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Episode #39: Alberto Cairo
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The post Episode #39: Alberto Cairo appeared first on PolicyViz.
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Introduction and Sponsorship
00:00:00
Speaker
This episode of the PolicyViz podcast is brought to you by Juice Analytics. Juice is the company behind Juicebox, a new kind of platform for presenting data. It's a platform designed to deliver easy-to-read, interactive data applications and dashboards. Juicebox turns your valuable analyses into a story for everyday decision makers. For more information on Juicebox or to schedule a demo, visit juiceanalytics.com.
00:00:35
Speaker
Welcome back
Meet Alberto Cairo and His Book
00:00:36
Speaker
to the Policy Viz Podcast. I'm your host, John Schwabisch. I'm very excited today because I have a very special guest with me, Alberto Cairo from the University of Miami, and a variety of other things, other places, lots of activity from Alberto with his new book, The Truthful Art. Alberto, welcome to the show, friend. Hi, John. How are you? Thanks for having me. Yeah, how are things?
00:00:55
Speaker
Yeah, things are, you know, I'm doing good. Good. The book has just been published, so that's a relief. So a little bit of sleep, maybe. Well, maybe not for you. I'm moving on from there. Moving on. So this is the second book in the much anticipated trilogy.
00:01:13
Speaker
Yeah,
Comparing 'The Truthful Art' and 'The Functional Art'
00:01:14
Speaker
yeah, it's the second in the in this series is my second book in the American market But it's actually the third or the fourth one in total because prior to the functional art I did a couple of other books in just for the Spanish market But yeah, it's a second one right in the in the American market. Yes
00:01:30
Speaker
So while the functional art was sort of an introduction, I think, to the field of data visualization, maybe some best practices, and then, of course, a long section at the end on practitioners and what they actually do, this one seems to be a little more focused on the practical nature of not only designing, but actually using data, lots of sections on different types of data and distributions.
00:01:54
Speaker
and targeted towards journalists, designers. Yes, you're completely right.
Who Are Cairo's Books For?
00:02:00
Speaker
The first book, the functional art, it laid out the basics, I would say, of how to use both data visualization and infographics
00:02:11
Speaker
to for communication to communicate with the general public because as you know i have a background in journalism so my specialization is not in how to use data visualization for data analysis or data exploration although there is some there are some of that in my books it's more about how to use the graphics to to tell a story to build a narrative to make things clear to the public so the functional art was basically like an intro
00:02:36
Speaker
And then the truthful art digs a little bit deeper, as you said, into how to use data and how to present data effectively by means of charts and maps. So who the public is? I don't know. I wrote the functional art.
00:02:51
Speaker
very selfishly because I wrote the first book for my students basically. I needed a book that I could use to teach my classes and I wrote it. So my original audience for the first book was my students who usually come from
00:03:07
Speaker
a background in journalism, backgrounds in graphic design. Some of them come from science backgrounds, but it's mainly designers and journalists. That's what the original audience is. And this second book is also similar in that sense, so it's a book written with journalists and graphic designers in mind.
00:03:26
Speaker
But that doesn't mean that other people will not end up reading it. That's something that happened with the functional art. After it was published, I discovered that most of the people who were reading the book came from other backgrounds. So business analytics people, you know, people working in finance, people working in science, people who were in general wanted people who work in more technical fields.
00:03:48
Speaker
but still felt the need to be able to communicate effectively using graphics. And I believe that something similar may happen with the truthful art. Who knows? I don't know. But the
Why Journalists Need Statistical Training
00:04:00
Speaker
other thing is that I usually say that I write my books for myself many years ago. And this is even truer for this second book, for the truthful art. Because the truthful art also describes how to handle data, some elementary principles,
00:04:16
Speaker
of how to process data, handle data, analyze the data, very, very, very basic principles. And the reason why I describe them in the book is because I remember myself, seven, eight years ago, 10 years ago, I didn't know anything about statistics. And I know
00:04:33
Speaker
that this is a situation in which many people, many of my colleagues in journalism and graphic design are right. We are not trained in statistics or data analysis in school. Unfortunately, unfortunately, we are not trained. So we go then to which are working in newsrooms and we are not really prepared to handle data. That was
00:04:52
Speaker
You know, myself, I would say five, seven, eight years ago, I was not prepared. And still I needed to handle those data. I needed to present those data using charts. So the book is written in a way that I could have read it like five, seven years ago in order to avoid the many mistakes and hitting my head against the wall, suffering and sweat and I would not say blood, but I would add that to the mix that I have to go through just because I didn't know
00:05:21
Speaker
much about all this stuff, right? So it's reading in that way. It's like, you know, here's the handbook that I should have read, you know, a number of years ago in order to avoid all the many mistakes that I made in the past. Right.
Improving Data Visualization in Journalism
00:05:34
Speaker
So when it comes to journalists who are creating visualizations, would that be the piece that you think they need to improve upon the most, the understanding of statistics and how to work with data?
00:05:45
Speaker
Yes, at least at a very elementary level. So being able to explore the distribution of a data set, being able to do a very quick, take a quick look at a time series, being able to run a very simple regression, something like that. Because what I say is that in this new book is that
00:06:11
Speaker
We usually, and this, I put myself here, right? So we journalists sometimes or often receive data from a source and we run to represent the data visually and just publish something. Without, you know, thinking carefully first about what we have on our hands, without, I don't know, going to experts, to statisticians, et cetera, who can help, or mathematicians, scientists who can help us understand the data better. I think that part of the process
00:06:39
Speaker
should be planned ahead, should be part of the dynamics of creating a visualization. So creating a visualization is not just visually representing the data. It also should have a previous step, which is exploring the data, analyzing the data, taking a look at the possible stories that we can extract from the data,
00:06:59
Speaker
And then, you know, taking our assumptions or those possible stories to people who can help us understand if what we are seeing is just pure noise or if those are meaningful stories that are worth telling, right? So, and in that process,
00:07:15
Speaker
It will be really difficult for a journalist to do that on her own or on his own. We need to collaborate more and create ties with people who know a lot about data in order to represent it later on. So it's certainly part of the process. And why do you think it's not?
00:07:32
Speaker
part of the process. These in many places. So if you go sample to a media companies or organizations that are producing the best visualization nowadays, I would mention, for example, for example, ProPublica in New York, ProPublica.org.
00:07:50
Speaker
That portion, that step is built into the process of creating the visualization. So they get
Transparency in Data Journalism
00:07:56
Speaker
the huge data sets, they explore those data sets on their own, you know, they create their models, they extract possible stories from the data. Oh, this is interesting. Oh, this is interesting. They don't rush to publish right away. What they do is to take those data again, either
00:08:09
Speaker
to the original sources or to other sources that can help put the data in context or to basically compare your assumptions to the reality or to double check it again if you what you're seeing those patterns and trends that you're seeing in the data are just noise or if they are meaningful patterns that you can tell stories about right so that is built into the process and my guess is that you know the best places out there is what they do like the New York Times the Washington Post you know but I usually put ProPublica okay because
00:08:38
Speaker
it's a case that as an example because it is a case that I know quite well but I don't think that that is something that happens in smaller organizations like regional newspapers or you know small news websites because of different kinds of
00:08:54
Speaker
I mean, we, in journalism, you know, turnaround is really quickly, you need to publish really quickly. And sometimes we forget that that's a crucial step of the process. And again, I'm talking based on my own experience. I remember, you know, doing these kinds of, you know, data stories a decade ago. And I remember that in many cases, we just published stories without double checking with
00:09:14
Speaker
other sources or with experts who could help us understand it better. We just published the story. We just published the infographic, right? And that is completely wrong. That is something that it sounds like a no-brainer right now, right? I think that people here in this podcast may be asking this, how could you be so stupid?
00:09:30
Speaker
But again, we are stupid in that sense. And I recognize myself in that picture being stupid in that sense. And again, I believe that part of that step should be built into the process somehow. You know, taking the time to consult with people who can help you overcome your confidence in the data you have on your hands, basically.
00:09:51
Speaker
And what about the documentation of that work? So our journalists maybe get some data and then run some regressions and then writes a story. You see this all the time now. Oh, we did this correlation or we ran this regression. And yet there's no documentation about that.
00:10:06
Speaker
Yeah, and I think that that is completely wrong. And I mean, again, the biggest news organizations that are doing better, best visualizations and infographics, etc., they sometimes, in some cases, they often document that process and they disclose that process to the public. So 538 does that. They have a GitHub account and they disclose their data and their models.
00:10:31
Speaker
in a systematic way, ProPublica does it. And even more so, ProPublica even writes articles about how this particular project was created. But that takes a lot of time, right? ProPublica can do it just because they don't do breaking news stories, right? They produce stories that are done throughout three or four months, something like that. They have the time to put together the story and also put together some sort of article, explain the methodology behind the analysis, disclosing the data, et cetera.
00:10:59
Speaker
So one hand, a media organization that has a much faster turnaround and needs to publish really quick can do what that kind of organization can do. I would say that if you're working with public data, at least put the data at the disposal of the public. The public can download the data from your website. That will be one thing.
00:11:16
Speaker
And the second thing is, perhaps you can write just a single paragraph explaining the methods that you have used to create your visualization, the assumptions behind your data. I don't think that that takes a lot of time. And it's an exercise in transparency, in my opinion, that can improve your credibility in the public. We are being transparent.
Public Data and Methodologies: A Credibility Boost
00:11:35
Speaker
Here's the data that we are using. Here's the models that we have created. Here's the assumptions in the data. Here's how the visualization was done.
00:11:43
Speaker
Now, here's everything that we have used in case that you want to double check. I believe that if we only do that, we will increase our credibility because we will be acting a little bit more as a scientist should behave. At least this is the ideal, right, in science. If you're running a regression or if you're doing a kind of analysis in your data, if it is possible, if the data is not proprietary, which is the case most often in journalism,
00:12:09
Speaker
You put the data up there in the cloud so people can download it and then you write an article explaining what the methodology was. So I believe that more and more journalists could adopt this model or imitate it a little bit and apply it.
00:12:22
Speaker
Yeah, it's interesting. I spent a lot of time with, you know, researchers and scientists trying to get them to think more like journalists and the way that they write and the way that they visualize data. And we sort of need the same thing on the journalist side to think more like scientists when it comes to being transparent, documenting what they do. Oh, I ran a regression. Well, what regression and we do it.
00:12:40
Speaker
Again, if you go to the best newsrooms, they have version control, they document those steps. So if you are already doing that work internally, you're documenting all those steps, and you're basing your work in publicly available data, why don't you publish all that? Why don't you publish it? Just publish it. And if you're not documenting it- This is a matter of putting- Yeah, perhaps you should. Yeah, you should. Maybe you shouldn't be doing it.
00:13:04
Speaker
So I'm curious whether you think journalism is sort of the leader in the data visualization field at this point. Obviously we have an academic wing, we have practitioners, and we have freelancers, and we also have journalists. Do you think journalists are sort of the leading edge of who's creating and moving the field ahead?
00:13:22
Speaker
Well,
Journalists Leading in Data Visualization
00:13:23
Speaker
certainly, as you know, visualization has many different branches. We can use visualization for exploration, we can use visualization for communication. I don't think that journalists are leading the pack in terms of visualization for data exploration or data discovery, but certainly, you know, the best visual journalists out there are leading the pack in terms of using visualization to communicate with the public.
00:13:43
Speaker
So the most creative people, designers in visualization nowadays, are working for news organizations, are working for places like again ProPublica, the New York Times, NPR, I don't know, the Washington Post, the LA Times, which is also producing
00:13:59
Speaker
amazing visualization work, and they are doing very innovative stuff like, you know, trying new technologies. The other day, for example, I was attending the Malofia Infographics Conference in Spain, and there were a couple of talks about using virtual reality in visualization and infographics. One of them was by Len de Groot, who is the head of data and visualization at the LA Times. And he was talking about the technologies that they are using, in many cases, in a very experimental way.
00:14:28
Speaker
to push the field forward, right? Do you just try new things? Try and fail, basically. See if it works, see if it doesn't work. And then if it doesn't work, try again, right? So, yeah, there's a lot of innovative people in journalism nowadays, and that's extremely exciting. Yeah. Do you think that the quick turnaround in journalism may damper sort of the innovation that people can try because they have to get stuff out? Or is it good that there is quick turnaround and that's what helps them drive to some other things?
00:14:54
Speaker
Again, the best organizations out there have like two different production trucks, right? So first of all, they have the, you know, the projects that need to be done very quickly and very published very, very quickly. But then on the side, you know, many people or most people in these departments also have projects that are more long term and they can focus more on that. Right.
00:15:13
Speaker
And then the other thing is that the fact that journalism has such a quick turnaround also forces some of these departments to develop tools that can enable them to produce faster, right? So, for example, the case of the New York Times has produced tools that they have open sourced, such as, for example, there's one called AI to HTML.
00:15:34
Speaker
that was produced by the graphics desk at the New Times that allows you to transform an Adobe Illustrator file into an HTML file. So it transforms the text, the Illustrator text into HTML text. And then they make that tool available for anybody. They open source it. But I believe that they developed that tool just because they needed that. They needed that to produce quicker, to produce faster.
00:16:00
Speaker
But then at the end, it ends up benefiting everybody else in the industry, right? So it's all related, I guess. Yeah, absolutely. So let's talk a little bit about some of the common debates in the field. And I wonder whether you think the common debates
00:16:16
Speaker
we should stop debating them. So things like our pie chart's good or truncated Y axis. I mean, rainbow color palette's like, should we just get over it and just run? No, all right. So the truncated Y axis in bar charts, we should not stop debating them. I mean, we need to stop debating them because it is obvious that truncating the Y axis on a bar chart is wrong. Okay, good. We've settled that. Of course, we've settled that.
00:16:41
Speaker
So we should stop debating it just because it is a subtle debate, that particular one. But what we should not do is to stop pointing out cases in which y-axis that are truncated are used to mislead the public, for example.
00:16:57
Speaker
I think that to the extent of our skills and to the extent of our capability is whenever we see an example of graphic that has been obviously created to mislead the public, we should call it out, basically.
00:17:12
Speaker
So rainbow color palettes, for example, there's some evidence that shows that rainbow color palettes can be leaning, right? So whenever we see a colleague in science using a rainbow color palette on a map, perhaps we should gently point out, well, you're showing continuous data, so why don't you use a continuous color scheme, right? You can do that nicely. It's not a criticism.
00:17:36
Speaker
helping people do better work. Right. So I think that, yeah, we could we the debate is perhaps over in some of these cases. But what is not over is the educational side of these things. Right. And the education doesn't mean forcing people to do things, is to suggest things to people. Perhaps you may want to do and then show the evidence. Right. Show the rainbow color palette in comparison to a continuous color scheme and asking people, well, do you see the data better here or here?
00:18:04
Speaker
right, and make them compare. Now, there are many possible debates that are not over, right? So, the role of, you know, narrative, the structures, and things like that, that's still an ongoing debate, and I believe that is super healthy and very enlightening for everybody. So, no, we should not stop debate. All right. I want to come back to that one, but I like the way you said gentle critique.
00:18:25
Speaker
Have you seen the – and you are one to come in and you do it a lot in the book about pointing out some visualizations that aggregate the data in some dishonest – I mean, I would argue a dishonest way, showing data in arguably dishonest ways.
00:18:41
Speaker
Have you observed a change in how the field does critique and approaches critique? And do you think there's room for the field to move in a different direction or a better direction? Yeah,
The Role of Constructive Critique in Visualization
00:18:56
Speaker
so first of all, I think that critique
00:19:00
Speaker
has a role in visualization, as in any other field. And if it is done in a constructive way, obviously, rather than saying this graphic is crap, perhaps. And there's a very good article by Fernanda Viegas and Martin Wattenberg that points out, in my opinion, in a very good way, how to actually do a critique of a graphic.
00:19:21
Speaker
And, you know, I think that there is room to do critiques in a nice way, right? Being gentle and saying, you know, point out why you believe that that visualization is wrong, or at least, you know, could be improved. And then point out possible ways in which they can be improved.
00:19:41
Speaker
always making clear that you're not trying to settle the debate. You're just saying, this is what I think. And then if you want to take this advice, take it great. If you don't want to take it, well, perfect. I would not be offended if you don't take it, right? So I think that that's the spirit that we can adopt. And yeah, I see that feel moving in that direction. So most critiques that I have read recently
00:20:05
Speaker
Are they are very nice and very constructive and and i believe that that's it all that we should follow rather than being distracted. Yeah in the past we had you know many critiques i would say too many critics that were extremely harsh or or destructive right right.
00:20:24
Speaker
Yeah, I think one good example is the Makeover Monday project that, you know, Andy Kreebel... Yeah, I was actually thinking about the name of that project by Andy. I think that that's a great way of doing it, right? So you showed the before and the after. Do you recognize the effort of the original author? That's another thing, right? Saying, well, this is great. You know, point out also the good things about the graphics that you are critiquing, you know. And then, again, making clear that
00:20:53
Speaker
Visualization has certain rules, but those rules are flexible. In some cases, they depend on context. In some cases, the critique that you write, or in most cases, the critique that you write, is your opinion. It may be based on what you know about how visual perception works, but you always need to keep the door open
00:21:18
Speaker
to the fact that you may be wrong in the critique that you're right. I somehow suggest that this is just my opinion. Again, this is my advice if you want to take it great, if you don't want to take it great as well. One of the things that's interesting about that project and critique in general is some people's take on remaking a graph that may be sort of a non-standard type and transferring it into a standard line chart or column chart.
00:21:46
Speaker
And there's always this tension, right? And you talk about it a lot in both books, right? This tension between aesthetics and beauty versus being able to sort of, you know, get the, you know, make a comparison. Effectively, et cetera. Yeah. Well, that's what I was saying before that visualization has certain rules, but the rules are extremely flexible and context dependent, right?
00:22:07
Speaker
There's a very gray area in there. So if you are going to create your visualization for analysis and quick exploration, it is obvious that, at least for me, it is obvious that you need to stick to graphic forms that, throughout the years, have improved to be effective at letting you see patterns and trends in certain kinds of data. So scatter plots, line charts, bar charts, et cetera, right?
00:22:29
Speaker
Now, if you are doing visualizations to communicate with the public, there is another level that you need to consider, which is the visual appeal of the graphic, like the, I just said, the aesthetics. I want to call it the aesthetics, but I will call it like the visual appeal, right? The attractiveness of the graphic, right? The fun, the fun side of it. That's a very important component as well. So in some cases, you may need to sacrifice part of the
00:22:53
Speaker
let's say the effectiveness or perhaps the precision in the way that the data is represented in order to gain a little bit more of engagement, making the graphic more attractive to the public. That doesn't mean that you need to destroy precision outright, obviously. But you may need to sacrifice a little bit of clarity in order to make the graphic more visually appealing, more attractive.
00:23:14
Speaker
That may happen in some cases, and I point out several examples in the book, right? That graphics that, from the point of view of analysis, from the point of view of a statistician, they will be like a scene. They will say, well, this is completely wrong. But from the point of view of communicating with the public, they may be very effective graphics. They may be graphics that will attract people's attention to interesting facts, right?
00:23:36
Speaker
Right. Another thing you talk about in the book on the same vein is the balance between simplifying and oversimplifying. Yeah. You have some data set. Where do you draw the line there in your own work or the people you work with where you're going from? You have some complex data set, you've simplified it, and then there's some line that you cross where you've sort of oversimplified it.
00:23:55
Speaker
Yeah, well, in the book I say, and it's what I say usually, is that there is not really a line. It's a
Simplification vs. Complexity in Data Storytelling
00:24:02
Speaker
very subjective decision. And any statistician will tell you this. I think that there is not really an objective line that you can trace saying, if you go beyond this line, this is wrong. If you stay on the other side of the line, it is right.
00:24:15
Speaker
What you need to think about, and this is something that will sound very, very obvious to scientists and statisticians, is that any data set can be represented at multiple levels of abstraction, at multiple levels of complexity. So the simplest way in which you can summarize a data set is calculating averages, right? But then after the average, any kind of average, the mode, the mean, or whatever, you can also calculate the range. You can also calculate the shape of the data. So there's like multiple levels of depth at which you can analyze the data. Well, there is also multiple levels of depth.
00:24:45
Speaker
that you can represent the data or present those data to the public. What you need to ask yourself very openly and very honestly when you are working with those data is which one of these levels of complexity better represents the true story that I'm trying to tell, right? What is more truthful, right? And then, you know, make that estimation, right? Make that conjecture and then apply it, apply it to your work. And the reason why I say this is because in the world of journalism, and again,
00:25:14
Speaker
This is something that I have done myself in the past, right? We tend to stick too much to the simplest level of representation, which is the average, for example, to represent a dataset, or the averages. We forget that those averages may not represent the true shape of the data.
00:25:32
Speaker
So if all your data points are clustered around that average, around that mean, it's appropriate to just report the mean. But if your data is very spread out or it has a bimodal distribution or whatever, the average will not be a good representation of the data. You actually need to show the distribution of the data in order to show that it is bimodal. This sounds very obvious for the statisticians, I guess, but it's not so obvious in the world that I lived in in the past.
00:26:02
Speaker
All right, so let's close up by asking some forward looking questions. So where do you see data visualization heading in the next few years?
Future Trends in Data Visualization
00:26:11
Speaker
And where are you heading in the next few? Well, for you the next few days, but really the next few weeks and months. I don't know.
00:26:18
Speaker
I don't know. I'm not good at making predictions. But I think that we will see more video. I think that there is a lot of potential in using video to tell stories visually. Video and linear video, traditional linear video, but also interactive video. And then virtual reality. Really the talks that I saw at Malofie recently about virtual reality.
00:26:38
Speaker
apply to visual communication really opened my eyes to the possibilities of using those technologies in the future. So I think that we can head in that direction. The other thing is that we should consider that today most people don't consume our graphics through a computer screen.
00:26:58
Speaker
Today, I am at Univision, the Spanish-speaking television station here. I am in the newsroom here. I come here every week. And it's all mobile first. And why is it all mobile first? Because 80% of the access they have to their website is not through a computer screen. They are through a mobile telephone screen, 80%.
00:27:21
Speaker
So that forces you to adopt a mobile-first strategy. You first of all think about how to represent your data on a small screen. And later on, if you have time, then you think about how to show it on a computer screen. So that's another trend that is very obvious, not for the future, but in the present. Yeah, absolutely. And the crucial thing and the challenge for the future is, and again, I don't have any answer,
00:27:44
Speaker
Yeah, you're right. Over how to meet these challenges. But the challenge is how to tell the story without oversimplifying the story in such a smaller space, right? How to reproduce the richness of a complex interactive data visualization in a computer screen, how to reproduce that in such a smaller screen. How can you do that? I don't know how to do that. Obviously, you can make your story tall and scrollable. So you scroll, scroll, scroll. But there may be other ways of showing the same amount of data and the same amount of depth.
00:28:14
Speaker
Yeah, right in the past, you know, I had Shaqeen Villegas from Guardian on the show a few weeks ago. We were talking about one of the projects that they did on the Mekong River where the display that was shown on a desktop was very different from the version that showed up on the mobile because you scrolled in different ways.
00:28:30
Speaker
There was another very interesting talk in the Malofia Infographics Conference last week by Archit Se from the New York Times, and he was outlining some of the 2016 rules for data visualization for journalism, right? And one of them is less interaction, basically.
00:28:47
Speaker
And you can see that if you visit the New York Times regularly, you will see that their graphics have become less and less interactive. And that is because he says, you know, people don't really... Most people don't really interact with the graphic by clicking here or hovering over there. They just scroll. They scroll the graphic. So they try to show everything, you know, at the first level, right? So people can just, you know, explore it, scrolling the story.
00:29:11
Speaker
Interesting. Those are not really trends for the future. They are the present. Yeah, they're happening now. It's already happening, right? So where will these technologies lead us in the future? I have no idea. But it's exciting to see these things being used and being applied, being adopted.
00:29:28
Speaker
And so what about you? You're at Univision, you are at University of Miami. I keep teaching and I come to Univision once a week to collaborate with visualizations here and it's a lot of fun. You know, I keep doing some freelancing work, doing some graphics on my own and with some colleagues with some here in Miami, I don't know, writing a new book, you know, I keep writing.
00:29:56
Speaker
I'm going to write another one. It's exciting. And last I heard you were going to redo the website, maybe open a formal studio, or are you still planning on going that direction?
00:30:10
Speaker
Yeah so throughout the years I have been doing some consulting work and some freelancing for some companies but I did it mostly on the side and I didn't promote it that much throughout the years either on my website or anywhere else and I decided that
00:30:27
Speaker
Perhaps it could do it. I could just read a you know, a corporate website or something and and formally say yes Yeah, I I am here. I do this kind of work and you know want to bring me in I will be happy to help you with your project So yeah, I'm planning to do that in the next month or a couple of months They actually launch a corporate website, which will be albertrochiro.com basically. It will be the website. Yeah
00:30:50
Speaker
Well, we'll all look forward to that. And of course, we're all rooting for you because, you know, we all have our, well, at least I have an Amazon wish list. It's just based on Alberto Cairo's recommendation. Well, I read less and less every day, though. Because you're writing more and more. So, you know, there's always somebody out. I guess I'm getting older and I want to, you know, take some, you know, time off. Alberto, thanks so much for coming on the show. This has been really great. Well, thank you, John. Thank you.
00:31:18
Speaker
And thanks to everyone for listening. So until next time, this has been the Policy Viz Podcast.
00:31:35
Speaker
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