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The Financial Times' John Burn-Murdoch talks the Annotation Layer
229 Plays5 years ago
Part of the FT’s interactive news team, John-Burn Murdoch works as a journalist alongside developers and designers to produce a mix of long term data-driven projects and same day interactive news stories. Other activities include presenting to domestic and overseas...
The post Episode #155: John Burn-Murdoch appeared first on PolicyViz.
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Transcript
Introduction to Animation in Data Visualization
00:00:11
Speaker
Welcome back to the Policy Viz Podcast. I'm your host, John Schwabisch. On this week's episode, we are going to talk about animation and data visualization and the advantages and disadvantages of taking data visualizations and animating them as things transition from one state to another.
Guest Introduction: John Byrne Murdoch
00:00:28
Speaker
And to help me talk about that, I've invited John Byrne Murdoch from the Financial Times to join me on the show. John recently created a bar chart race as it's become
00:00:38
Speaker
fondly called that showed changes in populations of different cities as they change over time.
00:00:45
Speaker
And that bar chart race got a lot of attention.
Upcoming Workshops and Support Opportunities
00:00:49
Speaker
And so John and I sat down and talked about that project that he created, why he created it, and all the other ways in which animation can be really beneficial to presenting and communicating data. And in fact, I am about to give a talk on animating data visualization tomorrow night here in Washington, DC, as part of the Sage Ocean Speaker Series.
00:01:11
Speaker
and I'm really excited to be a part of it and to help kick off the Washington DC series and hopefully later this fall I will be in London to give a talk at the London office of SAGE so I'm looking forward to that as well.
00:01:24
Speaker
So before I get into the show, just a couple of quick notes. If you're interested in supporting the show, please do share it, tell your friends, tell your family. If you'd like to financially support the show to help me cover transcription costs and audio editing costs, please check out my Patreon page. I have a number of different new platforms that you can use to support the show, all the way from $1 a month to $3 a month, $5, $10, and even more. If you're so willing, I'd really appreciate it.
00:01:53
Speaker
And if you're interested in learning more about data visualization, check out my workshops page. Later this fall, I will be conducting a bunch of public workshops in September. Excuse me. I'll be teaming up with Stephanie Posovic.
00:02:06
Speaker
one of the members of the Dear Data team, and we'll be conducting our Dear Data series. We're going to take it on a road show. We're going to start in New York, I think. We're going to head over to Philly, and then we're going to end up here in DC. And then in October, I'm teaming up with a friend of mine here in DC to teach a data visualization in our workshop. And then finally, later in the fall, I will be in Amsterdam to teach a data visualization and Excel workshop.
00:02:34
Speaker
for a full day with my friends over there at Graphic Hunters. So if you're interested in attending any of those sessions and workshops, please do check out the public workshops page on my website. And if you have any questions or you want me to come and give a workshop to your organization, please feel free to reach out and let me know.
Impact of Animated Bar Chart Races
00:02:53
Speaker
So, but back to the podcast on this week's episode, I'm talking with John Byrne Murdock from the Financial Times about animating data. And here's that interview.
00:03:06
Speaker
Hey JBM, how are you doing? I'm good, how are you? I'm good, I'm good. I like this JBM moniker I can call you. I feel like... I'm really at the differentiates between the two Johns, I guess.
00:03:19
Speaker
It does a little bit. It does. How are you? You just got back from vacation? I did. Yeah. I was over in Italy for a few days, which was fantastic. Yeah. It was a real joy to be out there. So, yeah. Lovely. You know, Stephanie Postovic and I have done these workshops and she's originally from the United States, from Colorado.
00:03:38
Speaker
and she's in london and we're gonna meet in chicago for something like a year ago and i said oh yeah she was gonna be there her husband's family's out there and i said there's no problem i'll just fly there it's like two and a half hours flight no big deal she said two and a half hours that's like from london to turkey so when you're like i just went to italy you know yeah for whatever that's like me going to delaware so yeah it's the same ring but yeah
00:04:00
Speaker
I want to chat with you on the show because you hit I think a nerve with your bar chart races. And people really responded well to them and then Flourish came out with their own, the ability to really drop and drag and make something about them. So I was hoping maybe you could talk a little bit about why you built it and that process. And then we can talk about animation sort of more generally and your thoughts on the value of animating database. Sure. Awesome.
00:04:26
Speaker
Yeah. So this was an interesting one. So the one that I put out there, which I think seemed to get a lot of traction was, I think it was March 18th or 19th that I put that one out. And that was because in the couple of weeks leading up to that, I'd just been seeing, as I think a lot of people, both within and outside the database community had been seeing a lot of these animated bar charts, bar chart races, whatever we want to call them, doing the rounds on social media.
00:04:56
Speaker
I think one of the first ones that really seems to sort of come out of nowhere was this one showing the estimated values of the biggest brands in the world and how they changed over the last 20 or so years. I spoke to Matthew Navarro who'd shared that one on Twitter. I spoke to him earlier today and we're trying to work out where that one had originated and he wasn't exactly sure either so he just picked it up off.
00:05:23
Speaker
YouTube or something and then put it out. So there'd been that one and then there was one showing the number of goals that different soccer players had scored when they were all at a certain age. And these just seemed to be coming out of nowhere and coming out of different corners of the internet, different software, different
00:05:42
Speaker
stylistic touches, but the format just seemed to be really catching fire. There was huge, huge traction around these. And it just got me thinking, I guess I was thinking about it from two different perspectives. One was just the simple like, oh, I want to know, could I make one of these in the various toolkits that I'm aware of?
00:06:06
Speaker
And the second was more of a fundamental like, you know, is there really something about this format which makes these graphics really resonate?
Engagement Through Animation: Speculation and Curiosity
00:06:14
Speaker
And so I wanted to sort of find that out for myself. And so step one for me was to take the global brand values one and actually simply recreate that one in D3. So using observable, they're like the coach sharing platform.
00:06:33
Speaker
because I already had the data for that one and I thought, well, this will just be a proof of concept, see if I can get the old D3 transitions up and running again and make sure it all works. And so I did that fairly quickly and thought, okay, cool, this works. And then I thought, all right, what would be another interesting dataset to use in this format to create something of my own? And initially I was thinking I'll do sort of
00:06:59
Speaker
the size of different national economies and what have been the top 10 economies over time. So I started looking with that data, but yeah, that seemed fine. That seemed interesting. But then I stumbled across this incredible dataset of populations of pretty much every city that there has been on earth going back thousands of years.
00:07:19
Speaker
And I just thought, so you've got, first of all, the data just fits very nicely. It's a data set where you've got lots of different entities and each of them has a value that changes over time.
00:07:31
Speaker
very basic requirement for what I guess we should keep calling a bar chart race. The magnitudes of these numbers change a lot, so you go from populations of thousands to hundreds of thousands to tens of millions. The other thing I really liked about this, I felt it was just an inherently
00:07:49
Speaker
sort of curiosity peaking data set. Everyone's fascinated by which the largest cities in the world are. I think plenty of people probably couldn't even tell you the largest city in the world today, but certainly when you throw that back 500, 600 years, it gets just really, really fascinating. And so as soon as I started
00:08:09
Speaker
digging into the data, I thought, yeah, this is going to be the one. So yeah, I then plugged that data set into the same observable template that I built for the regional brands one. And I added a few additional bells and whistles. I think that was when I put on a world map.
00:08:26
Speaker
Which sort of served as a color legend for the for the cause of the bars so i did each continent a different color and allow me to sort of have these little things on the map whenever a new city entered the top ten and yeah so that was that was pretty much that i did the whole of that so.
00:08:45
Speaker
from coming up with the idea to making the brands example and the city example, that was essentially just playing around over a weekend. Then I think I set them loose as it were on Twitter on the Monday morning and I posted to Reddit as well. Then pretty much just sat back and my phone proceeded to melt.
00:09:07
Speaker
as all the notifications came in. So that was essentially it. It was a little weekend side project. And I guess for me, it really demonstrated that when you've got the right data to use in that format, these things, you know, people are just absolutely crazy for them.
00:09:25
Speaker
Yeah, I'm curious what you and I had a quick conversation about it after effort came out and I was thinking, well, you could just do a bump chart with this. But then, you know, when you have these, like you said, you have a lot of data over a lot of years, you kind of get this spaghetti looking thing, but
00:09:41
Speaker
I'm curious what you think about this. You have to sit and watch this thing for two minutes. It's not the immediate takeaway that you would get from a line chart or a bar chart. And I'm
Choosing Suitable Data Sets for Animation
00:09:52
Speaker
curious what you think strikes people that we're in a fast content world and yet they're willing to spend two, two and a half minutes watching these bar charts move around and just grow and move up and down. Why are people willing to spend two, two and a half minutes watching these things?
00:10:07
Speaker
Yeah, it's really interesting. That probably strikes us as the most counterintuitive thing because, yeah, we're in a world today and especially on social media where attention span is about four seconds. So, yeah, the idea that someone would sit back and watch is crazy. And so, I jotted down a few of the responses it got and
00:10:27
Speaker
one person said exactly this. They said, I can't put my finger on why these are so fascinating. You've got to watch for far longer than it would take to look at a line graph. So why is this so engaging? But then someone responded to them saying that the fact that you've got this time to speculate on the changes that you're seeing is for them the best part of this whole format. So
00:10:49
Speaker
the graph itself, the chart itself isn't answering the whys, but it's allowing your mind to think, ah, wait, so this thing just went up, that thing went down, I wonder what was happening there. And the fact that it essentially keeps you sitting in front of it thinking about what you're seeing, that is essentially a luxury in the database world. We all want to think that someone is sitting down with our charts for at least a few seconds to think about what they're showing. But
00:11:15
Speaker
very often with a static chart someone will have a quick look and if the if the overall message comes out clearly you know if it's which is a good thing then they may well just say okay got it and move on whereas with a chart like this the fact that you have to sit there and watch
00:11:32
Speaker
It does seem, you know, it makes sense to me, but it also seems from people saying this without being prompted that that act of sitting there watching does give you that time to really immerse yourself in the subject matter and think about what you're seeing, which you might not otherwise get. And then someone else said they wonder if the format encourages the analytical and questioning behaviors in a viewer that a scientist or a data analyst might do instinctively with a static graph.
00:12:00
Speaker
but maybe within an audience of people who aren't familiar with the idea of reading a chart and, you know, consuming it as a piece of information, with that audience, the animation essentially is what holds them captive and gets them thinking more inquisitively. So I think both of those are really interesting points about why the fact that you're spending time with it ends up being a strength rather than a weakness. Separately, there's this broader question of
00:12:30
Speaker
You know should these not simply be line charts all the time so you have tableau fame about other the other so how she was and he and dandy kirk did it had a great little debate and about this at a at a london tableau meet up a month or so back where.
00:12:51
Speaker
they were each just given once they were either had to be pro or again bar chart races and Andy Kirk was in defense of and Andy Cockery was he used a great line I think actually which is he said bar chart races are like the fidget spinners of data bits
00:13:07
Speaker
Right. These things that everyone for a while goes crazy about and spends a lot of time with, and then they end up being a fad. There's an interesting argument to be made there, and only time will tell as to whether these just end up being a passing fad. But there were a few things that came up in that debate, which I think were interesting points, but that got me thinking more about what it is that makes
00:13:28
Speaker
these work and you know cases when they do work well and cases when they don't work as well and I think I've kind of distilled it to three sort of ingredients as it were in a data set and the story you want to tell that are really well suited to this format. If one or more of those ingredients are in place then this could be the perfect way to tell the story and if they're not then you know maybe you should just be a line chart and so one thing I think is
00:13:55
Speaker
you should really be using these when the values in your data set are changing hugely over time, when you've got orders of magnitude and exponential growth. So a lot of people reacted to the one that I first put out showing city populations and said, yeah, why not just make this a line chart? They wanted to be able to trace, for example, the population of Cairo or New Yorker of London over time, rather than having it on screen one second and not the next.
00:14:25
Speaker
But the problem you would go into immediately with that is the populations of these cities are just vastly, vastly different from one period to the next. So when the time series starts in the year 1500,
00:14:43
Speaker
the largest city in the world as a population of 672,000. Today, 38 million. If you had a standard line chart going from 0 to 38 million, then in 1,500, that's 600,000, you're not going to be able to see it at all. You're certainly not going to be able to differentiate between the largest city on 672,000 and the second on 500,000.
00:15:09
Speaker
straight away, you have an issue there. And some people then counter and say, okay, but what if you had a logarithmic scale? First of all, you sort of have to have that logarithmic scale changing over time. But even then, the tiny differences between a city with 200,000 and 185,000 population, again, don't really come through. So for me, one of the huge strengths of the bar chart race
00:15:35
Speaker
where you are essentially saying that your horizontal numeric axis is constantly clamped at the highest value in the dataset. The huge advantage is that you get to view every year, whether it's 1500 or 2018, you get to view every snapshot of your data as if that's all that matters.
00:15:55
Speaker
as if you were alive at that time and that's what you're looking at. It allows you to constantly view this data to essentially see the most important points of the data as they were at every different point in time instead of having to view
00:16:11
Speaker
everything from the perspective of what's important today, which is tens of millions. So one thing I think, when you've got data set, when you've got a time series that changes by several orders of magnitude, I think this is perfect. Another thing, and this is one of a couple of points where I'm probably going to make analogies to movies and TV, I think these work really well when you've got a changing path of characters.
Global Recognition and Engagement with Animated Visuals
00:16:40
Speaker
when certain things drop in and out of those rankings over time. So again, with a line chart, the problem you might have is that in order to show every city that has been at a certain point in time in the 10 most popular cities in the world,
00:16:58
Speaker
you would have to have 100 lines on that chart. So that, again, is not going to be particularly readable. Whereas if you want to be able to show 10 cities in 1500, but then a different 10 cities in 1600, a different 10 in 1800, et cetera, this format, again, works really well. So if your points of interest, or as I'm saying, your characters,
00:17:20
Speaker
are sort of going on and off stage and changing then animation is really the only way to do that while still having a sort of accessible number of data points on there. And then the third thing that I think works really well is when your audience really can get on board with the idea quite naturally, get on board with the idea that they're watching some kind of race unfold.
00:17:42
Speaker
anything where it's ranking. So like the original one that piqued my interest, which was the most valuable brands in the world, or in this case, the largest city in the world. If you can give someone the idea that who is at the top matters in some way, then I think that just gives that
00:17:59
Speaker
you get a little bit of emotional investment, the idea that you're watching a race. And then I just feel like, you know, people are so conditioned to watching races unfold, whether it's, you know, watching the Olympics every four years or whatever, that we just this is a format that we just are familiar with. And so you get that tension, that suspense. And this is another thing that several people commented on was that you really do feel a sense of excitement because you don't know what's coming around the corner. And all of those things together. And I realize, I mean,
00:18:28
Speaker
A lot of what I've been talking about here is more about the pure sort of emotional reaction that someone has to these rather than, you know, I'm not talking purely about perception and the usual things we talk about when evaluating data. I'm sure we'll come onto that. But just in terms of people seeing this and just being completely immersed in it for the two minutes or so it takes to watch, I think
00:18:54
Speaker
When you have those things, so changing orders of magnitude, changing data points of interest, and this idea that you're watching some kind of race unfold, I really do think this is the best way of showing that data. And I don't think that that is something which is only true now because it's a new thing we're seeing. I think that's just a fundamental, just a fact when you're displaying this kind of data.
00:19:18
Speaker
I totally agree with all that. And I think there's on this point of getting behind it, there's this other part of similar to a map, at least the one that you did, at least the one on populations, this holds for a lot of the other ones. It also allows you like a map to place yourself in the context of
00:19:39
Speaker
the data. So for example, when I'm watching it, you know, growing up in America, you know, I'm looking for, you see New York show up, and then you know, you say, Oh, that's when the country started, right, you can see it grow, you can see different cities in America, you can see San Francisco show up and then sort of fade off. And like you said, you can imagine those stories, and you can identify with your particular area of the world. I'm sure other people and other
00:20:05
Speaker
continents and countries have the same experience of watching, you know, understanding the history of their own country and seeing the cities rise and fall of where they live and where they are. So I think there's that personal connection with these sorts of things that works well too.
00:20:19
Speaker
Yeah, 100%. And I guess that's, I think it is important to talk here about how, yes, this particular visual format seems to perform really well, but I think a lot of that is about what's being shown. So, you know, the original one showing brands, people could look at this and think, oh, like IBM, yeah, I forgot they were so big, and then I'll look at Amazon coming up now. People can recognize the entities there. And it's exactly the same here. I think the fact that this was looking at global cities,
00:20:46
Speaker
mentions that people from all over the world who even those who can't necessarily read English, they're still probably going to recognize the English spelling of the city they come from or cities in their country. I've seen people have taken this video and made their own Facebook posts in more than a dozen languages that I've seen from various
00:21:10
Speaker
Indian scripts to Japanese and Chinese. And so I think, yeah, the two, the dual things of number one, long time series, so people get that curiosity that you get when you're discovering about the past. And number two, all of these recognisable places on the planet, I think, yeah, those were really, really helpful as sort of hooks for people to get into the story, as it were. I think if this were the same format and even the same
00:21:36
Speaker
numerical values, but looking at something like, you know, the most, I don't know, the most prescribed medical products over time or something, you know, outside of the pharmaceutical industry, I suspect people might be a lot less engaged with it. So the subject matter, I think definitely is part of the reason some of these have been really popular. But yeah, I think when you put it all together with this format as well, then this version of this data is is probably just going to be more engaging than a static one.
00:22:03
Speaker
Right?
Enhancing Understanding with Annotations and Pacing
00:22:04
Speaker
Now, I sort of came to know and love your work because of what I view as sort of expert way of using annotation on your graphs. But a lot of those are static. And so I wonder how you and maybe the rest of the staff there feel about or approach animations sort of more generally. We've been talking sort of specifically about the bar chart race, but there's lots of types of animation, obviously. And so I just wonder what your experiences and what your thoughts are on data vis animation, which
00:22:33
Speaker
is just a totally different, I mean, maybe not a totally different thing, but it's certainly a different way to approach communicating data to people.
00:22:41
Speaker
Yeah, it's a good question because, again, just just staying with this, the current the city's example for a second, I think one of the strengths of it almost is the fact that there isn't a commentary over it. And that allows individual people to focus on whichever part of it resonates most with them. So we did at the FT, we did do a second cut of this with a voiceover from me on there as well. And I've done a couple of
00:23:08
Speaker
live performances of it, as it were, with commentaries. And I think those have been great for other reasons. It adds even more of this sort of sense of
00:23:17
Speaker
of suspense and excitement when you've got a commentary over the top. But I think what you lose there in this case is people are then probably going to be focusing on whatever I or another narrator is highlighting, whereas with no commentary, they're free to look at whatever bit they think is interesting to them at any given time.
00:23:39
Speaker
So, that's just on this specific example. More generally, I and my colleagues here at the FT, we really do think one of the most valuable things we can do as data visualization practitioners is this expert annotation layer. And so, with these kind of animations when you're just seeing
00:23:59
Speaker
sort of a data story unfold, as it were, I think voiceovers are absolutely one way to go. And so I know that Flourish, who, as we discussed, have put out their own template for this as well. I know they have the, in their toolkit, you can add voiceovers to animations. You can do that
00:24:19
Speaker
using text on the screen as well instead of obviously doing it through audio. So a subsequent animated piece that we did here, maybe about a month after this one, was looking at the times of day that different people around Europe eat meals. And for that one, I added a bit to the observable notebook that I created so that I could pause the animation and put up some short text annotations on screen, which I think helped.
00:24:47
Speaker
more broadly that's something that we've been doing in our animations for the last couple of years so several of my colleagues here will often do animated pieces where you're essentially taking one static chart or map and just showing a couple of keyframes which really sort of highlight the story that is being told there and you know in some cases they will be showing something that did change over time so we might have a map
00:25:15
Speaker
showing, for example, some military activity in Syria, and frame one will show how things were in, say, January, and then frame two will highlight an event happening in February. In other cases, we're taking one chart that doesn't actually change, but just
00:25:32
Speaker
putting up sequential annotations to guide someone through the chart. And I guess the two things we tend to focus on there is, number one, make sure the amount of text that is in any given annotation on an animation is short. So you're not asking someone to sort of stare into the screen and read a couple of paragraphs, but you're just putting, say, at maximum of a dozen or so words up that they're going to look at for a second or two.
00:26:01
Speaker
and also just to make sure you get the pacing right. So I don't think we have a hard and fast rule on this, but in one of these animations, so take the map example again, where we're going to be showing, say, four or five events and putting up a sentence on each one, showing what happened while pointing to a certain part of the map. I think we generally go for about a couple of seconds pause when each of those annotations comes up, because the difficulty really is
00:26:30
Speaker
as soon as you have a you transition away from an annotation before someone's read it, that's just such a frustrating user experience. Yeah. And in all likelihood, someone's not going to bother with sort of dragging the video slider back, they're just going to click away. And especially, you know, when somebody's a gift, rather than videos, and you can't you don't even have the ability to
00:26:53
Speaker
to scroll, to scroll. You don't have any controls, the user rate. Right, right. So yeah, I think it's with animation, you've really got to design for someone who isn't going to scrub across the video. Even though they can, you've got to design for someone who just wants to sit back and make sure that you get the timings right so that they really can take in everything without feeling that they've been interrupted, essentially.
00:27:20
Speaker
So what do you think it is about the animation that makes it...
00:27:25
Speaker
popular? Is it that it's more passive? You know, you receive any information in sort of a more passive way, you don't actually have to examine the chart in the same way, because you're adding this annotation, you're telling the story?
Comparing Animations with Static Visuals
00:27:40
Speaker
Is it because it's like a movie or video, which people might sort of instinctively associate with watching a movie or watching a television show? Like, what is it? Do you think about the animation?
00:27:51
Speaker
that gets people to stick around for longer than they might with the same graph or even a series of graphs that are more static. Sure. I think there's bits of all of that and a few other things in here as well. First of all, just in terms of the initial task of getting the audience's attention, as we discussed at the beginning, people are famously short of attention these days and that's no more the case than it is on
00:28:20
Speaker
social media, which is where a lot of data visualization is consumed. And so I think the fact that movement is a very, very effective way of drawing someone's eye is probably a big part of just getting the initial eyeballs on the graphic. And that's step one to producing an effective piece of data visualization. If people, you can produce, as Hans Rosling said in an interview with the Financial Times a few years ago,
00:28:49
Speaker
And if you can produce the best piece of work ever, but if people don't actually pay any attention, then as a visual communicator, you failed. So I think the fact that movement, that motion really draws people's eyes, is the first part. And one of the papers I was looking at when I wanted to dig some more into this, it's a paper called Enhancing Visualizations with Motion.
00:29:13
Speaker
And that is by, let me just see if I've still got that on screen. But if not, anyway, moving on. It's from a paper called Enhancing Visualizations with Motion. And the researchers there, the researcher found that
00:29:32
Speaker
When comparing different colors, different shapes, or using a little bit of motion to attract a data visualization viewer's attention to a certain data point, using motion, even very small motions, or slow motions, was far more effective at drawing attention. And this remained true when they then asked, read back the values that were being
00:29:57
Speaker
conveyed by the points that were moving. So first of all, I think there's just that basic fact that motion draws our eyes. It's the same reason that the horrible websites that we all hate will spin up loads of auto-playing video ads. And I realize I'm in danger there saying that bar chart races are sort of horrible.
00:30:19
Speaker
But yeah, so on the very basic level, I think motion draws attention. But then another paper, a really interesting paper that I read was one called The Effectiveness of Animation in Trend Visualisation. And this is a paper by George Robertson and some colleagues that actually came out in 2008. But in 2018, IEEE Vis, it won an award for papers that really have had lasting, lasting value on the field.
00:30:45
Speaker
And they took the example of the now famous and much loved Gapminder animated data plots that Rosling and Co. produced a few years ago now. And they presented three different versions of that same data set to participants and asked them several questions.
00:31:06
Speaker
about how they felt about the effectiveness and enjoyment of the visualizations as well as their ability to read off the values. So they had one animated version, one static version but using snail trails like the connected scatterplot and one small multiples version and they found that there is just something fun essentially about the animated version so
00:31:29
Speaker
I'm just reading through, they came back to it 10 years on and wrote up some of the most memorable insights they got from it. There's a line here where they say that study participants describe the animated version as fun, exciting, and even emotionally touching. But at the same time, some participants did find it confusing.
00:31:49
Speaker
They also found that whether or not the different displays of information had interactivity, users who had delivered the animated version were less accurate in their readings of the data than those that saw the traces or the small multiples.
00:32:06
Speaker
There's a few interesting things in there. So first of all, from this sort of database purist perspective of are people able to accurately read values of this visualization? It seems fairly clear that animated versions are actually slightly worse at that. And I think that seems pretty intuitive. When things are moving around, it's harder to get a measure on them.
00:32:27
Speaker
But if you've also got people coming away and saying they found they really enjoyed the experience of consuming the visualization, they found it fun, exciting, emotionally touching, and they gave it more focus, then I think that's noteworthy as well. And when you look at the actual numeric values that they assigned to people reading off
00:32:53
Speaker
the values on the different charts as well. It was higher for the static examples, but not vastly so. I think there's a lot there that tells us why people really do just seem to engage with trends that have motion in them. Coming back to what you talked about, is there something here about the familiarity of watching something unfold? Do we think it's like a movie or like a TV show?
00:33:23
Speaker
But again and it's not just what i think but based on a lot of the responses to the piece that i put out and that a few others have done as well and people really do the suspense of seeing something unfold really seems to hold someone's attention far better than static shots do the way i think about it is.
00:33:44
Speaker
A line chart, yes, a line chart is going to be a more efficient way of communicating that information because everything is there upon the first viewing.
00:33:55
Speaker
The flip side of that is that, one way of talking about it would be to say that the bar chart race is the no spoilers version of a line chart. It's that very fact that you don't know what's coming next, which is, again, especially for people who aren't familiar with spending a lot of time reading databases.
00:34:18
Speaker
that really just naturally draws people in and holds people in in a way that a static chart doesn't. So I guess my way of thinking about it is it's not so much that one thing is better than the other, but I think recognizing and appreciating that
00:34:36
Speaker
animation, there is just something about animation which makes people want to stay with a data visualization. Actually, those who are not data viz familiar, shall we say, I think that's really valuable, really worth knowing.
00:34:51
Speaker
Something that a couple of the academic papers I've read talk about is how maybe it's not about saying you should do one instead of the other. It's about thinking about the situation you use them in. Maybe you say, well, on social media, we're going to use the bar chart race version because that's where we're probably going to encounter a lay audience that is less familiar with database.
00:35:15
Speaker
that we just want to engage and hold and inform about this. But then the version that I might use in a slide presentation when I've already got a captive audience or in a news story or an academic paper where the person reading the paper is already there and you don't have to grab their attention, then that's maybe where you use the static version that they can then pour over, analyze and read off specific individual numbers.
00:35:43
Speaker
But I do think it's worth appreciating that there are fundamental things about animation that are simply more effective than static animations in achieving certain results. And again, that's why I think Andy Cockrieve's line that these are a fad, I think that's unlikely to prove true.
00:36:04
Speaker
Yeah. I want to ask one last question before we go.
Tools and Techniques for Creating Animations
00:36:08
Speaker
I would guess there are a few people listening to this episode saying, this all sounds good. I really like the bar chart race. I agree with JVM about making animations, but I have no idea how to do it. Or I don't necessarily have the platform to create something interactive or create something animated, forget interactivity, just animated.
00:36:30
Speaker
Are there techniques or tools that you either use or know of that you would point those folks to? And I'm thinking of the researcher or the data scientist who's either in our program or state of program or just using Excel, but has some good data and they want to make something they think a quick little animation would really help them.
00:36:54
Speaker
Sure, so one of the really nice things about this has been that I've seen these made in so many different tools and the first two, the brands one and the soccer players one, I don't even know what software was used to make those, but I've certainly seen a lot of people making them using the GG animate library in R. I've seen even some making these in MATLAB.
00:37:20
Speaker
And I've seen some made in After Effects. And again, I made mine using the D3 JavaScript library. And there's this open notebook on observable that anyone can fork and iterate on and use their own data. So yeah, there's a lot of tools out there to use. And I guess another
00:37:40
Speaker
Oh yeah, one other I should mention is I've seen people even managing to do this with the animations in Tableau. I know as well that another sort of halfway house version of this or at least a version that would probably deliver a lot of the same benefits would be to just actually make a set of small multiples and then using one of the various free online tools for converting a set of images into an animated GIF.
00:38:06
Speaker
you could go about it like that. Take a set of snapshots for each year, for example, in your data, and then turn it into a GIF. You wouldn't get the same smooth transitions from year to year, but you might still achieve that overall effect of watching a dataset grow over time. One other example I should just mention while we're at it,
00:38:28
Speaker
partly in terms of other people and tools producing this stuff, but also I guess I presented a bit of a false dichotomy earlier in saying that this is a case of bar chart races versus line charts, because a really, really great example of someone iterating on this was Josh Katz at the New York Times has been doing some line chart races, as it were, showing how the Formula One motor racing races have been unfolding this year. So it's the same principle of
00:38:58
Speaker
having these lines extend from the left to the right of a graphic and sorry whoever's in the furthest right and last is at the furthest left but yeah so Josh has been doing the same thing using lines instead of bars so you probably gain a few of the elements that people have criticized bar chart races for not showing and it gets a bit closer to what some of the line chart advocates have been asking for so yeah lots of tools, lots of formats, lots of people doing this kind of stuff
00:39:28
Speaker
Before I glaringly forget, Flourish, of course, have come out and created their own template explicitly for making bar chart races. There's lots of tools out there for people who code, for people who don't code.
00:39:43
Speaker
So it'd be great to see some more people experimenting with this kind of thing. Yeah, that's great. This is great. I'm excited to see what you and your team at the time has come up with in the animation space. It's certainly an interesting space to see developments in. So thanks for taking some time out to chat with me about it. No problem. Cheers. Cheers.
00:40:10
Speaker
And thanks to everyone for tuning into this week's episode. I hope you enjoyed that. I hope you learned something about animating data visualizations. I hope you'll check out some of the links on the show notes page, including the Patreon page that I set up to help support the show. So until next time, this has been the policy of his podcast. Thanks so much for listening.