:02:20
Speaker
angry, a little disappointed in the way people present their data. And we talk about charts that are misleading intentionally versus those that just are using bad data visualization techniques. I think there's a lot to learn both from the interview and from Alberto's forthcoming book.
New Season Announcement
00:02:36
Speaker
So I hope you'll enjoy this episode. I hope you'll stick with me for the next few weeks as I bring you some great guests doing work in the field. And so here we go.
00:02:45
Speaker
Season number six of the policy of his podcast is going to kick off with Alberto Cairo. Hey, Alberto, how is it going? Doing good. How are you? I'm good. I'm good. Getting towards the end of summer, kids getting ready for school, middle school for my oldest. So there's a lot of nerves going on over here. A lot of work. So I'm in the same situation. My kids are about to be in school. So we are getting ready for that. So it's a busy time.
00:03:15
Speaker
Yup. And you've got to get ready for teaching in the fall, right?
Balancing Academia and Promotions
00:03:18
Speaker
Exactly. In the exact same week. Oh, wow. Oh, you start early. Everybody goes back to school in the same week.
00:03:24
Speaker
Right, right. And you've got this new book, and you've been promoting it, and you're going to be getting out there and doing the rounds. So I want to talk about the book and some other things that we can talk about. But maybe you can just start by giving us the description of the book, why you wrote it.
Motivation Behind 'How Charts Lie'
00:03:41
Speaker
Give us the pitch, as it were. Sure. So the title of the new book is How Chats Lie, Getting Smarter About Visual Information. It comes out in October the 15th this year.
00:03:53
Speaker
And I've been working on it for the last couple of years or so. And the reason why I wrote this book is that before and after the 2016 election, I started getting interested in the many ways that people misinterpret, misuse, misread different kinds of data visualization and then how they employ them
00:04:19
Speaker
to push their personal agendas, to push ideas, to promote ideologies, et cetera. So I started doing some reading and some writing and some thinking about what could be done to avoid that, how I could perhaps help non-specialists, normal people, people in the general public, become better readers of charts. So what things we should pay attention to whenever we see
00:04:46
Speaker
a data map or a data graph or a diagram, etc. What are the features that we need to read, things that we need to pay attention to, the most common ways in which a chart can be seen, and above all, how we can avoid lying to ourselves with the charts that we see by projecting what we already want to believe onto a chart that tells a completely different story, right? So I started getting some ideas about that, putting them on paper, and that's where the book comes from.
00:05:16
Speaker
Yeah, no, that last point is interesting how we put our own biases on top of what we're seeing. Yeah, that's actually the most common problem whenever we see charts. And one of the main reasons charts mislead us so often. So I think that is the first thing that we need to address if we want to become better readers of charts.
00:05:37
Speaker
Right, let me ask, so I've seen a lot of these graphs that you've critiqued and you've been doing a lot more blog writing I've noticed to highlight some of these. Was collecting these and writing about them, was it like infinitely frustrating and infuriating?
00:05:49
Speaker
There are some examples in the book that are very infuriating and worrying and even sad. For example, I talk about a Dilan Roof, the guy who entered a church in South Carolina and he shot
00:06:08
Speaker
a racist who entered a church and shot several people. And by reading that very sad and very terrible story, I discovered that one of the reasons why his racism increased is that he read several reports by several racist publications that were misusing
00:06:25
Speaker
data and charts to sort of prove quotation marks in there that African-Americans target whites more often than whites target African-Americans when it comes to committing a crime. When black criminals target white people, that happens more often than white criminals targeting black people.
Impact of Misleading Charts
00:06:45
Speaker
And I'm not going to get into the details of why all these graphics are basically crap.
00:06:52
Speaker
I describe that in a lot of detail in the book. But it's like it's a very sad story and actually demonstrates that bad charts can sometimes have terrible consequences. So this guy, Ruth, will be a racist regardless of the existence of these graphics, I believe, because he was a racist since he was a child. But I think that the charts contributed to basically ground his beliefs and strengthen his beliefs even more. So cases like these are certainly infuriating.
00:07:22
Speaker
And there are a few other charts in the book that I believe that were designed intentionally to mislead people, and I call them out, obviously. But most of the examples in the book are examples of charts that are otherwise perfectly designed, but that are often misread or misinterpreted. And this is not infuriating. It's just a fact of life. I mean, we are taught
00:07:44
Speaker
or we are told that we should be able to intuitively understand visualization, that visualization is easy to read, that a picture is worth a thousand words, and things like that. And in the book, I try to demonstrate that all these myths are actually myths, and that we need to abandon them. That visualization is sort of like written language. You need to pay attention to it. You need to read it carefully in order to interpret it correctly.
00:08:14
Speaker
Yeah, I'm always surprised when I show people, you know, a different type of graph. You're like, you know, what we like a slope chart or a dot plot that we have sort, you know, that we in the field know now instinctively. And people say, I can't show this to my boss or my manager or whatever, because they'll never understand it.
00:08:29
Speaker
And I find that interesting because it's not like we know instinctively in our DNA how to read a bar chart. We have to learn how to read a bar chart. I perfectly remember when I learned to read a scatter block, for example. And it was not intuitive. I needed to pay attention to the chart, take a look at the axis, read a little description, read a little caption that the chart had in order to interpret it correctly. So in the book, I talk about visual literacy, obviously. There's a term
00:08:57
Speaker
to refer to visualization literacy. The term is graphicacy. And I explained where that term comes from and other authors that have used it in the past. And I said that the problem is that we lack graphicacy, but we can increase graphicacy. The problem with people who react negatively to novel graphic forms is that they say, well, my reader is not going to understand this chart.
00:09:22
Speaker
So they refrain from using that chart. But that's the wrong response. The wrong response is to say, well, if this is the best chart to represent your data or to tell or to convey the message that you want to convey, do use it. Go ahead and use it. But also explain how to read it. Right. Help, you know, guide your readers by the hand in order for them to understand what's going on in that chart. And then the next time that they see that same type of chart, they will be able to read it on their own. You have you would have increased their graphic.
00:09:52
Speaker
Yeah, yeah, absolutely. So so you talked about a couple of these examples where the graph was intentionally
Designer vs. Reader Responsibility
00:10:00
Speaker
misleading or misrepresenting the data, what's the balance between those type of charts that are intentionally misleading versus one that that use bad data visualization techniques?
00:10:11
Speaker
Well, in the book itself, in how charts lie, I would say that 20, 25% of the examples are charts that I guess, it's just a guess, that I guess that are intentionally misleading. The other 75% are charts that are either well-designed,
00:10:29
Speaker
but misinterpreted anyway, or charts that are designed with good intentions in mind, but that they employ visualization techniques that are not appropriate for a particular audience. And as a consequence of that, they end up being misleading.
00:10:44
Speaker
Anyway, the result is the same. The audience that is reading the graphic is misled. So I would say that that's sort of the balance. Because again, I'm much more interested, not in the intentions of the designers who create those charts, I'm much more interested in the consequences of those charts, of how the public can use charts to basically have better lives, to be more informed, to be better informed.
00:11:10
Speaker
But when you say that a graph is being misinterpreted, do you put the onus or the responsibility on the graph creator or the reader of the graph?
00:11:40
Speaker
The designer uses a novel graphic form, explain it so people will understand it. So there's always a responsibility on the part of the designer. But there is also a responsibility on the part of the reader. And this is connected to what I said before about the myths that surround data visualization. We have been told so many times
00:11:59
Speaker
that a visualization needs to be intuitive and must be easy to read and simple etc and we have been told that a picture should be worth a thousand words and blah blah blah that we have internalized that we can understand a visualization just by looking at it and rather than reading it.
00:12:15
Speaker
And we do need to read it. We need to pay attention. You cannot assume that you will understand a chart if you don't read it carefully. You do need to read it carefully. And if you don't read it carefully and you misinterpret the chart, if the chart is well designed, then the responsibility is not the designer's responsibility. It is your responsibility as a reader.
00:12:36
Speaker
Yeah. Where do you place the responsibility with using the data in this, I guess, process of extracting and analyzing the data, making the visualization and then publishing it? Where, you know, how do you separate the data part from the visualization part?
00:12:53
Speaker
Well, in most cases, you cannot really separate the data from the visualization just because the visualization is sort of the data made visible or the data made physical so people can see patterns and trends in the data. So the data and the visualization are intrinsically connected.
00:13:11
Speaker
Unless that you're doing, this is just an aside, unless you're doing a data art project, something that is a little bit more expressive. In that case, the goal of the visualization is not to illuminate anything about the data, it's more to create sort of an aesthetic experience based on the data. In that case, the data is a little bit secondary in comparison to the visual experience. But in most cases, when you do a visualization, the point or the goal of the visualization is to be able to see something.
00:13:37
Speaker
from the data. Whose responsibility is it? Well, it's the designer's responsibility, obviously, to try to get the data right, try to talk to experts who know much more than you do about the data, etc., etc., to verify what it is that you are presenting to the extent of your knowledge.
00:13:54
Speaker
At the same time again, in connecting to what I said before, there is a responsibility on the part of the reader to try to read the graphic carefully and not make assumptions about the graphic. And there's a specific example that I have in the book that explains this idea well. I show a chart, and let me say beforehand that this is a mistake that I have made myself repeatedly.
00:14:18
Speaker
So there is a chart that shows, it's a scatter plot, that shows a positive association between cigarette consumption per capita and life expectancy. This is an example that I borrowed from Heather Cross, who is a statistician.
00:14:34
Speaker
And that chart, it shows positive association. The larger or the bigger is the cigarette consumption per capita, country by country, the higher the life expectancy of those countries is, right? So if you're a cigarette smoker and you don't read, you don't think about the graphic carefully, you may describe the content of that chart. The more we smoke, the longer we leave.
00:15:01
Speaker
Right. And that's not what the chart is showing. What the chart is showing is that there is a positive association between cigarette consumption and life expectancy and vice versa. But that doesn't mean that one of these is connected to the other in any sense. There's the problem with correlation causation. There's problem with ecological fallacies. There is a problem with Simpson's paradoxes and many other things. So readers need to make an effort in sort of
00:15:28
Speaker
stick to the idea that a chart shows only what it shows and nothing else. Most of the other inferences that we made out of charts are inferences that happen in our brains. They are not in the chart itself, and that's a perfect example of that.
00:15:43
Speaker
Now, in a case like that, obviously, there's also a huge responsibility on the part of the designer. If I were to make that chart myself and publish it, I would add a very big caption warning people about not to see that in the chart. This chart is not showing that the more that you smoke, the longer you will leave.
00:16:03
Speaker
It just shows that countries that are richer, on average, can buy more cigarettes. And countries that are richer tend to also have better healthcare systems. And as a consequence of that, and many other factors such as diets and exercise and things like that, people also tend to live longer.
00:16:21
Speaker
Yeah, it's really interesting. Because I think when people see any visual stimuli, they're, they're led to make conclusions or see patterns, right? So, so this this point of trying to say, Hey, don't don't draw causal link between the two. Try to only see, you know, try to only see the correlation seems like a, I guess it's, it's kind of a heavy lift, right for designers.
Guiding Audience Understanding
00:16:47
Speaker
It is a heavy lift, but as I said, I mean, I think that the designer can make an effort to add, you know, if you are going to publish a chart like that, there may be a good reason that you want to publish a chart like that. You may want to make a point about that association for some reason. You know, you can use some space in the chart to warn people about what the chart is not showing or what are the possible wrong inferences that you can extract from the chart.
00:17:12
Speaker
Yeah, it's also a question of audience, right? Like where do you draw the line of what do I need to explain to what audience member, right? Like a scatterplot in an economics peer-reviewed journal isn't going to need a lot of the explanation, but on the Washington Post website, it probably does. And so then you have these audiences in between. Like how do you think about it? I mean, you're a journalist, you think about different types of audiences. So how do you think about targeting different audiences and trying to meet their expertise where they are?
00:17:42
Speaker
I prefer to put the emphasis on explanation, so I try to put myself in a frame of mind in which I assume that people know a little bit less than I believe they do.
00:17:57
Speaker
So I try to add more explanations rather than less explanations, just to avoid these kinds of problems. The problem with that, obviously, is that you can end up having visualizations that are a little bit overburdened with explanations and text and annotations, et cetera, et cetera. But I prefer it that way. I think that, again, as you said, visualization can sometimes lead you to see patterns.
00:18:21
Speaker
that are not really there or make inferences that are not warranted by the data or by the graphic. And I think that it is worth it to warn people about that. So there's a responsibility on our end to do that. Yeah, definitely. I want to switch gears a little bit and ask about your process. I mean, you've written, let's see, this is your what, fourth book. You've got a couple more in the works, I think. We can talk about those.
00:18:50
Speaker
This one is interesting because I know you've, you, this isn't a topic of interest you've had for awhile. And then, uh, what was it last year? Maybe you did your visual Trump re-tour where you sort of, you know, went around the world that looked like and, and talked about these topics. And I'm curious about your writing process and also how that tour.
00:19:10
Speaker
and your conversations and your presentations affected what you wrote, how you wrote, and things that people said to you. What was that experience like? Yes. The tour between 2016 and 2018, I did a couple of visual Trump retalks also in 2019, but mostly all of them took place between 2016.
00:19:34
Speaker
and 2018. Basically, the talks, this series of presentations, they led to the book. So I first put the slides together, gathered tons of examples that I had on my computer, added some more, et cetera. And I structured the talk as a talk explaining the systematic ways in which either charts are designed to lie in purpose or the ways in which we mislead ourselves or lie to ourselves with charts.
00:20:03
Speaker
And I use the talk sort of unconsciously as a way to test ideas, examples, see how people reacted to those examples, notice what people understood or didn't understand in the examples that I was presenting.
00:20:18
Speaker
And that shaped the book because originally I was planning to do a book just about lying charts. These are just charts that lie. These are charts that lie for this reason, for another reason. These are misleading charts for this reason, for another reason.
00:20:33
Speaker
But what I realized is that that's not what people need. Because if you only do that, you're not giving people the tools to take advantage of charts. Because the title is how charts lie. It's a provocative title. But the subtitle gives you a clue of what the book is truly about. Because the book is not a book about, here's a ton of line charts, or here's a ton of misleading graphics. The book is a manual.
00:20:59
Speaker
about how to become a better chart reader. So I added a whole chapter that is basically sort of a grammar of graphics light, the famous book, The Grammar of Graphics, you know, how a graphic is structured, how visualization is structured. What is visual encoding? I explained to the general public what visual encoding is, right? So I devote tons of pages to basically explain how charts are read, right? The same way that
00:21:26
Speaker
We need to teach people how to read words. We can also teach people how to read visuals, how to read visualizations.
Educating Visual Literacy
00:21:34
Speaker
So the tone of the book is positive. Originally, the tone was a slightly negative phrase. This is bullshit. This is a bad chart. This is whatever. And there's certainly something about that in the book itself. There are plenty of examples that are really, really bad. But most of the book has a very positive tone. It's not
00:21:53
Speaker
The book doesn't just say, charts mislead us very often. It also says, but charts can be used to make us smarter, to make us better human beings and more informed. And this is how kids ought to do it. I'm going to make you king of the world for a moment, or at least king of the education system.
00:22:14
Speaker
What would you change in the way people learn how to read charts from kindergarten all the way through college? How would you change the curriculum? Well, I don't think that we can really detach
00:22:30
Speaker
graphical literacy or graphicacy from numerical literacy, also called numeracy. There's a famous book titled In Numeracy by John Allen Paulus, which is fantastic. It's a fantastic book. I think that both things go hand in hand. We need to help people become more numerate, become more
00:22:51
Speaker
used to dealing with numbers or reason based on numbers. And then we can also teach people, help people become more visually literate, more graphicate, right? Those good things go hand in hand. Now, how to do it, I have no idea. I mean, I don't know. I'm an educator, but I'm not used to teaching small children. The way that perhaps we could do it in math classes is to spend a little bit less time
00:23:20
Speaker
time, making children do complex calculations by hand, and spend more time discussing how the numbers that they see every day in the classroom apply or relate to their lives. Perhaps use examples that speak to them, so more examples about music or movies or things like that.
00:23:44
Speaker
and then talk about how to reason about the numbers related to those topics that they care about. What is the album or the song that has sold more copies in the past? What is the song that has made more money in the past 10 years? And you can use that to explain adjusting for inflation. That song that was published this year obviously will make much more money than a song that was published 20 years ago. But that's just an effect of inflation, if you don't adjust for inflation.
00:24:14
Speaker
then it will appear that way. So you can use that as an example as an entry point to explain a complex idea or a complex issue. But again, this is just a very general idea. I don't know. I just think that I'm more fond about classes that sort of expand your mind by helping you see the multiple angles in which you can approach a topic.
00:24:34
Speaker
rather than just teaching people how to mechanically complete operations by hand, which I also believe is necessary. It is necessary to multiply. But after you have done that 10 times, just use a damn calculator.
00:24:50
Speaker
Well, what's interesting about the field of data visualization is that it brings a lot of these different skill sets together. You've got the math and you've got the literature and you've got design and art and you've got even computer science. It's bringing all these different skill sets and philosophies together into one area. Not only quantitative fields, it also brings together rhetoric,
00:25:18
Speaker
Yeah, journalism and narrative and storytelling is like it's a bunch of stuff, right? Yeah. So would you change the way people are taught visuals at the university level? Yeah, absolutely. So and actually this main form, this idea main form, one of the books that I have planned for the near future. It's a silly little bit vague in my mind.
00:25:47
Speaker
But I would like to follow the path of Andy Kirk. You know that Andy wrote his book with the idea that visualization is a process, right? So just go deeper into that idea and write a book that talks about
00:26:04
Speaker
How to reason about visualization? How to make good decisions about visualization? Not by applying cookie cutter rules, which is how visualization is usually taught. Here's a bar chart. Use the bar chart for these. Here's a scatter plot. Use a scatter plot for these.
00:26:21
Speaker
and go deeper into the reasoning behind all those rules. And that way, I think that people will understand better when the rule is applicable and when the rule needs to be broken, or when the rule can be basically just avoided, or how to create new rules and how to expand the vocabulary of data visualization. So how to think about visualization, how to reason about visualization, or how visualization designers currently think, right? That will be another way.
00:26:50
Speaker
in which in which people can learn so i think that that's the way to teach visualization at the moment to anybody who wants to learn it. Do you think the the database field is is pivoting in that direction in terms of what people are speaking about and writing about on on blogs and on and on websites?
00:27:10
Speaker
People who have been in the field for a relatively long time, absolutely. They are pivoting in that direction. Yeah, the field is pivoting in that direction. But I don't care that much about the people who have a lot of experience, right? They are autonomous, they can learn their own, right? I'm more worried about the people who are entering the field at this moment, right? We need, I think, to find the balance between saying,
00:27:34
Speaker
You know, there are certain rules, quotation marking there, there are certain principles, there are certain conventions, there is a tradition in data visualization, and you need to respect all that because there is a reason why all these things exist.
00:27:52
Speaker
But at the same time, it is also important to understand where all these conventions, principles and rules come from, which one of them are more or less supported by either evidence or logic or practice, etc. Learn how they were developed, etc. And then learn how to
00:28:12
Speaker
break them or how to expand them or how to create new ones in the future. So yeah, we are pivoting in that direction, but I think that we need to pivot perhaps a little bit more. Interesting. I have the reading company here in front of me and I've been going through it again. I think this is like the third time I've read it. It's great. I'm really enjoying it. And I look forward to seeing it come out and make its ways around the world and see what people
Episode Wrap-Up & Promotions
00:28:38
Speaker
say about it. Thank you, John. You're very, very kind. Well, thanks, Alberto. Always fun to chat with you.
00:28:49
Speaker
And thanks to everyone for tuning into this week's episode. I hope you enjoyed it. I hope you'll check out Alberto's new book, How Charts Lie. It is coming out any day now. And if you're interested in seeing Alberto speak in person, he'll be at the Urban Institute in October.
00:29:04
Speaker
to talk about his book. So stay tuned for information on that. That'll be coming out in a little while. And if you'd like to support this podcast, please check out my Patreon page or just share the show with your friends and your colleagues and review the show on your favorite podcast provider. So until next time, this has been the policy of this podcast. Thanks so much for listening.