 So, good afternoon everyone and welcome to the webinar today. My name is Gerry Ryder and it's my pleasure today to host this webinar about data visualisation. It's my pleasure to introduce Martin Schweitzer. Martin's currently working with ANZ as a data technologist. He has a background in computer science and a particular interest in data visualisation, computer science and user interface design. He has a very professional background which includes photography, working on large IT systems, lecturing as well as running workshops and training courses. Martin is currently succumbed to ANZ from the Bureau of Meteorology where he is largely responsible for the climate record of Australia. Today Martin is presenting for us the first in a series of two webinars focused on data visualisation. This first webinar will focus on visualisation design and principles while the second will focus on tools and techniques. So having covered off on those introductions, it's my pleasure now to hand over to Martin for our presentation today. Thank you Martin. Thanks very much Gerry and hello everybody. I'll just jump straight in. So when asked to present a series on visualisation, the first question I guess that everybody would have asked is what is a visualisation? And I wanted it to be slightly broader than just presenting graphic data. So my definition of the visualisation is that it's a visual explanation. It's anything that helps us understand something by looking at it. And a typical example is something that should be familiar to most people, a map of the underground. One of the things that makes this a good visualisation is it helps show the relationships between the different objects inside this and help people in this case understand how to get from one point to another. If you're trying to imagine looking at a text description of how to get for example from Edgeway Road to Blackfriars, it would be particularly complex, particularly if for example somebody told you that Tottenham Court Road is closed. One of the things that made this visualisation famous was that the designer discovered that when you're underground, it's really only just the relationships that matter. The actual exact geographical location is a lot less interesting. And that can be seen in this visualisation. So we'll just have a look at this. This shows the actual place on the map and then it moves to what it looks like on the underground map. It just cycles through what the locations really look like and the underground map. So once again, a beautiful visualisation of how the underground map actually maps to the real locations in London. Yes, another example. Often for people who may be a bit 3D challenged, we'd be familiar. Many people would be familiar with this IKEA visualisation that shows us the correct way to construct a bookcase. OK, why are visualisations important? Why don't we just have text description? We have a lot of descriptive statistics. Well, one of my favourite examples and something that really made my hair stand on end the first time I saw it was this thing called Anscombe's Quartet. Many people may be familiar with this. It's a famous example. What we have here are four data sets. One, two, three and four in Roman numerals. Each one is a series of X and Y values. Just looking at them, it's very hard to read much into them. But we can look at their summary statistics and, sorry, and for example, they all have the same average value for the X. They all have the same average value for the Y. The sample variance of both the X and Y is the same in all four of them. The correlation between X and Y is almost identical in all four of them. The linear regression is exactly the same. So a statistician may be tempted to say, well, these numbers are pretty much the same. However, as soon as we look at a visualisation, in other words, see the values plotted, we see something quite different. So just one example of how seeing a visualisation is very different to looking at the raw data. Another example that I've taken is we'll just go to some text and we will have a look at a file. This is the contents of a file. And as you can see, it's probably not easy to interpret what's in the file. Most files, when they're still done this, got just bunches of numbers. If I told you that these numbers represent RGB values, arranged according to the next Y grid, once again, it may not be obvious what the numbers represent. However, if I do this and present them as an image, suddenly we see, OK, we have an image. So as numbers meant or as data, the numbers meant absolutely nothing. However, as soon as we visualise it as an image, it all makes sense. So as Jerry mentioned, I've been interested in visualisation for a very long time, in fact, over 20 years. And one of the first books I came across was by Edward Tufter and was one of the seminal works. At that time, I think it wasn't really realised that it would become a seminal work. And he wrote a book called The Visual Display of Quantitative Information. And NIT says, excellence in statistical graphics consists of complex ideas communicated with clarity, precision and efficiency. And for the rest of the presentation, I'm going to try and expand some of these ideas. So he came up with a few principles. And the first one is graphical displays should show the data. We'll go through these principles first and then we'll look at examples. It should induce the viewer to think about the substance rather than about methodology. Avoid distorting what the data have to say. Present many numbers in a small space. Make large data sets coherent. And reveal the data at several levels of detail from a broad overview to the fine structure. And in his book, he's got many fine examples. However, I've tried to find more modern examples and I've taken some of the examples from the work that I do. So the first one show the data. What we're looking at here is a rainfall map of Australia. And the government instituted a plan where they said they would give farmers concessionary loans if they were in a region that had suffered a one in 10 year rainfall deficiency or one in 20 year rainfall deficiency. So the map we see here is a map where users can typically zoom in and out. But what we've done is to show only those areas where that are affected or covered by this concessionary loan. So I guess one of the things is we could have shown a typical rainfall map, but ideally make it as simple as possible and show only the data. So the pink and red areas are the areas that had been affected by either one in 10 or one in 20 year rainfall deficiency. Next, induce the viewer to think about the substance rather than about the methodology. So what we're looking at here is in Kyoto, Japan, cherry blossoms are a big thing. And in Kyoto, they've been recording the peak of the cherry blossom season since the year 800. So they have over a thousand years of data. And what somebody's done is to plot all this data. And what we see is that for about a century, they pretty much peak between the 10th and 20th of April. However, since the early 20th century, they start blossoming earlier and earlier. And a lot of people would say, well, this is a signal of climate change. However, what I wanted to show about this graph is that the person has plotted the actual data points using a little image of cherry blossoms, which is quite acute. But they also noted in an article they wrote about it that initially they had plotted it with a cherry blossom with six petals until somebody pointed out that cherry blossoms only have five petals. And the point about that is if people are thinking about how many petals the cherry blossoms have rather than about what the graph is saying, maybe they should have thought more about the substance than the methodology. But nonetheless, I think with any of these rules, often it's a good thing to break a rule none again because in this case, for example, I certainly remembered this graph long after I'd seen it because I remembered the issue with the cherry blossoms. The next one was avoiding distorting the data. And yeah, we're going to do something exciting and let's do it live. So what I've done is we're now seeing what's known as Jupiter notebook. I imagine a lot of people would be familiar with Jupiter notebook. Jupiter notebook allows us to run path and code. And in the next webinar, the whole webinar will be based around looking at a work in Jupiter notebook. However, this is a small demo that I've got in this presentation. And what we're looking at here is storage levels in the dams that are around Melbourne. So the first graph I'll pull up, I'll just, so this is fantastic, it worked. What we see in this graph is it looks like that Thompson, Cardinian, Aperyera dams are really low and all the rest of them are almost full. So we may worry a bit about that. However, when we look at this graph, we see that we started, the base of it was 60% full. So Cardinia, for example, is actually, well, let's say Thompson, it's actually almost 65% full. So it's really not that bad. And when we look at the graph, plotted against starting at zero, we notice, well, it doesn't look that bad. And we may also look at this and say, well, the other dams are all over 80%. So we've got nothing to worry about. However, not all these dams are the same size. So looking at only the percentage can be a bit misleading. So let's run this one. And what we see here is that the amount of space in the Thompson dam, there's probably not enough water in all of these smaller dams to even fill that gap that's in the Thompson dam. So that's what we mean when we say avoid distorting the data. Try and make sure that we're telling a story with integrity. The next principle was to present many numbers in a small space. The map that we're looking at here is Australian rainfall decels. So this says that the areas that are in this right red have received the least rainfall this December. They in the lowest 1% of rainfall, December rainfalls. And these tiny dark blue patches on the highest 1% of rainfall that this record goes back to 1910. So they take every year from 1910. Okay, we say present many numbers in a small space. So what we're looking at here is a grid and they're roughly 640 by 800 grid cells. So each one is calculated. And for each one, there's 117 years of data. So what we're looking at is almost 36 million data points. However, we've condensed those 36 million data points into one, well, simple map. And so I think this is a fantastic example of presenting many numbers in a small space. Sometimes, as I said, we want to break the rules and yes, something where we break the rules. This was a recent tropical cyclone. We've got a visualization that shows the current position of the cyclone. This arguably is just one data point. However, it's a really important data point, particularly if you're living in the north of Western Australia and you want to know how close the cyclone is or whether it's got a chance. Also, by clicking on that one point, we see a far more detailed image, which then takes us into seeing the data at different levels. Okay, the next one was around making large data sets coherent. This is something that at the Bureau that we're very interested in. How do you communicate things like probability? When people year almost certainly, do they think that an event is more probable or less probable than if they year highly likely or if they year very good chance? So what they've done is taken all these terms and presented them using a technique known as KDE on one graph. And so we can very easily compare that, for example, if somebody says chances are slight, that people think that there's actually slightly more chance of an event happening than if we, for example, say it's highly unlikely or if we say there's almost no chance. So that covers off on Tufta. And the next few slides are some of my ideas and some of my experience in developing visualizations and some things that I feel are important. And one of the most important things in any visualization is that you actually have something interesting to talk about the data. Whenever I see somebody saying, oh, we've got this data, it looks pretty boring. Can we just create a visualization? Well, that's when the, here's on my neck, prickle a bit. So this is a famous video, it started off as a TED talk by the Swedish Hans Rosling. So here we go. First, an access for health, life expectancy from 25 years to 75 years. And down here, an access for wealth income per person, $400, $4,000 and $40,000. So down here is poor and sick and up here is rich and healthy. Now, I'm going to show you the world 200 years ago in 1810. Here come all the countries. Europe, Browns, Asia, Reds, Middle East, Green Africa, South of Sahara, Blue and the Americas, Yellow. And the size of the country bubbles show the size of the population. And in 1810, it was pretty crowded down there, wasn't it? All countries were sick and poor, life expectancy were below 40 in all countries. And only UK and the Netherlands were slightly better off, but not much. And now, why start the world? Industrial revolution makes countries in Europe and elsewhere move away from the rest. But the colonized countries in Asia and Africa, they are stuck down there. And eventually, the Western countries get healthier and healthier. And now, we slow down to show the impact of the First World War and the Spanish flu epidemic. What a catastrophe. And now I speed up through the 1920s and the 1930s. And in spite of the Great Depression, Western countries forge on towards great... Okay, I think people get the idea. Now, one of the things that strikes me about that video is talking about inequality, et cetera, and gave this TED Talk. At a similar time, Thomas Piketty, who was famous for his book on capitalism, also gave a TED Talk. I watched both talks, both were equally impressive. I thought Piketty's was the more impressive. However, Rosling's, the one you've just seen, got 10 times as many views roughly as Piketty's. And I think the real reason it got so many views was because it had such a story. It had such remarkable visualization and graphics. So it certainly says that it's important. And obviously, Rosling was a very impressive storyteller and was just a very impressive presenter, and so did really well. Of course, not all of us have his talents. However, we can all do good or great visualizations. So yeah, it's a simpler graphic. And this one shows the trend in maximum temperatures from 1970 to 2016. So wherever the graph is read, the average maximum temperature has been increasing and wherever the graph is blue, the maximum temperature has been decreasing over the years. And I think this one tells quite an alarming story. Yes, another visualization. And this one, I've got three slides, which show a progression of how we're trying to convey something. So in the first slide, the person has just taken the data and they've put it, this is rainfall data. They've started at 1900 and showed how much rainfall up to the years 2010. Now, there are two large influences on rainfall. One is the Enso, which is often we either in a La Nina system or an El Nino system. The other one is what is marked as IOD, which is Indian Ocean Dark Pole. And once again, these can be either positive or negative. So we've got two, four, six, seven different colors on the graph showing that when this rainfall fell, what kind of system we were in. However, this doesn't really tell a good story. If we look at it having been rearranged, we see that the blue lines on the right, when all the years where we had a lot of rainfall, all tended to be where we had a La Nina and a negative Indian Ocean Dark Pole and the red and brown on the left. We're doing generally El Nino years. However, we can improve this as well because we've got seven different things. We have to keep looking at the colors, move forwards and backwards. So here's a graph where what we've done is we've plotted the IOD along the bottom going from negative to positive. We've plotted the ENSO along the left-hand side. So these numbers in the top right, we can see had a strong ENSO signal, strong La Nina and a positive IOD. While these numbers to the left had a, sorry, these are the La Nina and negative IOD. And we can see as it gets stronger, how it affects the rainfall. Here's another graph which also tells quite an alarming story. This is the water supply in Cape Town. And in 2013-14, we can see that typically get their rainfall in winter. And so from October onwards, the dam levels start falling. And because for about the last five years, there hasn't been good rain, they've continually been falling each year progressively. That's 2013-2014, 2015-16 up till this year, which is 2017-18. And we see when I pulled up this graph was between January and February, and we were over there. And they were projecting that around April, May, June, Cape Town could run out of water. And there were a few projections. One is if people use 600 megalitres a day of water, one with 500. One is if they were using 600 megalitres and they've started up some diesel plants. So what would happen? And all of them show pretty dark consequences. And a visualization like this really does tell a story. So the next principle is keep your graph as simple as possible. And I've made a very quick 3D graph. Just made a fictitious one, which is how many people attended morning teas and maybe the person who attends the most morning teas at the end of the year gets a prize and the person who has attended the least gets a wooden spoon. So this was my first graph. And I felt, well, this can always be improved. Whenever I see a 3D graph, if it's not displaying 3D data, I'm a little bit disturbed. So I modified it. So we've not got a 2D graph. However, the numbers are in the box. We probably don't need those grids and there's many of them. We certainly don't need dotted and solid line grid. So clean that up a bit. So there's a simpler graph. However, when looking at that graph and often I see graphs like this, the first question I ask is, what do those colors mean? Why are there different colors? Well, in this case, the colors mean absolutely nothing. So I've got rid of the colors. The next thing is getting back to this idea maybe of telling a story. What am I trying to say? Well, really what I'm trying to do is find out who attended the most and least morning teas. And so maybe by improving the graph, well, I've now put the least. I've ordered them from least to most and that's quite obvious who's all attended the least, who's attended the most. So is there anything else we can do to make this presentation simpler or to remove any unnecessary data, et cetera? This is a trick question, but of course there is. Well, in this particular case, I think we can just remove the graph altogether. I don't think that that visualization has given us any more information than simply looking at a table of numbers. The table remains ordered. I get exactly that same information. So it's probably important to ask that question occasionally. Do we really need a graph for this date? Or do we really need a visualization for this data? And I think Antoine de Saint-Luc-Zouper said it best. When he said, perfection is achieved not when there's nothing more to add, but when there's nothing left to take away. However, this was a however, Einstein was apparently famous for saying, make it as simple as possible, but no simpler. And so here's another example of a visualization. This is called a skew-T log P graph. And this is used by meteorologists every single day. Temperature is on these diagonals. The pressure is going along this way. And the reason it's called log P is because at the bottom, you see the gap between 900 and 1000 is much smaller than the gap between 200 and 300. So even the scale appears to be changing. There are two different color lines, each of those lines has a meaning. The red line is what was recorded today and the blue yesterday. In case, well, I imagine most people aren't familiar with these graphs. So what this is actually plotting is at a lot of locations around the world, they send up weather balloons or sands. And so this is plotting the temperature as the balloon is moving up through the atmosphere. So we can see that it's getting cooler, et cetera. And the second line is the dew point. So we can see, for example, if the dew point crosses the temperature, we're going to get precipitation or rainfall, so on. On the right-hand side, we've got another particularly interesting thing being visualized here. And these are called wind bulbs. The direction of the wind bulb shows the direction of the wind. So these ones pointing upwards show northerly wind. And the number of feathers shows the speed of the wind. So the short ones are five knots, a long one is 10 knots, a long one is short is 15 knots and so on. I won't go too much into this. But the fact is that for meteorologists, this is a really important graph. It's not as simple as a bar chart or a line graph, et cetera, but it's serving its purpose. And that's the most important thing. A visualization has to be fit for purpose. Okay, next thing we'll look at is color. And I'm not going to go into color in a lot of detail. The main reason being because you can spend hours talking about color to really understand it thoroughly. I've got a few suggestions, but the most important one, I think, is for color. If it's important, please try and find somebody who's an expert. There are lots of different factors to consider things like color blindness, common conventions, cultural differences and so on. And this is just a very simple example. These are from images of blood traveling through an artery. And this one, basically they showed these different images to a lot of doctors and asked which one they preferred. And most doctors came up with this A. However, when they asked people to diagnose issues with these things, I think the best one was F or G. Where they were able to identify the most issues or see the most problems with the patient. So even though they thought that this one was the easiest one to read, the colorful one, admittedly that we're used to those colors, et cetera, it's not always the case. And the reason I'm saying this is it really does say that color can be a tricky issue. And that really it does need some expertise. And in this case, it was actually some research. In this slide, there's a reference to this paper that talks about this. It's quite an interesting paper. Just on the topic of color, you have some examples from the Bureau once again. This one is showing rainfall. It's using a scale, a gradated scale. So darker means more rainfall. And they've used the color blue, which makes sense because the more saturated blue tends to show areas that have more saturation in terms of rainfall. This map is not showing how much rainfall, but it's showing how variable the rainfall is. In other words, how much it differs from year to year. So it wouldn't have made sense to use blue year because some areas can be very dry, but at the same time have a lot of variability of or have very little variability. Areas that may be very wet may have a very small variability because they're wet all year round, just as areas that are wet all year round have low variability. So this one is showing, they've chosen a different color for this one. This one is showing how much rainfall, in this case fell in the week of the 23rd of January. And this is using a scale that people who are looking at this type of map are familiar with. The white areas have had no rainfall or not be able to record. And these dark colors are the areas of the highest rainfall. Once again, we see that the scale is not linear. So there's a color for between one and five millimeters. There's a different color for between 300 and 400 millimeters. Okay, I think it's useful when looking at visualizations also to see examples of maybe things that we can try and avoid. And this is always the part of this presentation that I feel uneasy about. But I think it's just worth having a look at an example. So we'll have a quick look at this one. So what this is talking about is average household debt in America by this person who is a financial data journalist. And it's how much debt you have. It's an infographic. So the first thing I looked at when I saw this is, okay, we've got some sort of thing that looks like a visualization. And I try to work out what it's telling us. And I looked at it and I thought, well, why are some people green and some people, is it the green ones have less debt now or different sizes that, and I realized that probably doesn't mean anything. It's just decoration. So we can move on. So the next thing is the total out about the average. We see credit cards are 16,000, mortgages are almost 10 times that amount, but the mortgages aren't actually 10 times as long in the specialization. A 28,000 is a lot longer than 16,000. So there's clearly no clear scale. I should just say there's no clear scale on this. And once again, we've got different colors, but once again, that seemed just for decoration. The other thing is I couldn't understand why any type of debt is 134,000, while mortgages are 176,000. So it wasn't quite clear what any type of debt meant. Also credit cards and auto loans were lumped together with mortgages, which are more of an asset and some people differentiate between things like mortgages, which they classify as good debt and things like auto loans, which are classified as bad debt. The next one is how much does debt cost you? And this is probably one of the better ones, but given that she's used comparative scales in the previous ones, I was surprised that there wasn't any comparative scale. And I think one thing I did notice here was that this figure from memory didn't really add up. This was an interesting one, medical debt on the rise. There were a few issues with this, but one of the things we notice is if that's 33%, then that one is about 37%, and yet that 37% segment actually looks a bit bigger than the 42% segment. And considering that halfway across would be 50%, I don't think that that 42% is accurately reflected in the pie chart. I won't go into the colors that have been chosen or talk much more about pie charts. A lot of people have very strong opinions about how useful pie charts are. We now come to debt broken down by age. In this one, it actually looks as though the colors may be meaningful because they're two red bars, two orange bars and two green bars. But once again, it just seems that the colors were arbitrarily chosen. And that's all I'll say about that, but except to say, I do think, have a look at examples and always look critically, look critically at your own work, at things that can be improved, but also when looking at other things, think about, okay, is this a good visualization? Is it a bad one? When you see something that looks good, what makes it look good? When you see something that looks okay, maybe think, how could it be improved? What could this person have done to make the story clearer? So what are some techniques that you can use when doing a visualization that will make it better for the people looking at it? And one of the first ones I talk about is natural mappings. What we're looking at is what's called a wind rose. And what this is showing is a wind in eight quadrants, but eight sectors and how windy it is. So this is Melbourne Airport that we see here. And we see that most of the winds at Melbourne Airport are northerly. These are the averages taken over a particular period. As we go out in this telescope, it shows us stronger and stronger winds. So for example, we hardly ever have, let's say, gale force winds in this southwestly direction. And there's very few easterlies at Melbourne Airport. So, but the natural mapping is if it's facing upwards, then we can see straight away it's a northerly wind. We've seen in this graph before, but the important thing is to highlight relevant information. So if all five of these lines were the same color, it wouldn't be quite clear what the story's telling us, but given that this one is highlighted and the others are muted, we can see straight away our focus shifts to this one. The next thing, make comparisons clear. So what this is comparing is Arctic ice. This is going back to 1879, and it's comparing sort of as we're progressing into the present. And one of the things we see is it seems pretty clear that there's less and less Arctic ice as we're coming into the present. And by overlaying those plots, one on top of the other makes it a lot clearer. And going back to this graph, we see once again, by plotting all these different attributes on the same set of vertical axes, it makes those comparisons much clearer. So for example, when we're comparing highly likely to very good chance, we can see quite clearly how they compare. The next thing is, in this case, it's probably exaggerated, but make the scale clear. This is showing the stations in Australia that record, it's showing basically the largest difference between two days. So between the maximum temperature on day one and day two. So at these stations, there was a 25 degree or 27 degree difference. So one day the maximum temperature was 10 degrees and the next day 37 degrees, for example. As we went further north, there's less difference between successive days in temperature in terms of their records. He has another visualization this time of space. And we've got a very different scale here and it's probably hard to read on the slide, but that distance there is 100 million light years across. So a light year is pretty big. 100 million light years is 100 million times as big. Okay, finally, color should add meaning and not detract. And we come back to this slide, which is how much Australia has or the warming trend in Australia since 1970. And clearly color is enhancing the meaning of what we're trying to say here. Use conventions. If we look at this time series of temperature, at first look it may seem that temperature is actually declining. This is just a dummy slide I created for this presentation. What I've done here is these temperatures are actually, if we look carefully at these numbers, we see the numbers are actually decreasing as we go from left to right. Normally when we read from left to right, we expect time to increase. In other words, get either closer to the present or further into the future. But turning it around, we've defied that convention and then obviously made this a whole lot harder to read. There's a lot of ways to display different dimensions. And I'll just skip this for the moment today. We'll get back to it if we've got a bit of time. So here's another slide showing how we can plot dimensions very differently. In this graph or in this visualization, what we've done is this is temperature in Africa, but across a range of latitudes going from 30 south to 30 north. So the y-axis is latitude. The x-axis is the month of the year and the actual colors depict the rainfall during those months. So what we see is in the southern latitudes, we get rainfall around December, January, February. As we go north of 20 degrees north, it's very dry. And around about 10 degrees north, they get mostly a winter rainfall. This way of plotting data is known as a hofmuller plot. These are called churnoff faces. And what this does is it allows us to plot multi-dimensional data by using faces. So churnoff said people, their brains are hardwired to really recognize faces quickly. So what we can do is we've got about seven or eight different attributes we can change. We can change the smile on their mouth. We can change the length of their nose, the distance between their eyes, the amount by which eyebrows are raised, and so on. So we've taken a kind of dummy data set here comparing different universities, different people across the universities. And then we've said, okay, we'll use, for example, the eye color to show how where they are of data sharing and maybe the length of the nose to show awareness of data licensing, et cetera. And so basically it's a novel way of displaying data with a high number of dimensions. And as I keep saying, it's always good to break the rules. Some people may be familiar with this image. It's called pale blue dot. If you're not familiar, it's a visualization, or I guess any image can be. But what it's showing is over there, there's a pale blue dot. And this photograph was taken by Voyager 1 from Art of Space, or from Space. And that pale blue dot, that almost single pixel down there is Earth. So often we're told to make the data we display significant and obvious. In this case, the strength of this visualization comes from how insignificant that tiny little dot on that photograph is, how insignificant this huge planet that we live on is. And Da Vinci said simplicity is the ultimate sophistication. I've got a few things in my slides. I'll just go back to the slide that I was trying to find earlier, which would be, for some reason. Okay, so what we're going to see is how Australia's temperature has changed for the 12 months ending December 1910. Just maximize this. And this is an animation. The color shows the year. And we see as we coming more and more to the present, the colors spiraling outward, representing warming. So I guess what makes this visualization effective is not only the animation, but also the fact that we were able to draw a line which shows about a hundred years of data, which typically would have been a very long line. But in this case, by wrapping it around in a circle, we were able to show it all in one compact way. Okay, so finally, all visualizations are wrong. What do I mean? There's a famous quote from George Box, the statistician that said all models are wrong. And he said, all models are wrong. The only question of interest is, is the model illuminating and useful? And I've changed that to all visualizations are wrong. The question is, is the visualization illuminating useful and does it have integrity? Thank you. Well, thank you so much, Martin, for that really valuable presentation that's I'm sure given us all a lot of ideas and some things to look forward to in the next webinar where we'll actually see some of the tools that you've used to create these examples. We do have time for questions. If we have anyone in the audience that would like to ask Martin a question about anything he's presented on today, please do put it into the question pod. And I'd happily relay that and put Martin on the spot. So we've got a number of people thanking you, Martin, for a really interesting talk. And we have got one question, Martin, from Mark McKay, who's asked if you could suggest any textbooks or papers that he could share with students. Yes, I do. Quite a few. And I've actually put them in the slides. So at the end of the slides, there's some references. I believe the slides are going to be made available, Jerry. Yes, that's correct. We'll have both the slides up as well as the recording up. So, yes, you can have a look at the slides separately to the recording. If I'll just quickly look at some, because if you put them up now while we're doing the questions, then they're there for people to see. So if somebody wants to change to the screen, I think I've got to accept them. And another question, Martin, can you provide the name of the visualisation with the faces? Somebody's obviously liked that one. Shunov faces C-H-E-R-N-O, either V or double F. OK, so perhaps we might put that... Susanne, you might be able to pop that in the question box for people to see. C-H-E-R-N-O-V or double F. Someone's... Richard's asked, Martin, you've used Jupiter notebooks. He's pre-empting the next webinar. What sort of other technologies do you normally use to build visualisations? And another question related about open source software for visualisation. So I know we'll cover that in the next webinar, but perhaps a teaser today, Martin. OK, so definitely Jupiter notebooks and Python. And the next webinar will focus largely on Python. We'll also do a lot of work with web front-ends and JavaScript. And so if somebody's working with JavaScript, there's a huge array of visualisation tools. But probably if you don't mind a steep learning curve and want to be able to do absolutely everything, D3.js is the go-to one. And it's open source. OK, excellent. Thank you, Martin. Someone wants you to... at your centre, wants you to look in a crystal ball and asks, what do you see as the future direction of data visualisation? Wow! I think... What's happening is we're getting to things with higher and higher resolution. We're going to more dimensions. So we've got, like, the two-dimensional static flatwork. We moved to sort of two-dimensional animation with the web. One of the things that is becoming popular is virtual realities. So people can put on some glasses and maybe see storms, the data for the storm being visualised, but in their own surroundings. So what does it feel if a rain... And that actually gets us on to the next one, which is augmented reality. So I can look around at Monash University or let's say I could go down to St Kilda Beach and see what it's going to look like maybe in 100 years with sea level rising two feet or 10 feet or something like that. Both exciting and scary. As technology changes tend to be. We do have a couple more minutes if there is any other final questions for Martin. So Lisa is interested in the relationship between storytelling and data and the idea of integrity and worries about collecting data to suit a story and there being a lack of rigor and accountability. I guess that's a comment more than a question, but you might like to respond to that, Martin. Yeah, I think it's a... Integrity is always in sort of the mind of the beholder. So you can't... Data cannot have integrity. The people using and presenting the data need to have integrity. They need to present the data with integrity. And I would say any tool that can be used for good can also be used for evil. So yes, people can create visualisations that try and push an agenda or push a point, et cetera. And hopefully, by being more critical of visualisations, we can actually see those ones where somebody is trying to push something which isn't true. And that's why I also push for integrity in data that as soon as we show a visualisation that only shows 30 years of data where maybe, let's say, temperatures have been decreasing, immediately it sort of puts a cloud over everything that person is saying because why have they picked that 130-year period where the temperature was dropping? So I think in the long run, it pays to be as honest as one can about data. And a final question today, thanks, Martin. Is there a common standard for colour coding for general use in data visualisation? Very simple and short answer, no, absolutely not. There's a website called Colour Brewer. Actually, it's called Colour Brewer 2. So colours felt the American way and Brewer like somebody who brews, too. And I would recommend anybody looking for a good set of colours to go there first. Excellent. And a tool for visualisation will actually use... So it was written by a researcher called... Her last name is Brewer and she's done a lot of research into colour and how to use it well. Great. I'd like to thank now Martin for his presentation today and also acknowledge Susanna, who's been quietly sitting in the background responding to your questions and making sure the webinar runs smoothly. So thank you all today and have a great afternoon.