Episode Notes
Alberto Cairo is an associate professor and the Knight Chair in Visual Journalism at the University of Miami’s School of Communication. The former director for infographics and multimedia at Editora Globo, the magazine division of the biggest media group in Brazil, he has been described by Microsoft as having “spent his entire career in the vanguard of visual journalism.”
In September, Alberto visited Notre Dame’s online master’s program in data science and delivered a public lecture as part of the College of Science’s John A. Lynch Lecture Series. He is the author of three books, including How Charts Lie, which is being published by W.W. Norton & Company literally next week. Well, next week from when we’re releasing this episode. So, just to be safe: The book comes out—or if you’re listening to this in the future, came out—Tuesday, Oct. 15, 2019.
And here, Alberto and host Ted Fox talk all about it, from the five different categories of lies charts can tell us to why calling the book How Charts Lie is a provocation, an invitation to think about how we read and misread them—not a rejection of their usefulness and importance.
Because one thing becomes clear when talking to Alberto: He likes charts. So much so that he’s devoted an entire book to helping us get better at how we use them.
LINKS
- Alberto’s Book: How Charts Lie
- Alberto’s Piece from Scientific American: Graphics That Seem Clear Can Easily Be Misread
Episode Transcript
*Note: We do our best to make these transcripts as accurate as we can. That said, if you want to quote from one of our episodes, particularly the words of our guests, please listen to the audio whenever possible. Thanks.
Ted Fox 0:00
(voiceover) From the University of Notre Dame, this is With a Side of Knowledge, the show that invites scholars, makers, and professionals out to brunch for an informal conversation about their work. I'm your host, Ted Fox. With a Side of Knowledge is supported by Sorin's restaurant inside Notre Dame's Morris Inn, which serves breakfast and lunch seven days a week and dinner Tuesday through Saturday. If you see us recording, feel free to stop by and say hi--preferably not when we're chewing. And when we're not recording, or chewing, you can always find us on Twitter, where we are @withasideofpod.
Alberto Cairo is an associate professor and the Knight Chair in Visual Journalism at the University of Miami's School of Communication. The former director for infographics and multimedia at Editora Globo, the magazine division of the biggest media group in Brazil, he has been described by Microsoft as having, quote, "spent his entire career in the vanguard of visual journalism." In September, Alberto visited Notre Dame's online master's program in data science and delivered a public lecture as part of the College of Science's John A. Lynch Lecture Series. He is the author of three books, including How Charts Lie, which is being published by W.W. Norton & Company literally next week. Well, next week from when we're releasing this episode. So just to be safe, the book comes out--or if you're listening to this in the future, came out--Tuesday, October 15, 2019. And here, Alberto and I talk all about it, from the five different categories of lies charts can tell us to why calling the book How Charts Lie is a provocation, an invitation to think about how we read and misread them, not a rejection of their usefulness and importance. Because one thing becomes clear when talking to Alberto: He likes charts. So much so that he's devoted an entire book to helping us get better at how we use them. (end voiceover)
So Alberto Cairo, welcome to With a Side of Knowledge.
Alberto Cairo 2:21
Oh, thank you. Thank you for having me. It's a pleasure.
Ted Fox 2:24
So I thought we could start out with a concrete example of, to borrow the title of your book, how charts lie. In a brief piece you did for the September 2019 issue of Scientific American, which we'll share on our Twitter and in the episode notes, you break down a seemingly simple chart plotting life expectancy with respect to obesity--specifically the obesity rates of entire countries. And you note that the correlation on this chart between longer life expectancy and higher rates of obesity is quite strong. In other words, as one goes up, so does the other.
Alberto Cairo 3:00
It's a positive--it's a positive association between the two.
Ted Fox 3:02
Which seems really counterintuitive, and someone looks at it and goes, Well, the more obese I am, the longer I live. So, just as we're starting now, what are the ways or some of the ways a chart like this example, they're getting misread and leading people to draw erroneous conclusions?
Alberto Cairo 3:20
Well, the Scientific American example is based on an example that appears in How Charts Lie, which correlates not obesity and life expectancy but worse: cigarette consumption and life expectancy. There's quite a strong positive association between cigarette consumption per capita, per person, and life expectancy of an individual country-by-country. Those are country averages, obviously. If you plot in your mind a scatterplot, in which each dot is a country, and you place the countries on the horizontal axis in proportion to cigarette consumption, the farther to the right a country is--meaning the higher the cigarette consumption or the obesity in the Scientific American piece--the higher up that country tends to be on the y axis, which is life expectancy. So what I say in the book is that many journalists--and this includes myself, by the way, 10, 15 years ago--and many, many casual readers, like non-expert readers, will describe the content of this chart, as you said, as, The more I smoke, the longer I will live, right? And there's a very natural reason why that happens because it's basically what the chart is sort of suggesting unless you remind yourself the dots in this chart are not people; the dots in these charts are countries. And there may be other possible factors that contribute both to the increase in obesity and the increase in cigarette consumption and the increase in life expectancy.
For example, wealth--wealth is correlated to both cigarette consumption per capita and is correlated also to obesity. In general, the richer a country is, the more obese people are, but there are some exceptions to that, but the association is there. Therefore, wealth contributes in many other ways to the increase in life expectancy and increase in cigarette consumption. It is only that if a country increases its cigarette consumption per capita, that decreases life expectancy. But that decrease in life expectancy is compensated, is balanced out, by the fact that wealth also leads to better health care and to better diets and to safer environments. And that increases life expectancy. The reason why this happens, by the way, considering your question why we misread these kinds of charts is that we don't pay--there are exceptions to all these things that I'm saying--but in general, I have observed throughout the years that people all over the political spectrum, all over the educational spectrum, we tend to pay less attention to charts than we do to text. Because we assume, unconsciously perhaps, that a chart is a picture, that a chart is an image, and therefore that we can interpret it correctly just by looking at it. And the argument that I make in the book, the core argument of the book, is that a chart is not a picture, and it is not just an image; it is a visual argument or an argument made visual. And therefore in order to understand that argument, you need to read it in order to decode it correctly and extract the right meaning from it.
Ted Fox 6:12
And on that point, you write near the end of the introduction, "We"--and you're talking about as a society--"must become more graphicate."
Alberto Cairo 6:19
Mm hmm, mm hmm.
Ted Fox 6:21
What are we talking about when we're talking about graphicacy--related to literacy, but graphicacy?
Alberto Cairo 6:26
Yeah, graphicacy. That's a very strange term that was invented in the '50s to talk about visual and in particular graphical literacy, the ability to read charts, maps, graphs, diagrams, and other kinds of informational visuals, and it's related to literacy, right? There are several books that talk about it. There is a wonderful book by Mark Monmonier, who is a cartographer, and in the book Monmonier says that today, in order to consider ourselves an educated citizen, we need to be taught or we need to learn much more than literacy, the ability to read and write. We also need articulacy, which is the ability to express yourself correctly in the spoken language. And also you need numeracy, which is the ability to think about numbers and scientific evidence. It's not mathematics alone, it's not statistics alone, numeracy is sort of our sixth sense that will start ringing when you see a number that doesn't sound right, for example, or when you see a chart and you say, Well, there's something wrong with this chart, I don't know exactly what it is, I think that I'm reading it wrong. You don't know exactly why. But something in the back of your brain, it starts ringing. And in relation to numeracy, we have graphicacy, which is graphical literacy. That's what it means.
Ted Fox 7:42
One thing I realized I had never consciously thought about--that seems like a weird thing to say--before your book was the idea that someone had to invent charts. Someone had to invent visual arguments. And you don't spend a lot of time on it, but you talk about it briefly. When and how did that happen? Because in the big scheme of humanity, it really wasn't that long ago that people started trying to make arguments this way.
Alberto Cairo 8:11
Yeah. Yeah, I don't spend a lot of time on that in the book because that would be a whole book on its own. There are several books, actually wonderful books, about the history of information, graphics, and the history of charts, history of graphs. There's just one that came out recently called The History of Infographics, actually, by Sandra Rendgen, which is wonderful. And there's another one coming out by Michael Friendly. And anyway, so in the book, I just give a very brief overview of where charts come from. So charts, in particular graphs, graphs that have an x axis and a y axis, are basically a derivation of cartography. So it's like what happened was that--cartography has been around for centuries, right? The idea of plotting the world and features of the world in an x and y scale, in a Cartesian scale, that idea has been around for a very long time. Since the (inaudible), but even before that the idea of mapping was there. What happened was that around the end of the 18th century, certain people--in particular a person called William Playfair--they realized that x and y axes, or a set of axes, longitude and latitude in maps, those numbers, those measures could be substituted by any other kind of measurement on those axes. And they realized, Well, what about if instead of latitude and longitude, I put, for example, years on the horizontal axis and some sort of magnitude on the vertical axis, and then I plot dots according to those two positions, x and y? And then I connect the dots to each other, I will have a line chart like a line graph, a time-series line graph, right? So that sounds so obvious to us today. But at the time, it was probably a revolution, right? And that revolution continued during the 19th century because the 19th century is--the 18th century and the 19th century are the centuries of quantification and the development of the elementary methods of statistics. So in the 19th century, you have some of the main landmarks in the history of data visualization of charts. And I talk a little bit about that at the end of the book because I talk about, for example, about Florence Nightingale, the famous nurse who [went] to war in Crimea, and I tell you all this history and talk about the charts that she created, right? But there are many other historical figures in the 19th century that helped the development of charts.
Ted Fox 10:26
So following the introduction, and the chapter on how charts work, you do five chapters on charts that lie in particular ways. And I'm not asking you to give away everything that's in the book because the book comes out--when this airs or when we publish this, I believe it's five days after we publish this, the book comes out. But I'm wondering if we could do a brief run-through of each of these categories just to give people a fuller sense of what you're talking about. So first one is, charts that lie by being poorly designed.
Alberto Cairo 10:54
Yeah. Well, that's a pretty obvious one, right? Well, first of all, I need to clarify that the title of the book is actually a provocation. Because the book is not just about charts that lie on purpose, or even mainly about charts that lie on purpose. The book is mostly about how charts that are otherwise correctly designed may end up misleading us just because we don't pay attention to them, or because we don't have the right knowledge to read them, or because we tend to lie to ourselves when we see a chart. We tend to project our own beliefs onto the charts that we see, right?
Ted Fox 11:28
Well, I mean to your initial example, talking about like, Well everyone's telling me how bad it is that I smoke ...
Alberto Cairo 11:32
But take a look at this chart, right?
Ted Fox 11:33
But this chart tells me that I'm gonna live forever.
Alberto Cairo 11:35
And there is nothing wrong with the chart itself. The chart is just showing that at the national level, there is a positive association between cigarette consumption and life expectancy, right? So the chart itself is not wrong. It's our interpretation of the chart that is wrong. So the book is about chart interpretation. So how to interpret a chart correctly. Anyway, so that chapter that you're mentioning, that chapter is the one that contains most of the examples that are really charged, that are designed to mislead people, right? So for example, if you truncate the Y axis in deceitful ways, for example, to exaggerate a metric or to underplay something, etc. So that's a very common trick that peddlers of propaganda tend to use quite a lot, right? Truncating bar graphs or truncating line charts in mischievous ways, or selecting the color schemes of data maps to emphasize something that they want to emphasize or to de-emphasize something that they want to hide. So there are many different techniques that can be used to basically distort or warp a chart, and it is good that general readers become aware of these little tricks just because then you can defend yourself against them.
Ted Fox 12:40
(voiceover) Hey, just taking a quick break to tell you about another podcast from Notre Dame we're pretty sure you're gonna enjoy. It's called Notre Dame Stories and hosted by our friend Andy Fuller. It features interviews with Notre Dame faculty making headlines as well as stories about the life and work of the University. It's even won a platinum award from the Association of Marketing and Communications Professionals. You can find Notre Dame Stories wherever you get your podcasts. And now, back to the show. (end voiceover)
How about charts that lie by displaying dubious data?
Alberto Cairo 13:14
Well, that's a key one, right? So that's one of the chapters when I start getting into how important the role of the reader is. So I think that readers have a huge responsibility nowadays. So the traditional literature on data visualization puts a lot of emphasis on the role of the designer, right? The ethics of the designer, how important it is that the designer designs a good chart that can be understood with the right data. And I think that we need to keep doing that, obviously. The designer is the main [person] responsible for a chart being understandable. But there is also some onus on the part of the reader, and the reader has a responsibility. The reader is not a child. The reader is supposed to be someone who can think clearly and rationally about things that we are seeing, right? So therefore, there is a lot of responsibility on the part of the reader I think to, for example, take a look at the source of the data. Well, first of all, if a chart doesn't disclose the source of the data, if it doesn't say where the data comes from, just distrust it. Period.
Ted Fox 14:06
(laughing) That's a big red flag, right?
Alberto Cairo 14:07
Yeah, the big red flag if they don't say where the data comes from. If they link to the original data, then that shows [they're] more trustworthy because it means that the designers don't really mind if you take a look at the original data, which is basically an exercise in transparency, which is great, right? The message that I give in that chapter is that whenever you see a chart, particularly in social media, it is very easy--and I know this because it happens to me all the time--it's very easy that you see a chart and that chart sort of confirms something that you already believe, and you retweet it automatically. You don't even think about the chart, Oh, this chart is saying that, oh, great. And you retweet it. What I advocate for in the book is that you need to curb that impulse a little bit. I learned the hard way to do that myself by making mistakes. So don't retweet it, don't post it on Facebook or on Instagram. Just give yourself two minutes. Two minutes or one minute--one minute sometimes is enough just to read what the source is. Then if you can, go to the primary source, take a quick look at the primary source, for example, to see whether the metrics that are shown in the chart are actually measuring what the chart says is being measured, right? Only 30 seconds sometimes are enough just to make sure whether the chart is actually reliable or not, right? You will not be able, doing this, to avoid, you know, retweeting wrong charts 100 percent of the time, just because most of us are not data scientists, or a specialist. But with this little exercise in paying attention, I'm pretty certain that you can avoid 50 percent or more of the occasions in which you will retweet a wrong chart. And that's progress.
Ted Fox 15:37
And I mean, it really, just what you were saying there, too, about kind of the Facebook or Twitter example. I mean, people take that responsibility at different levels, but we have such an ability to share information now that we're--it's kind of this democratization of information, but a lot of those ...
Alberto Cairo 15:56
Which is extremely positive, and I say that in the book explicitly. I'm a great believer in the power of communication and the power of social media and on the internet, etc. But at the same time, that power comes with responsibility as the movie says, right? "With great power comes great responsibility."
Ted Fox 16:10
(laughing) That's right.
Alberto Cairo 16:10
Spider Man movie, right? And that is completely true. That's a great line, actually, that's a great quote. Because right now we all have the power to spread information or misinformation. As I said in How Charts Lie, nowadays, we are all sort of publishers. And if we are all publishers, we all have a publisher's responsibility to sort of make sure that what we put out is actually reliable.
Ted Fox 16:33
Right. We don't have an editor or an ombudsperson sitting on our shoulder ...
Alberto Cairo 16:38
But we can try to auto-edit ourselves somehow. And I wish that this kind of, you know, this sort of teaching could be taught in elementary school, for example, or in middle-- particularly in middle school and high school--to teach children and teenagers in particular, or even young college students, how important it is to curb our own impulses, how important it is to control yourself consciously. Because otherwise, you just mindlessly, emotionally retweet things all the time. And again, this is something that happens to me, it's something that happens to you, it's something that happens to everybody. But we can become more mindful.
Ted Fox 17:12
This seems like maybe, I don't know if it's the other side of the same coin, but charts that lie by displaying insufficient data.
Alberto Cairo 17:18
Oh, that's a big one, right? So this is a huge problem in journalism, for example, the world that I come from, right? So if you talk to journalists, and you ask journalists, What is your main task, what drives your job, what is your main goal? They would say, Well, I like to tell stories, I would like to reveal the truth etc. But one big one is to say, I like to simplify information. And when I talk to journalists, I say, Well, our work is not to simplify information; our work is to translate information to reduce complexity by explaining that complexity in words or in visuals that general people can understand. But that is not simplification. That's clarification, which is different, right? And it's also translation. And I say this because when you think about in terms of simplification, usually what happens in the minds of many people is that we tend to think about simplification as reduction, reducing the amount of information that we show, and sometimes that's the right drive because if you show too much information, the graphic that you're designing will become completely impossible to understand. But you can go too far, you're going to oversimplify. So, what I warn about in the book is how dangerous it is to show, for example, I don't know--measures of central tendencies such as the medium or the mean etc., when the distribution behind those measures of central tendency is very skewed, when you have outliers, for example, or extreme values that may distort the average for instance, right? In cases like that, showing just the average is very misleading. You need to show the average and the outliers, the extreme values that are maybe distorting that value, right? And so in that case, instead of simplifying, you will be clarifying, and not by reducing the amount of information, but by increasing the amount of information that you show in order to make the story clearer.
Ted Fox 19:04
Charts that lie by concealing or confusing uncertainty.
Alberto Cairo 19:08
That's another huge one and is an ongoing discussion in the world of data visualization where I come from, as well. So most numbers that we deal with every single day, particularly numbers that relate to people, measures of people, they are always surrounded by a cloud of uncertainty, right? The level of confidence, basically, that the researcher has around the number that they're reporting--that is measured not as a single data point, it's usually a range of possible values. The problem is that particularly news media--but not only, also in research papers in scientific publications--sometimes what is reported only is just the point estimate and not the level of uncertainty that surrounds that point estimate. Now, that sometimes is not a huge problem. For example, if you're comparing, let's say, a survey, or a poll, you do a political poll, an election poll, and you see that candidate A has, you know, 70 percent of the vote probably, and the other candidate has 30, and then the confidence level 95, and then the margin of error is like four points or something like that, then reporting the uncertainty is not that important just because the point estimates are so far from each other, right? However, if--and this is one huge problem in news media--if in the poll, you know, candidate A is 54% and the other candidate is 46, and then the uncertainty, the margin of error, in that case, is, you know, let's say seven points, which is not that unusual in polls with small sample sizes, then you need to report the uncertainty just because you cannot really tell that one candidate is ahead of the other with lots of--I mean, you should doubt yourself if you are going to write that. You need to be extremely careful, right? So the problem is that these measures of uncertainty, of error, they are usually not reported, in particular [in] news media--but as I say, not only. And that misleads people because it misleads people into believing that, you know, again, candidate A is ahead of candidate B, and it may not be true.
Ted Fox 21:00
Right.
Alberto Cairo 21:00
It may not be true. So that will be a case of concealing the uncertainty, right, either on purpose or not on purpose. I know that this is an oversight in most cases; we journalists don't like to show uncertainty for some reason. We just want to tell the story. And the story sometimes is not a story; it's a bunch of stories or possible alternative stories that we need to tell. That's sort of the uncertainty at some level. But then also sometimes when we do display uncertainty, we do it in ways that confuse people just because we don't explain what that uncertainty means. In the book, I go over at great lengths in, you know, maps of a hurricane forecast, for example, that confuse everybody. But again, not because the maps are badly designed, I think, but because the maps are not properly explained. They are not explained how they are supposed to be read.
Ted Fox 21:47
And I saw you in the lead-up to Hurricane Dorian when it was approaching the Florida coast, before it turned, and just trying to get people to look at kind of this cone of possibility that it's showing--what it actually meant versus the way people on Twitter or whatever else are looking at it and thinking, Oh, it means this or I'm in the clear because I'm not in the center of this path.
Alberto Cairo 22:11
Or I'm outside of the cone, right? So yeah, I wrote that New York Times piece in the way that I would explain to friends and family how to read that map. So I don't say that explicitly in the piece itself, but if you read it, you will notice that it sounds--if you read it out loud, it sounds like a professor or a teacher talking to you and explaining how to read the map. Because I'm used to teaching how to read that map to my students, particularly international students who come to Miami for the first time. So I have sometimes in my visualization class, I have a session particularly during hurricane season, telling them, explaining to them how to read those maps. What I'm doing in The New York Times piece and also in these sessions in my classes is to increase their level of graphicacy. One of the myths that I warn against in the book is a myth--which is very common, by the way, in visual design, graphic design, another world that I'm very familiar with--the myth of showing, don't telling, show, don't tell. And I say, well, you need to show and tell, or you need to tell before you show. Because if you show me without telling me, these maps that are very complex, and they represent very complicated realities, they are very ambiguous, people will misinterpret them. But if before showing me the map, or while showing me the map, you explain to me what it is that I'm looking at, you're not only helping me understand that map in particular, but you're also teaching me how to read that type of map. So the next time that I see that cone map, I will be able to interpret it correctly, just because I have the right level of graphicacy.
Ted Fox 23:41
This was related to something you said earlier, but there's a point, I believe it's in the introduction, where you say--and because you were talking earlier about people see something visual, and we assume it should just be, Oh, I can just glance at it, and I understand it--you even make the point of saying, Look, most good charts aren't just easy to analyze.
Alberto Cairo 24:00
They are not intuitive.
Ted Fox 24:00
Because they're trying to explain something that's complicated or has nuance or whatever else.
Alberto Cairo 24:05
And you need to read it. Yeah, yeah, that's another myth or another problem, another bias that we have. And I blame we journalists and designers for spreading these myths: "A picture is worth a thousand words" or "The data should speak for itself," "Show, don't tell." But the one that you're talking about is another one [myth] that I mentioned, I believe explicitly, which is that charts are intuitive; data visualizations are intuitive just because they are like pictures or you just look at them. I heard tons of times in newsrooms, for example, they're saying, Oh, let's do an infographic because people don't read anymore; therefore, let's do an infographic--forgetting that an infographic is also a text. It's a text made visual, an argument made visual, people need to read the graphic in order to understand it. So it's actually not easier to read than a text; it's just a completely different way of displaying information that is adapted to certain circumstances, right?
Ted Fox 24:51
So the last of those five chapters that was I talking about, it's charts that lie by suggesting misleading patterns.
Alberto Cairo 24:59
Or making us suggest to ourselves misleading patterns. This goes back full circle to the example that you opened the interview with, the obesity or cigarette consumption versus life expectancy. So that chart suggests a misleading pattern, but it's not--I don't think we should blame the chart itself. We should blame the person who's reading that chart--both the designer and the person who reads the chart, right? So it's like, that's a case in which a chart that otherwise, it's fine, right, ends up being misread or ends up being interpreted in one way because the reader is rushed, or because the reader doesn't pay attention, or because the reader doesn't have enough knowledge to interpret the chart correctly. Now what to do in those cases? Well, I think that on one hand, there is a responsibility on the part of the designer that you're going to do a chart like that, cigarette consumption/life expectancy, and you sort of know that most common people will misinterpret that chart, put a caption saying, You may be reading this chart this way, but this is not how to read it, this is what it actually shows, right? So this is an example of the show and tell that I was talking about before. So add a caption, or you can do a video, put yourself in front of the chart and explain how to read the chart. So there's nothing wrong with the chart, but you need to explain it. And then on the other hand, again, there is a responsibility on the part of the reader to pay attention and stop yourself and say, Well, wait, wait a second, is this pattern really real? Because, you know, there's another saying, besides "With great power there is great responsibility," there is also the other one that says, you know, "Extraordinary claims require extraordinary evidence." And we know that cigarette consumption is not very good for you, right? So if there's a chart that sort of, like, refutes that idea that cigarette consumption is bad, that's an extraordinary claim. So you need extraordinary evidence, and that chart alone is not extraordinary evidence.
Ted Fox 26:46
Yeah. At the beginning of How Charts Lie, you quote the physician Hans Rosling, who said "The world cannot be understood without numbers. And it cannot be understood with numbers alone." And it really reminded me of, we did an episode this past spring with a doctoral candidate at MIT named Clare Kim. And she was working on the history of math, and one of the things that we talked about was, especially in the 20th century, math kind of took on this identity as, Well, it's the true truth, it's almost above all these other disciplines because it's numbers and it's dispassionate, and we can always look at this and know, whatever anyone else is saying, because I have numbers and I have this data, okay, this is the truth. When we're talking about visual arguments, when we're talking about charts, is part of the problem that we've all just been kind of conditioned to accept things presented mathematically or numerically as true, that they're somehow more official than anything else that we might look at?
Alberto Cairo 27:46
Absolutely. I mean, we consciously tend to believe that numbers are objective and therefore as an extension of that graphs and data maps are also objective, and they are not. They are not. They absolutely are not, and that idea is completely wrong. And I have tons of examples of this. I mean, I have in my previous book, in The Truthful Art, I actually have a couple of examples of how important it is to pay attention at the source and read what it is that is being measured. For example--well, I talk about this in How Charts Lie, as well, right, in the chapter about paying attention to the source of the data, right? But in The Truthful Art, I show actually more technical examples, I would say, of how to do this.
And one example is, for example, I borrowed this from a friend of mine called Heather Cross, who is a statistician, and she works in human development and international aid, and she works with NGOs. And she once told me this story about measures of intra-family violence all over the world. And she said, Well, when you pay attention to the numbers, what is the rate of, you know, violence against women, for example, in the family, those rates change, or vary widely, depending on the source that you consult. I mean, one source will tell you that the rate per 10,000 people in this country is x. And then this other source will tell you that the violence rate against women in this other country is y or x plus 100--it's like double or triple, right? And you say, Well, what is going on, how is this even possible? Well, it all depends on how violence is defined. And that is what leads to the measure of how you measure it, right? So before you can measure anything, you need to have a theory about what it is that you're going to measure, or you need to have a definition that will demarcate what it is that you're going to measure there, violence. For example, source A, which has a very low rate of violence against women in the family, probably is measuring just physical violence, but source B, right, that has a much higher rate, it may be because they are also measuring verbal violence against women. So that's why it's so important to read the source, why it is that these things vary so widely, what it is that is being measured. So numbers are not--are not--objective, they are not. They can be--I mean, they are, as Rosling said, "We cannot understand the world without numbers." But the numbers themselves alone are not enough, right? We need to go into the qualitative side of things, what it is that is being measured.
Ted Fox 30:11
Kinda as we wrap up here, my two favorite things about your book is, one, how readable it is, which I think is far from a given in a book about data visualization. It's very easy and very much directed towards kind of a layperson's understanding, which is great. And two, and maybe this sounds like a funny or obvious thing to say, but how much you do with the visuals themselves and giving an example of, Okay, here's a bad chart, see if you can figure out why it's leading you to think something that maybe is not true, here's a better way to express that. And the very first examples you use are maps of voting results from the 2016 U.S. presidential election. And I love that you point out flaws in the versions of these visual arguments that conservatives tend to favor, and in versions of these arguments that progressives tend to favor. So we're a year out from the next presidential election. What should we be thinking whenever we're presented with maps of, say, how the country is projected to vote or even when we're being reminded of, Oh, this is how everyone voted in 2016?
Alberto Cairo 31:14
Yeah. Well, again, we need to pay attention about what it is that is being represented, right? So the first map that I call out in the introduction to the book is the county-level map of election results, which basically looks like an ocean of red with tiny islands of blue here and there. So it's like, if you represent the data like that, that map alone, it's not a bad map. There's nothing wrong with that map. What is wrong with the map is the interpretations that people extract from the map. So that's a map that conservatives, particularly people who support President Trump, tend to use to say, Well, you know, take a look at this; I mean, we won sort of in a landslide, right? Take a look at how much red and how little blue. But obviously, that's a very bad representation, if you are going to use it to basically talk about the popular vote, how many people voted for each one of the candidates, just because the surface covering red is 80 percent of the country, and because the Republican vote is very spread out. And it tends to concentrate mostly on sparsely populated areas, and the Democratic vote tends to concentrate in big cities and urban areas. So that's why there's so little blue on the map. But again, the map itself is not bad, it's the interpretation, how we project what we want to believe--I want to believe that I won in a landslide, I see this map that sort of confirms what I want to believe, and therefore I use this map to say we won in a landslide, right?
Now on the other hand, people on the left tend to use bubble maps, right? And bubble maps of who won where, color blue and red--those are also, I mean, they are not bad. If you know how to read them well, those maps are not misleading. But if you have an ideological interest to confirm or to reaffirm your own beliefs, you will use those maps to do that, forgetting that maps like those--and you can see them in the book--they show you who won, only the votes for the person who won, on each county, forgetting that in Democratic counties, there were plenty of people who voted for Trump, and the other way around in the Republican counties, there were plenty of people who voted Democrat. So those maps are not good to talk about the popular vote, either. There are other ways to represent the popular vote. So one thing that I tried to make explicit in the book, or at least it permeates the entirety of the book, is that visualizations are never good or bad in the abstract--or rarely, they're rarely good or bad in the abstract. They're always good or bad in relation to what they are intended to represent. So the county-level map of election results is not intended to display popular vote; it is intended to display who won where, that's what that map is for, and it works perfectly for that. It's only that people sort of, like, repurpose it, right, like to do other things with it. That's why it's so misleading.
Ted Fox 33:50
Alberto Cairo, this has been a pleasure. Thank you for making time for the show while you're here, and it's October 15th ...
Alberto Cairo 33:56
October the 15th, yes.
Ted Fox 33:57
How Charts Lie. I really enjoyed the chance to look at it ahead of time. So thank you, and thank you for doing the show.
Alberto Cairo 34:03
Thank you again for having me.
Ted Fox 34:06
(voiceover) With a Side of Knowledge is a production of the Office of the Provost at the University of Notre Dame with support from Sorin's restaurant. Our website is provost.nd.edu/podcast. (end voiceover)