Who are my data visualisation inspirations?

Who are my data visualisation inspirations?

I wanted to write a post about my inspirations in data visualisation. First of all, I feel a little bit uncomfortable citing inspirations, not because I feel uncomfortable expressing praise to others, but because I feel that as someone so inexperienced in the field, you might think that I am eliciting comparison. Please don’t think I’m like the guy who can sing and play the piano a bit and goes on TV wanting to be the next Elton John. In the same way, I can’t claim to be in the same league as most of my inspirations. If I’m honest, I didn’t know I was inspired by quite a few of the people on this list until I stopped and thought about it. But if you can get past that, then here’s my list.

1. Hans Rosling

Last week saw the sad passing of Hans Rosling. It feels a bit like jumping on a bandwagon to pay tribute to him at this point, since many words have been written in tribute. Hans Rosling is known to many in the data visualisation field for his work on gapminder.com, his TED talk videos, and his BBC FOUR appearances presenting The Joy of Stats. I’ll almost take it for granted that anyone who has found this page has done so because they have an interest in data visualisation, so will be aware of some or all of these things. If not, go and seek them out!

I can think of at least three things that point to the influence of Hans Rosling on my work. First of all – this animated viz, on the Global Peach Index.

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Above is the start of the visualisation, but you can click through to a hosted version which shows the animation of peach production in each year from 1062 to 2012. Bizarrely, this started as a bit of fun on Andy Kriebel and Andy Cotgreave’s Makeover Monday project which started as a result of a typo on the Global Peace Index. But that notwithstanding, I produced this animated visualisation. It’s crude and quite simple, but I got feedback that it was “very Hans Rosling”. I felt enormous pride at this comparison.

The second example was from last week’s project (below), the last viz I created before his death. Like Rosling, I love the scatterplot. It allows granular display, with x, y, colour and size available to compare four different dimensions. Five, if you include time as Rosling often did. I used this, I hope, to good effect in my Brexit visualisation here:

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The dataset available last week contained data on many millions of Chicago taxi trips. I can’t tell you how many more stunning and successful visualisations were made – see Exasol’s blog “Let them have ALL the data” for just some of the best. But I didn’t want to use Exasol’s big data capabilities, choosing to stick with what was regularly available to me. I also stuck with my favourite visualisation form. A scatterplot of destinations, using two measures, colour and size to cluster destinations. It didn’t attempt the sophistication of many of the big data dashboards, but it could only have been more Rosling if it was animated. And it was produced the day before his death was announced.

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My third example of his influence is perhaps the most powerful, but not a visualisation per se. Rosling had a real passion for his work, and wanted to get up and tell people about it. Not about anything technical in relation to his visualisations, but he wanted to tell the story the data was telling, to enhance the message. He told good news – focusing on social measures and the improvement in standards of developing countries, and used his charisma to enhance what his visualisations reported. This was perhaps the most inspirational thing that set him aside and placed him as such an influential master of his craft.

Just two weeks ago I was part of an exercise at the Tableau User Group in Nottingham. We split into groups of 4 or 5 of varying skill and experience levels to look at some data provided around road traffic accidents. Between us, we came up with a story and a couple of visualisations to present to the group. Nothing earth-shattering, I think we found things like the existence of more cycle accidents in London, more accidents during weekday rush hours, and each year’s quietest days being on Christmas day, that sort of thing. I felt I should get up and present – something that, like many introverted data people, is not something that comes naturally, but my role as a Tableau Ambassador would have made it a good chance to introduce myself and say a bit about what the team had done. But another attendee from our team wanted to get up and talk. I tried to be quite persuasive to say that I should be the person to present – I really didn’t mind – but my new friend was having nothing of it. Of course I was delighted that he was keen enough to take the presenting on, but in part I was actually quite disappointed. Having helped create the visualisation I wanted to tell people about it – explain the findings and tell the story. That to me was the biggest Rosling influence. Pride, ownership and conviction in our visualisation findings, with enthusiasm and charisma to pass them on.

2. David McCandless

There’s a spectrum in data visualisation that has Stephen Few or Edward Tufte on one side (strict adherence to best practice – “go to” experts for rules, standards and expertise), and David McCandless (fun, visually engaging, disregard for best practice at the expense of beauty). The first books on visualisation I bought were by David McCandless. As a novice stumbling on the concept for the first time, only about 18 months or so ago, his were the books I wanted to flick through, read and enjoy. Most exponents of data visualisation know the Few/Tufte principles and some decide never to stray from them. I feel all should know them and use them when appropriate. But I gain more pleasure from the visually striking and engaging, undoubtedly coming from the fact that I have created more projects for pleasure than I have professionally. McCandless’s brand, and website, is centred around the words “Information is Beautiful”. I bought the books because I agree!

Here’s a simple McCandless from his website – “Plane Truth” visualising commercial passenger plane crashes. The data is all there and accessible, but behind tooltips. Outwardly, all that can be seen is different sizes and colours of rounded squares. They tell a story and are instantly visually striking, but aren’t the best medium to exactly compare and contrast incidents without digging further; but to my mind you have to ask yourself if that matters. Are you using exact size of shape for insight (and, with that, the quickest, most accurate insight possible?) Or is an initial visual estimate OK if it’s visually engaging – if not an approximation, then an accurate depiction in not the most intuitive way. If the latter, then you probably, like me, lean towards the McCandless side of the spectrum. I firmly believe that it’s important to understand and adhere to all best practice principles where appropriate. But, once you understand them, you develop an appreciation of when it’s OK to bend, break or turn a blind eye to them!

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3. Chris Love / Rob Radburn

As I was starting out – one morning I was working from home with my twitter feed on in the background while listening to an England cricket match on the radio. At one point during the day, I suddenly realised that one person was visualising the game almost as quickly as I could listen to it and sharing his work with his followers:

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http://www.theinformationlab.co.uk/2016/03/30/7-tips-for-creating-a-topical-data-visualisation/

Instantly from this I realised several things

  • Though I was a complete tableau novice, the software can be used to make visualisations which are great, topical, quick (though I don’t think many people are as skilled and quick as Chris), and on almost anything
  • There are people doing this who are local to me. Within a few miles, accessible via the online community, and part of the local Tableau User groups.

I had no idea what a Zen master was but it turned out that this guy was one. As I’m sure you know, and I do now, that’s a pretty good thing. I’ve since seen many more great pieces by Chris – and though we often don’t agree (Chris is both technically adept at producing a great range of chart types while advocating the need to keep things simple), I can’t help be inspired by his work.

I also noticed plenty of correspondence at the time between Chris and Rob Radburn – another Zen master who worked in the same city as me. Rob was producing some lovely stuff too, and worked in local government, so often his concerns were to do with social data, the same field I was about to move into. This viz by Rob really resonated with me, and gave me ideas of more striking things I could do professionally. I didn’t yet realise that it was actually technically pretty easy to do, but it was a great example of “thinking outside the box” and telling individual stories through visualisation

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It was a great encouragement to have two experts local to me, living as I do a long way from big city London, and they continue to be leaders in their field. And I’ve yet to see a bad piece of work from either of them!

4. Adam McCann / Chris DeMartini / Rody Zakovich

So many talented data visualisation creators, but these are just three more of those high on my personal list, who do things that I like to create, and do them in my software package of choice: Tableau. There are people who create jaw-dropping graphic creations but I don’t include them because they are not the kind of visualisations I create. I like curves, mathematical visualisations, and unorthodox chart types. I know that some of what I create will make the purists cringe. I like to create exploratory vizzes, not necessarily insightful ones. So why these guys in particular? Here’s one example from each of a technique I’ve leaned heavily on …

Adam McCann loves to demonstrate new ideas and techniques, the very latest of which was just two days ago – the spark bar chart:

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It’s come in for equal measures of appreciation and criticism (probably the Few/McCandless spectrum again!). Why aren’t the sparklines aligned? How can one measure on the x-axis represent time but the bar chart doesn’t? I see past all that – I don’t need to directly compare the sparks, just see a visual snapshot of each against the bar it represents. And I borrowed heavily from this to create my own, using Valentine’s Day data.

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It’s not as good as Adam’s, not least because the bars are more uneven, leading to sparklines off the chart to the left. But Adam remains a huge influence (as I may have alluded to in my signature radial bump chart viz here)

Chris DeMartini has also created and documented some great chart types – including the jump plot which remains one of my favourites. Mine is adapted for Premier League goalscorers. Unorthodox, and there are many simpler ways of showing goalscoring trends. But this is crisp, different, and fun! I included the jump plot in my previous post right here

Rody Zakovich loves to create vizzes with curves – check out his public profile to see great work on Queen songs, golf shots, US football quarterbacks to name just a few. When he created a sunburst chart based on the Boston RedSox, I knew it wouldn’t be to everyone’s taste. But I loved it and used it twice, with Rody’s backing and help!

Rody’s Redsox viz (top right) became anglicised to a Birmingham City football viz (bottom right), and eventually resurfaced as a viz for songs of my favourite prolific band. Great examples of exploratory vizzes, versus explanatory ones

One thing each of the three have in common is that their work is unorthodox but accessible, and all use Tableau (to Zen Master status) so I know that they might all have greater skills and experience, but start with the same toolkit, and the same blank canvas, as I do. This gives me the inspiration to know what is possible.

5. Nicholas Rougeux / Valentina D’Efilippo

My other inspirations here signify what to me has been one of the most exciting ways of using data visualisation; that is to create pieces of art. I could have included other examples of great data artists here, such as Stefanie Posavec or Giorgia Lupi, the two responsible for Dear Data. The insight comes from the way you want to explore the visualisations and the time you want to take enjoying it. For me, I’m so completely uncreative and unartistic, that creating data visualisations has been the first opportunity I’ve ever really had to do something a bit more “arty” (or so it seems to me, anyway). And, unlike some of the inspirations I’ve quoted above, these artists don’t use Tableau. So in this case, inspiration is less about stealing (like an artist, of course) and more about being inspired to create something similar from scratch.

Nicholas has created some great work on www.c82.net. Often his visualisations are completely word-free, with simple lines and swirls depicting multiple examples of a certain genre. Below are visualisations of sonnets and literary first sentences respectively

Nicholas’ work is good enough to sell as a poster, which is something I would love to aspire to. When I recently wanted to do something a little “different” in visualising classic chess games, I came up with the following, based on average piece position and number of pieces present throughout the game. As soon as they took shape, they reminded me of Rougeux’ work on sonnets, road intersections, or classic novel first paragraphs. I saw that only as a good thing!

I haven’t finalised/published these yet as I’d like to include some more games, but here are my classic chess game visualisations covering three centuries:

(Inspired by Anderssen vs Dufresne, Carlsen vs Karjakin and Spassky vs Fischer). These are snapshots of a work in progress, but I think they could become similar art style visualisations. Would anyone buy them, even a fellow chess nerd? I don’t know about that.

Valentina D’Efllippo is another data artist I admire, and I have included her here specifically in relation to the Oddityviz project. Oddityviz is a serious of visualisations inspired by David Bowie’s classic Space Oddity album. How good must it be to use data to create physical works of art in an art gallery? The full gallery consists of seven record-themed visualisations: here is one of them, based on rhythm.

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I had this very much in mind when I tried all sorts of ways to visualise my 1980s pop data which I first blogged about here. The influence of Rody & co (see above) meant that I was always planning to do something circular or spiral, but I was never particularly happy until I muted the colours, my viz became more record-like, and I realised I had an OddityViz inspired creation here:

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It won’t sell out any galleries, and it has been pointed out to me that it looks very much like an eye – I think I’ve perhaps missed out on a whole different metaphor opportunity there. But I’m still learning and experimenting, and this is one visualisation which I wanted to frame (no pun intended) as an attempt at data art, with Valentina’s work as an inspiration

6. Donald J. Tr*mp

So who saw that coming?! The first five are current and past influences. This one, tangentially at least, is one for the future.

People are human, and although my social media bubble, in particular twitter, is almost entirely data visualisation related, a lot of people have been very affected by this guy (I refuse to write his name in full). They write, rant, protest and share about this man. What can we do, other than vote differently in 4 years time, and encourage others to do the same? (In my case, as a Brit, only the latter!). We’re not politicians or major influencers.

The fact is, dissatisfaction is a great source for visualisation material, and that is something that we can do. Tr*mp has appalling attendance figures at his inauguration? Visualise it! Tr*mp has worst approval ratings of a new president in recent memory? Visualise it! So far I’ve only created my tweet spiral viz but the very fact that the “topic” of the viz is something that polarises opinion has created more interest than usual.

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Ben Jones and Flippo Mistriani have created great visualisations too about his recent golfing following his many tweets about Obama doing the same. Here’s Filippo’s:

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People such as the fabulous Brit Cava and Ken Fierlage have visualised the strength of feeling about Tr*mp’s Muslim travel ban and reaction following Women’s marches in the USA and round the world here, they are just two of many doing great work.

Here’s Brit’s visualisation

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And here’s Ken’s

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As a final example, author Alberto Cairo (who I mention a lot, a highly respected data visualisation practitioner and lecturer) has had a lot to say about the election of Tr*mp. For a while, his (very frequent) twitter output changed almost exclusively from data visualisation and journalism related posts, to political comments and opinions. But, crucially, he has now decided to do something about it.

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Alberto is channeling the political anger and disappointment in the wake of the ubiquitous “fake news” articles into his area of expertise – data visualisation. He will be touring with “Visual Trumpery” lectures later this year. Personally I will do all I can to persuade him to visit the UK (more specifically somewhere Midlands or North!). More of this to come later in the year if and when it happens. But this is a fine example of using social or political issues to inspire data visualisation. Something I need to do much more of. And I’m sure that Tr*mp will inspire me and people round the world to get visualising, telling stories that need to be told.

What can be achieved by collaboration?

What can be achieved by collaboration?

At the start of the year were a lot of tweets, messages and blog posts covering predictions in data visualisation for the year ahead and people’s own resolutions for what they might do in a new and different way this year to develop their skills, experience and exposure. Personally, I ducked out of that approach, preferring shamelessly to focus on what a good 2016 I’d had instead. But many spoke of collaboration being a key topic for the year ahead. Projects where people would work and visualise together, or pool together thoughts, designs and technical tricks in order to get the perfect combination. Some predicted this would happen more, and others resolved to be part of this new way of working.

As projects grow in size and the community of data visualisers grows at pace, I’m starting to notice this already this year. Makeover Monday has been a huge success through 2016, with on average approximately 60 people submitting visualisations on the same dataset each week by the end of the year. But in 2017, the first three weeks already have seen well in excess of 400+ submissions alone. Opportunities for sharing and collaboration are growing as quickly as the project.

This week, our dataset looked at New Zealand tourism spend figures, in index form, from 2008-2016 domestically and internationally. Our data covered the 60+ Territorial Authorities, and after a quick perusal of the data I came up with one conclusion: regional breakdown, comparable figures in every region, never seen it done before … I’d create a New Zealand tile map. I’ve blogged here about tile maps and had plenty of fun creating them, so I set to work on Sunday and created my visualisation below.

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I’m an early starter (it suits me to complete on Sunday as I will be much busier with work during the week) so mine was one of the first submissions. I was pleased with my effort but I think it was far from perfect. The tile maps worked well (or, they  worked the way I intended anyway). The colour scheme showed the regions as well as the Territorial Authorities, and my showing of monthly rather than yearly figures had two effects: (a) it showed all areas were somewhat seasonal but some much more than others, and (b) It meant it didn’t matter that 2016 figures were incomplete.

However there are some things I wasn’t pleased with. The overall colour scheme was eye-catching but somewhat quirky at best, with the lines looking like childish pen lines. The blocks to filter region in North or South Island worked and were functional, but the chart looked very busy. And generally I thought the whole thing was very unstylish (I know this is a particular weakness of mine).

I didn’t attempt to label each Territorial Authority on the map. Well, that’s not true, I did attempt, but with the long names for some areas and fiddliness of the task, I chose to remove the area labels and include them in tooltips and the rollover summary graph instead. And the colour scheme, though it did receive some compliments, will not have been to everyone’s taste – the colours were pinched from a London Underground colour palette (I can see UK readers having lightbulb moments here!). I kept in the regional regional breakdown as it was an important visual reference for me when creating the original tile map (see below – the aim to match left and right as close as possible whilst keeping the individual colours as true to their correct geographies as I can ). But was it necessary to keep the regional breakdown in or did it make the viz more complicated? Perhaps the latter, let’s just say I haven’t seen anyone else use the regional breakdowns yet.

The next day I saw the following submission (below) from Sumeet Bedekar. He posted his submission giving me full credit for the “design inspiration” (and crediting sirvizalot.com for the content/layout of the tiles). I don’t know Sumeet but he also participates in the Makeover Monday project. At first, I was torn between feeling a smidgen of unease that my tile map had been used, and delight that someone had chosen it. After all, in well over forty years on the planet I have never been anyone’s design inspiration in any way, shape or form! But within a matter of seconds, as soon as I looked in detail, any unease had dissipated to be replaced by satisfaction, and the delight remained.

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So what did I like about it? Yes the tile map design is exactly mine and uses the “work” from me the previous day to set up multiple graphs in tile formation. But the colours from my original have gone to be replaced by a much more pleasing red/blue scheme. The blocks for filters at the top and bottom merge in seamlessly across the screen instead of sticking out in ugly fashion.  Showing each line in a box gives a lot more definition and outlines the country more clearly. There is far less clutter and it’s a far more aesthetically pleasing visualisation – great work, Sumeet!

There are still elements of my own that I preferred. i’m not sure of the validity of combining domestic and international tourism in one graph as a percent, it doesn’t feel to me like that’s a valid measure and it doesn’t show increase over time. And perhaps he’s gone a little too far in keeping it simple – there needs to be some way of understanding what the figures represent (clarify, don’t simplify!).

Then, a little later, but also on Monday I received a message from Sarah Bartlett – it seemed she wanted to use the tile map as design inspiration too! Her final visualisation is here:

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Wow! This I really like. First of all – by now I’ve had a couple of hours to get used to being an influencer in the world of visualisation design, so this is old hat to me! If someone wants to use a snippet of my idea, I’m fine with that, so this time there was a bit of instant pride in seeing my own personal NZ map centre-stage for the third time. But everything else in the overall layout has taken my idea, and, like Sumeet, come up with something better and different! I tend to shy away from graphics and stylisation too much as it’s something I tend to struggle with, but it making the chart black with the subtle addition of just the Kiwi silver fern, we have a really elegant and NZ-branded visualisation. This confirms to me that it would have been easier than I thought to come up with something better than rainbow scribbles and grey text on a lime green background!

And in showing yearly, rather than monthly figures, we don’t see seasonal variation but we see increase over time more easily, and, whether by accident or design, each individual line chart resembles an ocean wave – perfect for the island geography and wild seas of New Zealand. To be critical – I think the upturn at the end of each line is due to incomplete 2016 figures skewing the averages up, which might need redressing (a point shared by Chris Love, in a further example of collaboration), but the visualisation would still look great if revised. Blue and orange are used to show decrease/increase since 2008, which I think is a better use of colour than dividing North and South island into its regions – used for insight rather than geography.

This might not be a true collaboration – after all, it’s three visualisations that were designed by three people. But the overall process feels like a collaboration of sorts that happened organically – I’m still really pleased with my original but feel that both Sumeet and Sarah took it up a notch and there are definitely elements of all three that I like. To frame it another way, I can claim credit for Sarah’s stylish visualisation which was beyond my means/creativity, because it couldn’t have been done without my tile map! There might well be a visualisation that combines the best bits of all three, or surpasses them all, but the nature of visualising with freely-available open data in a growing, proactive, online community which communicates, offers feedback and collaborates is sure to reap rewards and push standards higher. I’m looking forward to it.

How did I create the spiral chart?

How did I create the spiral chart?

Jump, Darn Dolt!

My latest visualisation is the spiralling chart below this paragraph, showing all the tweets of a certain dolt in anagram form who I don’t want to give any more publicity than he already craves, part of the Makeover Monday project. I’ve been toying with a spiral chart for a while (for example, I created one for my 80s pop data featured in this post, but it didn’t really show anything other than to achieve the goal of producing a spiral, so I never published it). But this time I decided to take the plunge. Uncharacteristically, this time I’ve had a few requests asking how this was done in Tableau. I hadn’t really planned technical posts on this blog – I tend to think that if I’ve come up with something new it must be simple, and if I haven’t it’s because I’ve usually nicked the idea and code from elsewhere (like an artist, of course!) But in this case I’ll make an exception – I’ll try and explain the thought processes behind the visualisation rather than just the instructions to replicate it.

As usual, you can click through to the online version – there’s some interactivity inasmuch as the instructions for reading the chart are behind the question mark if you hover.

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So, before going into the nuts and bolts, perhaps a quick thought process as to why on earth you might produce a spiral chart. What kind of things would I put in this post if I’d phrased it “When might you use a spiral chart?” or “Is there ever a place for spiral charts in data visualisation?” There’s not really a good answer, other than it is a legitimate way of saving space showing data on a very long timeline. If each turn means something (such as a year) then you have a potentially space-saving way of showing, say, nine years of information rather than a very long line, and you can see information from the same time of each year in the same area of the chart. You can’t read information off it particularly easily, but as a simple, fun, visualisation, it has its merits. In my case, I was able to marry the chart to the analysis (using the term loosely) that the tweets are “spiralling out of control” and if you add up each of these factors I think I have at least a few scraps of justification.

Moving on to the chart itself, the first thing to note is that it’s just a scatter plot. There is no connecting line being drawn or path being followed. It just looks that way because all of the circles are so close together (a consequence of this particular dolt tweeting almost every day). So, this is just a plot of Y versus X for every tweet, where X and Y are determined by the date. With 30000+ tweets in the dataset, and 2800 or so days elapsed since tweet one, it’s just 30000+ circles plotted on one of 2800+ possible locations.

The key to this visualisation is obviously the spiral. We’ll design the spiral so that each turn of the spiral is one year (this aligns days/months together and nothing else would really make sense). Spiral plots are like circle/radial plots, except the distance away from the centre of the circle increases the further round the circle you go, and this distance continues to increase with each revolution.

In order to plot the spiral we need to know two things for each date:

  • (1) How far through the year is the given day? This will determine the angle of the plot and how far round the circle we want to plot the data.
  • (2) How far away from the start (first tweet) is the given day? This determines the distance of the plot from the centre (without this, all the plots from each year would overlap).

So we use the datepart function to get the day of year from 0 to 365

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Then we can calculate the number of years since the start of the year 2009 and use this to work out which day of the whole dataset each date represents. First tweet was actually in May 09, but that doesn’t matter, it just means that the lowest value isn’t as low as zero. It’ll still give a sequential number of all the possible dates.

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Next, the angle. We know that each day is 1/365th of the way round the circle. Tableau uses radians, but all you need to know is that there are (2 x π) radians in a circle. So the angle is however many 365ths of the year have elapsed, times (2 x π)

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I should mention, I should strictly be a bit cuter here for 2012 and 2016 which were leap years, so in theory there two days (1/1/13 and 1/1/17) where we might have two days’ data on top of each other, where they coincide with 366/365ths of the way round the circle from the previous year. But for the purposes of this makeover visualisation, I’m OK with that. Just putting out there that I know this should be tweaked for 100% pedantic accuracy. But I don’t intend this visualisation to be used for analytical purposes. To be fair, I didn’t expect it to be open to scrutiny on this blog!

Now, we have enough to calculate X and Y using a bit of schoolboy trigonometry, see below for X:

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Ignore the left hand sides for a minute, but first we want to start by plotting X and Y at the correct place on a unit circle based on the angle value we’ve worked out, with day zero (angle=0) at the 12 o’clock position (X=0, Y=1 (maximum)). Best simple explanation I can see of how to use cos/sin is here:

https://en.wikipedia.org/wiki/Unit_circle

… though note in the Wikipedia example that the angle starts at the horizontal (x-axis). We want to start at the vertical (y-axis) for our zero point. But cock your head and it makes sense – the sin or cos function will give you the correct point on the circle!

Calculation for Y is below …

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For the left hand side of either the X or Y calculation we are controlling the multiplying factor which relates to distance away from the middle. This is based on the overall number of the day in the full timeline, which is what we want. The larger the “day of whole” figure, the more the sin/cos value is multiplied, therefore the further away from the centre it gets. It’s additionally controlled by two parameters: “Inner” and the imaginatively titled “parameter”. Without “Inner” the spiral would start at the origin, but tweaking “Inner” until you are happy with it will affect the size of the central area. “Parameter” controls the spacing of the spiral layers – again, try increasing or decreasing this number to get the lines closer together or further apart.

All you need to do now is plot Y vs X with the tweet ID on the detail shelf, and your spiral is in place. The key thing is that the values of X and Y don’t actually mean anything as such per se, they have just been calculated to put everything in roughly the right place on the screen, and to show your spiral you’ll remove all axes, headers and grid lines. Tableau will essentially plot all points as a ratio, in other words if X is, say, 200 it’ll be twice as far away from the x-axis as if X were 100. And the chart will automatically centre at X=0, Y=0. I settled on values of 50 for Inner and 4 for Parameter, which gives the spiral below. Note how it starts on the inside at about the 5 o’clock position (for the first tweet, May 2009) and spirals out to just after the 12 o’clock position on the outermost ring (since there are 12 days’ worth of Jan 2017 data).

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The rest is all formatting – I decided to go with the number of retweets determining the size, and the tweet method determining the colour. I’ve given the circles a small amount of transparency and a light grey border to allow for the overlapping effect, but obviously the nuances of the visualisation are up to the individual.

I think this was quite an easy one, though I say that as a closet mathematician! Perhaps it’s more accurate to describe as “easy when you know how”. No tricky Tableau functions to be aware of (apart from the favourite trig functions we all remember from school: SIN/COS), main visualisation on one sheet, no dual axes, only a few calculations and no level of details or data densification required. As mentioned above, I might struggle to think of a particular use case where a spiral chart is a perfect choice over and above anything else, but for me it’s just something a bit different which does its job visually but not in an over-analytical way, and given the subject matter of the visualisation, that’s fine by me.

Though if anyone can recreate it using wild spiralling strands of his hair, then I would be seriously impressed!

Exploratory or Explanatory?

Exploratory or Explanatory?

Today’s question is all about how much explanation should be included with a visualisation. In terms of your overall look, should you clarify, or simplify? It’s a topic that often comes up in visualisations, and I think it’s one I don’t always get right.

Earlier this week I revisited an idea I had following the Tableau Conference last year. As part of his talk, Chris DeMartini presented the Jump Plot, a new visualisation type he has devised and documented. Many examples and instructions can be seen in www.jumpplot.com. After a few attempts, I got the hang of this particular chart, and game up with my own version, for Premier League Goalscorers this season (up to the end of week 19 – end of 2016).

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I can take little credit for the design or clever technical work behind the chart, only for adapting what was already out there to fit my dataset, but I’m pretty pleased with the way it has ended up. The jump plot does a good job of showing the range of time/matches between each goal for all the division’s top goal scorers. Click through to the online version and you can see a lot more if you explore. Diego Costa’s prolific and consistent season (all low “jumps”), the locations of hat-tricks (three goals in one game, resulting in two horizontal flat lines in a row) for Romelu Lukaku and Salomon Rondon, etc.

But when I published it, I left out the “number of goals” text, before I got feedback that the person viewing thought the numbers referred to games, not goals. I can concede that adding text makes that clearer, so it’s in. Then I was contacted by Chris, the man behind jump plots, who explained that I should really include a y-axis and faint axis lines to make the chart easier to read and understand.

I think he’s absolutely right, but I haven’t done it! This is an unfamiliar chart type, so it makes sense to give the viewer more guidance than I’ve given. Show them the axis and grid lines, so there is more meaning to the size of each jump, without relying on the viewer to find out for themselves. But, I’ve designed it so that my information is on the tooltip when the viewer explores the chart. And I want the viewer to explore the chart, that’s the only way they’ll activate the highlighting and/or see which bump refers to which player. I haven’t overloaded the chart with instructions, but just made sure to include the instruction “hover over any line to learn more”.  I think in best practice terms I’m probably wrong to do this without offering more viewer guidance. But I don’t always follow best practice, and I know that I err on the side of exploratory (viewer find out for yourself!) over explanatory (viewer, this is what I’m showing you). There’s personal style behind my stubbornness. My jump chart above hasn’t been active for very long, so I’d welcome opinions/comments/feedback as to whether it needs more explanation!

Moving on, in the last couple of days I’ve been compiling a great dataset from all the top 75 singles charts in the UK over the 1980s. I couldn’t quite get the data scraping process to work, so have been doing much clicking, copying, pasting and data wrangling to get my dataset. Of course, in doing this, the names of many 80s songs and artists have passed through my field of vision and I’m looking forward to the data visualisation possibilities lying ahead. It’s an exciting dataset, almost 40000 rows, each of which is describing an 80s song and artist, and should be a source for a wide range of possible visualisations.

I’ve loaded the data into Tableau, set up a simple visualisation pretty much out of the box, to show line charts of every song released, coloured by artist, showing a mass of lines to cover ten years, positions 1 to 75 on the charts. It was only done as a validation exercise at first to see if I had any duplicates or blanks showing as outliers (I did – so a quick correction and reload sorted the issue). Instantly, with a simple title added, I had this:

 

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You can’t get much more exploratory than this. No instructions, nothing visible, just two axes and a mass of colour. It’s a mess. As a work of art, some might say it resembles Pollock’s (careful …). But I love it – I didn’t get any further, and as I blog about it, I almost don’t want to go. As it stands at the moment, hover or click anywhere, and a song emerges, along with its individual path and position within the timeline of the 80s. The tooltip will tell you the song, chart position, artist and date. As simple as that.

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Above is an example (which shows better in full-screen – click above to see in a new window) – the trace of Soft Cell /  Tainted Love shows it rising up the charts to number 1 in 1981, but unusually shows a couple of resurgences in 1982 and what looks like a possible re-release in 1985. But the whole screen is like a dense jungle waiting to be explored. Find “Relax” by Frankie Goes to Hollywood and you can see its trace shows 58 successive weeks in the charts (over a year). Find “Merry Xmas Everyone” by Slade and you can see regular pulses every twelve months. Use the Tableau lasso function to select a chunk covering one year and see the number of songs that had surprise re-entries later, or which were re-releases themselves. Click almost randomly and you’ll find songs you’d forgotten all about, or which had much shorter but individual imprints in the DNA of the 80s pop timeline!

Now this is the ultimate in exploratory visualisations, and I couldn’t possibly release this for everyone. It even looks like a dense jungle – the ultimate in exploratory environments! Data nerds like me who loved the 80s and have time on their hands don’t need more than this. We don’t need to be told where to find songs and bands, but are happier stumbling across them or honing in to them using our memory of where and when it charted first time round. Because this is a project with data that I’m enjoying, I’ve done enough for myself. Now, I will do something more with this – make it more appealing and accessible in some way, because in considering exploratory versus explanatory visualisations, we should always consider who our audience is. I haven’t yet decided who the audience is, and currently the audience is just me (or people like me). But, if I don’t like the finished and more polished visualisation I come up with, it won’t see the light of day.

Recently, Andy Cotgreave (I keep mentioning him – he doesn’t really need the publicity!) wrote an article here about the place for punk in data visualisation: I don’t think he meant it literally (given that this starts only just post-punk, with the likes of Stranglers, Clash and Boomtown Rats still featuring in this chart), but was referring in general to quickly-produced messy and ad-hoc visualisation. The upshot of the argument is that it doesn’t need skill, presentation and flair to tell a story or present data. I think we can safely say the above falls into those categories.

I don’t conclude from this that exploratory visualisations are better than explanatory ones. In many cases it’s a weakness of mine that I don’t explain enough, either to guide the user to navigate the visualisation, or to spell out the most pertinent points that the visualisation shows. As a style and preference, I must admit I lean towards exploratory visualisations for personal projects, as they more closely mirror my own taste/preference as a user. But part of my learning journey as a practitioner of visualisations is using all kinds of approaches and knowing when certain approaches are acceptable and when they’re not.

 

What were my data visualisation highlights of 2016?

What were my data visualisation highlights of 2016?

I’ll be honest – I don’t for a minute believe I have the standing, expertise, or width of knowledge and experience to comment on data visualisation generally in 2016 (or, similarly, to make predictions on future developments for 2017). But 2016 was an eventful year in my progression into the field of data visualisation. So I want to share that with you in this post. Looking back, it feels like I made great progress in 2016, from rank beginner who had just started in a new job which would require a small amount of data visualisation, to a potentially much more exciting position within the field (read on) with a lot of progress, recognition and learning along the way. I hope my 2016 can influence or inspire new people in the field for 2017. So, here goes:

1. The Truthful Art (January)

At the very end of January 2016, Alberto Cairo published his latest book: the Truthful Art. It’s a great book which gives any newcomer a great grounding in the field, and I’d recommend it for anyone’s bookshelf. In short, buy it. Read it. But that’s not where its relevance for me ends. At the end of 2015 I took part in an online course through the Knight Center for Journalism in the Americas – run by Alberto and Scott Murray, it was a great introduction to Data Visualisation theory and practice (Alberto) and introductory d3.js (Scott). But it was this tenuous link with Alberto which made me reply to one of his tweets right at the start of the year, asking for proof-readers for the book. So for me, I got a chance to read through the book in full ahead of time and offer my thoughts on pedantic English to poor Alberto.

1truthfulbroken

My contribution is rightly minimal to say the least – a very small mention in the acknowledgements and the knowledge that without me you might have had a quote by Ronald Dumbsfeld rather than Donald Rumsfeld. But it made me feel part of the data visualisation community and made me realise how open and inclusive it was. A particular thank you to Alberto for that (and his efforts to get me a copy of the book in thanks). If I wasn’t hooked on data visualisation right at the start of the year, I was by now.

2. Makeover Monday (February)

Makeover Monday gets a lot of mentions on this blog, and I explain it more fully here. But although the project started at the first week of January, I made my first contribution in week 7 (during February). From this point on, I had at least one project to work on, publish and put online every week, and a growing group of fellow contributors doing exactly the same thing. Follow the link from the banner below and you might find some of my work but you’ll need to find it form among well over 500 other authors!

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And now, as I write at the end of December, I’ve completed week 52. All done, with no gaps, and I’ve even been keen enough to go back to do those first six datasets in an effort to reach the end of the year in the elite 100% club! The project has been instrumental in improving my Tableau skills and getting my work out into the public domain, and looks sure to continue through 2017 with the addition of Eva Murray to the team.

3. Climate visualisation (May)

This is where things started to take off for me – my visualisation on the spiralling temperatures of 2016 (below) was made Tableau’s Viz of the Day (and, a week later, Viz of the Week). Now there were a number of brilliant visualisations this week, many of which I think were better than mine which was still simple and within my capabilities. I don’t even like it very much (the lower and upper CL bands add nothing, and why did I not fix the white axis in the median chart?). But I admit it’s got a nice unintentional heat-haze look about it. Somebody else somewhere liked my contribution, and hence I got my first noticeable recognition for any of my work.

Dashboard 1-2

4. Questions in dataviz (May)

So once my work was starting to be noticed, people suggested I start a blog. There’s no tricky Tableau in my visualisations above, so I didn’t feel I could add much technical advice to the large community of experts out there. But I can write and waffle on a good day, and I love a discussion or point of view on general issues, and so this blog was born. Some posts get a great readership count, others less so (does nobody like German psychology?!), but generally I’ve had some encouraging comments about this blog.

So, it’s here to stay! I’ll continue to post updates/topics and questions that are not too technical or Tableau-specific, but would love any suggestions or comments from you, my readers.

5. Brexit (June)

Ah, Brexit. The Brexit result itself wasn’t exactly a highlight of 2016. But, along with the US elections later in the year, (ah, the US Elections – the US election result itself wasn’t exactly a highlight of 2016 …), it was a ripe source of visualisations which understandably held the data visualisation community’s interest. I brought out my Brexit visualisation, not just because I wanted to create something that looked good, or that learnt a new skill, or to demonstrate anything else, but genuinely because I wanted to use the visualisation as an end user. To see, play with and try and understand the results (let’s face it, they weren’t easy to understand!). My visualisation, below, is the one many people have said was their favourite of mine in 2016. It was also another viz of the day!

Dashboard 1-10

Tableau have also featured this in their review of visualisations of the year: https://www.tableau.com/about/blog/2016/12/vizinreview-share-your-favorite-vizzes-2016-63852

6. Tableau Torch (August)

By the time August came along I was using the brand new beta version of Tableau v10. Not a big deal, as Tableau are great at encouraging active users to take part and feed back on beta versions. Tableau Torch was a chance for anyone to enter a competition with a theme – demonstrate one of the new features and win a ticket to the annual conference in Austin 2016. I know I’m not up to the standard of their annual Iron Viz feeder competitions, but this seemed like it was worth a try, with my love of scatter plots (see above) and interest in what Tableau could do with the new clustering features. Sticking with the Olympics theme, I came up with the visualisation below. To my amazement, a winning entry. I was going to Austin, Texas!

Dashboard 1-22

7. Malaria in Africa (September)

In September, our Makeover Monday dataset looked at malaria over recent years in Africa. I decided on an approach with its own pros and cons, but one I had never seen before, namely an Africa tile map (which I blogged about in much more detail here). The beauty of publishing online via twitter means that anyone can see your work, and before I knew it a French magazine was interested, they contacted me, we re-worked it a little (just by using more publication-friendly fonts and colour schemes, and obviously translating into French), and voila – I was in print!

Over the course of a couple of months, I’d won an a award, won a competition and been published. It was starting to feel like I couldn’t convincingly pass all three off as a bit of luck (though I must admit I still hold that view!), and that data visualisation, using Tableau, was starting to take off for me.

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8. Culture and Politics of Data Visualisation (October)

I’d been implementing my growing interest for, and confidence in, data visualisation in my work job as much as possible. A few new dashboards had been produced, a few new chart types had crept into presentations, and I felt that as things were progressing nicely on both fronts (in and outside of “work”) that I’d be happy to present at a conference. The opportunity came up to talk about some of our formative evaluation work at a one-day data visualisation conference in Sheffield (“The Culture and Politics of Data Visualisation”). Now I’m no keynote speaker, but my talk combining and showcasing our professional uses of data visualisation in formative evaluations, and explaining how any visualisations, however simple, acted as a call to action for our clients and stakeholders seemed to go down well. My audience was about 40 or 50 people, and consisted of artists, PhD students, visualisers and analysts. But it was another landmark for me – I was confident enough in my role to present my thoughts and findings.

Later in the year I was able to present research and simple visualisations from one of our work projects to a much bigger audience of about 150-200 people. Through involvement in data visualisation my opportunities to talk and present are growing, and I’m happy for that to continue into 2017.

9. Tableau Public featured author

Just before travelling to the conference in November, I got a great surprise. The Tableau Public team made me a featured author. Now this is something that gets awarded to ten or so people at once, and is rotated every few months (I’m not sure exactly for how long I will stay featured). But this has resulted in a few more views and followers on my public projects, and is a great way to showcase what i have been doing this year. It’s also further great recognition for my work for which I am incredibly grateful. If people really want to, they can get straight to my public work with one click … and if you really want to you can click below.

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10. Tableau Conference (November)

Of course this was a highlight of the year – a full week immersed in Tableau, learning about data visualisation and meeting community members and experts from all around the world.

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I’ve blogged extensively about this in these two posts:

What did I learn from Tableau Conference 2016? (part 1)

What did I learn from Tableau Conference 2016? (part 2)

11. Interview (December)

In December I went for a job interview. I’ve loved my job over the course of this year but really wanted to take advantage of my growing exposure to (and improvement with Tableau). So, though not actively looking for new opportunities, I saw an option which looked too good to ignore. It felt like the culmination of my great year in Tableau – a chance to sell myself, my abilities and my visibility within the community. After a technical test (which, to be honest, I think could certainly have gone better) was the standard interview panel, during which I was asked to demonstrate one of my visualisations. Ahead of me was a laptop projecting in full onto an empty wall.

Going into Tableau Public, via my featured author page (see above), it was great to be able to wow the panel with the sheer variety and volume of my work. A few times recently I’ve heard people recommend the use of their Tableau Public profile as a portfolio to show of their work. I can’t agree with this strongly enough. Building a portfolio with a combination of Makeover Monday projects and my own experimental projects

tableau-public

In short – I got the job and start in late February. Much more data visualisation focussed than my current role (in particular, using Tableau), it’s an exciting role (for me) where I can be a user, trainer, mentor, evangelist and possibly more. I won’t go into too much detail about my new job since I still have two months to work at my current position, which will get my full attention through January and most of February 2017. But I’m excited about the new prospect and it’s sure to be an important step in my continuing journey for 2017 (cringeworthy cliche alert)

12. Sports Viz of the Year (Dec)

This feels like a nice way to round off my review. My favourite viz (of mine) of the year hasn’t featured anywhere above, but it was featured near the top in this review of the top ten sports visualisations of the year:

http://www.minto.net/2016/12/the-10-best-sports-graphics-and-data-visualisations-of-2016/

And the viz itself, of course, is this one:

Premier League-2

I wanted to include this because so much of what I’ve done has been reviewed, liked and publicised within a relatively small closed circle. But this has been picked up and included from outside, in a different sports visualising community – with the exception of the malaria visualisation above, that’s still quite unusual for me. A great way to end the year!

Next Year

So what’s in store for next year? Who knows?! However these are just some of the plans/ambitions I have the following for next year:

  • Take part in January in this MOOC course
  • Speak at another conference
  • Attend the Tableau Conference in the US again (somehow!)
  • Continue to meet and network with all those who have helped me in my progress so far (and meet those I have yet to meet!)
  • Continue growing my output through Makeover Monday
  • Work hard on my technical skill in tandem with the skills needed for my new job (perhaps become Tableau Certified)
  • Keep blogging!

There are just too many people to thank and mention for their help and encouragement this year,  but I hope to keep working with all of you next year. See you all in 2017!

What does data visualisation have in common with psychology?

What does data visualisation have in common with psychology?

A few weeks ago I was preparing some simple bar charts for a presentation. I was presenting demographic results for three separate cities, so I’d decided to give them each their own colour. But I just sought clarification on the best way to group the data. What I decided to do was something I should do a lot more often, which was to sketch out the possibilities.

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So I came up with two ideas. Assume each colour is a city, and each option is one of three age groups. Do I show the age groups within each city (colour), as has been shown in C1/C2/C3? Or do I split up the colours and show the cities within each age group, as shown in T1/T2/T3?

I thought about my dataviz principles and was pretty clear the former option would be best. But I sought confirmation from my colleague, Guy, next to me. He agreed, keeping the age bands within each city (the first option) was best, and would allow easier distinction of age profiles (hence drawing the arrow). And then, as I turned away to fire up Tableau, he said something along the lines of – “Yes, that’s consistent with Gestalt theory”

Now I’ve churned out a few visualisations and read a fair few books, but had never heard of Gestalt theory. A quick google confirmed them as a school of German psychiatrists in Germany in the 1920s. Immediately this interested me – the multi-disciplinary nature of data visualisation is one of the things that fascinates me. When I gave a presentation at Sheffield University earlier this year, few of my audience had traditional data science or analysis routes into data visualisation. Many came into the field from traditional or digital art backgrounds, some were statisticians, some were students/theoreticians, and some came from altogether different backgrounds. So, is there a link to psychology?

My first look into German psychology was a common mistake – I ended up here. Are principles of data visualisation driven by the great German intellectuals through history? Kant, Schopenhauer, Beckenbauer(!). Nietsche et al?

german-philosophers

Wrong – it’s German psychologists I need, not German philosophers.

Gestalt, in German, refers to the principle of perceiving something (or, in our case, visualising something) by considering its individual parts as having different characteristics to the whole. The word itself most closely means “organised whole”.So, you might describe a tree by describing its trunk, its branches, its leaves, its blossom, its inhabitants. But if you were to step back and look at a tree, you would do just that and consider it as a whole, not necessarily the different parts we just mentioned.

Today, knowing I was going to write this blog post soon, I came across this post by fellow data visualiser and friend Eva Murray, who writes about her route into data visualisation via psychology here:

As a direct example of linking an interest in psychology with an entry data visualisation, I’d have put this higher up in the blog. But, she also specifically mentions Gestalt theory, so I’ve now come across this idea twice now in relation to data visualisation. So, perhaps we’re on to something here! Somewhat dauntingly, she mentions that her father studied it at quite some length for three years, so it doesn’t really feel right for me to go into any great detail here, being only a data nerd with access to google. So, while I will go into it a little bit, I strongly encourage you to read, explore or study further if anything whets your appetite.

But given that Gestalt refers to visual perception, it must have parallels in data visualisation. And it seems it does. Six principles are listed below:

  1. Proximity / Contiguity: – this is the principle shown in the handwritten example above. Things that are closer together will be seen as being grouped together
  2. Similarity: – things with the same characteristics tend to be seen as being grouped together. Colour is a good example of this – also shown in the example above
  3. Common Fate: – Things that move together are seen as being grouped together. The most common example of this seems to be a flock of birds: from a dataviz perspective this could refer to animations but also elements which seem to be tending in a similar direction
  4. Good Continuation: – We can perceive groups such as points in lines even if they are intersected – if two lines intersect in an X form, we see the groupings as two lines crossing rather than four separate identities meeting
  5. Closure: – we perceive closed shapes as a grouping, or near-closed shapes which we perceive in a group shaped as the whole shape.
  6. Area/Symmetry: – included for completion, this seems to refer to completely overlapping groups, where the uppermost group is seen as a whole, and the “underneath” group as background.

I haven’t backed this up above with any images, as that would just mean copying someone else’s post. But I think it’s important to look at a visual example to understand the principles a bit further. So instead I’ve just knocked up a five minute incomplete viz on a dataset I’m working on, to see if I can see or recognise any of the principles above. Below is a map of police killings in the US from 2013-15. Each circle represents where the killing took place, and each colour represents the victim’s ethnicity.

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And straight away, I think I get it. We don’t want or expect people to look at this and see a map of the USA. We see individual states. We see areas rife with crime and we see areas where the victim count is sparse. We see clusters of red and orange packed together in a Florida-shaped wedge, and a light blue mass around about the Carolinas (excuse my geography if I’m a little bit out). We see the proximity of circles forming the shape of California but with less consistency of coloured circles. We see a couple of orange clusters on the Mexican border.

The areas rife with crime – dots packed together – are principle 1 (proximity/contiguity). The colour distribution, whether close together or otherwise, are principle 2 (similarity). Perhaps the mass of red along the east coast of Florida is an example of 4 (good continuation) – a proliferation of black victims (red dots) which we can see despite interspersal of orange (Hispanic) and light blue (white). Principle 5 (closure) – the states of California and Florida are pretty recognisable even though there are small victim-free gaps.

In recognising Gestalt principles at work, it enables us as designers to notice and work on stories in the data. So, I think we can learn a lot from German psychiatry. (Not German philosophers – after all, as any Python fan knows, they lost 1-0 to Greece).

 

 

Is there a place for chord charts in data visualisation?

Is there a place for chord charts in data visualisation?

I have a number of dataviz questions I still want to cover, but this has usurped a few others and moved right to the top of my list for the moment. The debate focuses around this chart here, published at http://www.global-migration.info/

chord-diagram

You can probably skip to the concluding statement of this post (which I haven’t yet written so genuinely don’t know what it’ll be) to predict that there’s going to be an element of “it depends” in the final conclusion. The question about chord charts themselves is a very specific example of a more general debate about simplicity versus complexity. The chord chart is certainly seen as a proxy for “complex” and this post is really a specific example of the continuing debate between simple and complex.

I’ve referred to Chris Love’s talk before here where he refers to the acronym “KISS” for “Keep it Simple, Stupid”. His talk eloquently and passionately shows the importance of keeping visualisations simple, while also acknowledging the time and pace for flair and complexity. There’s even an example of a ridiculously over-complicated Premier League visualisation in his talk from an up-and-coming visualiser who was really just using complexity to show off what he could do … but I digress. Chris has a love-hate relationship with the Sankey diagram. While his own work and the documentation of how he has achieved it has made this complex chart a lot more accessible to those who want  to use it (in Tableau), he is increasingly frustrated by the proliferation and over-use of the Sankey, often by people using his own method.

We don’t agree on everything – I generally love a good Sankey chart, perhaps because I pompously think that I have a reasonable understanding on when it’s a valid chart type to be using to show insight, so I don’t shy away from using them, even professionally. Below is an example of one I have created and used (ironically, adapted from Chris’ original excellent code).

sankey-grouped

Please note I’ve deliberately cropped out labels and explanations here, and haven’t included a link to the interactive version. The reason for this is simple – this is a client project which I’d prefer to leave anonymised. The chart shows the change in rating scores for particular measures between the first sample point (left) and second point a few months later (right) But I have a version of this with explanations, filters and drop-downs allowing the user to choose all manner of options, hover and interact, and when briefed to users it works really nicely.

Simple versus complex continues to be debated. Last week, in an excellent webinar (hosted by Tableau, using the #datadebate hashtag), Andy Cotgreave of Tableau and Andy Kirk of visualisingdata.com debated briefly on the nature of simple versus complex visualisations. Andy C put a good case for the simple visualisation – it’s more important to be able to dive in and get to the numbers without having to work hard for them. But in a detailed reply, Andy Kirk explained that a good visualisation should be able to assist and coach viewers through even the most complex of visualisations, so that they in turn shouldn’t be shied away from. Two great soundbites emerged which gave great weight to the argument for “complex” over “simple:

  • “Everything is new once”
  • “Don’t simplify, clarify”

In other words, maybe it’s better not to lose function/complexity just for the sake of simplicity, when it can be left in if effort is made to coach or clarify?

So, on to chord charts. Conversations rumbled on as a result of the simple/complex debate, with the upshot being that the chord diagram at the top of this post was selected for this week’s MakeoverMonday project. I’m sure I’ve mentioned Makeover Monday before, but I mention it in a lot more detail here. Can the community make over / improve an initial chart to produce something new and potentially better?  I’ve never created a chord chart before, and to be honest I wouldn’t know where to start. But for a makeover, that doesn’t matter, and my attempt is below (click to access interactive version)

dashboard-1-44

First, some context into what I was trying to do. When Andy Kriebel (are you keeping up with all the Andys?) posted the original chord diagram, a quick chat confirmed what I suspected – he doesn’t like chord diagrams! I could certainly see his point – they might look great, but how easily, if at all, can we get real insight from the chord? Is it not just showing a lot of people moving around and not much else? I could concede that it was one unnecessary step beyond a Sankey.

They require quite an investment of time and even then do not often show a great deal of insight. So my plan was to show something that didn’t need much explanation, which showed trends over the four data points 1990-2005, and which showed the nature and magnitude of net migration flow from region to region.

I think I’ve done OK (but, just “OK”). My small multiple charts do show trends in each pari of regions, and my use of blue/orange (another hot debating topic which I will avoid for now!) shows the asset I’ve tried to focus on most: net immigration / emigration for each paired region combination (though this needs explaining in the text). But in sticking to consistent axes throughout, almost all trends are “flatlining”, dwarfed by the large net flows from Africa and Latin America to Europe (which, to be fair, ultimately is the main analytical take-out from the visualisation). The blue/orange shades are otherwise indicating little more than a net positive/negative flow. And the image itself, though I’ve tried to make it neat and clean, is still quite crowded and not overly striking.

On reflection, I’d rather invest ten minutes in the original chord than one minute looking at mine. Granted, it’s not until looking at the chord for at least the fourth time in compiling this blog post, that I’ve even noticed the additional functionality to split regions into individual continents. A lovely addition which isn’t easy to spot if you tend to just dive into visualisations, but I see that as more of a fault of me as the viewer than one of the visualisation. Mine has no individual country breakdowns. So that’s a negative for the chord showing how much time you need to spend to realise all the potential insights, but a positive indicating how much information is involved once you’ve found it!

So at this point, I’m a fan of the original chord visualisation but the jury is still out for me. But the plot thickens for me in the publication of the makeovers from the project’s leaders: Andys Kriebel and Cotgreave. A fantastic element of the Makeover Monday project is that the two organisers will always post a short accompanying explanation to their thought processes behind their reworks. See below, in that order, and click here (for Andy Kriebel) and here (for Andy Cotgreave) to visit their accompanying blogs and explanations:

 

This is music to my ears! The two experts leading the visualisation community I owe so much to have differing views – and whether or not it encourages me to either take an opinion or a balanced view, it’s certainly great news for a novice who runs a blog based on debated opinions in dataviz. I don’t think I’d be involved if there was only one accepted wisdom, one way through the profession from beginner to intermediate through advanced to expert. If that were the case I’d make it to a certain point only, limited by skill, opportunity and technical ability.

It’s the dataviz equivalent of loving Kandinsky and hating Picasso, or admiring Bach and being indifferent to Radiohead. By glorious coincidence, I’m publishing this within seconds of being notified of the Turner Prize winner – a choice which always courts controversy over what is “good”, or even what is “art”, and visualisations can be just a divisive for similar reasons.

In summary: here are the contrasting things that both said in their blogs:

Andy Kriebel:

  • Chart is way too busy
  • Can’t see trends
  • Some of the colours are too similar
  • No order to the regions
  • Difficult to compare different arcs
  • Easier to present as heat map to show region pairs with highest movement
  • Including countries over-complicates the visual

Andy Cotgreave:

  • Designers have explained it well
  • Initially looks a confusing mess, but comes to life/becomes clearer with interactivity
  • Best way to show so much detail
  • Andy C’s own makeover is a flat admission that original can’t be matched – simple approach to makeover doesn’t even try, and loses detail
  • Investing time to read chart is not a reason in itself to avoid using
  • Using width to show range of measures ensures outliers aren’t dwarfed

Enough about them – what do I think? While not disagreeing with any of the points above, I’m leaning towards the Cotgreave way of thinking in choosing an opinion which is pro the original chord diagram and not anti it, albeit just in this particular specific example. But let it be noted that this was the same man advocating simple over complex in an earlier debate! It might be that I’m fickle, most influenced by the most recent well-presented opinion I’ve encountered, and that another viz, opinion or well-reasoned article persuades me further in the opposite direction in the future, and I don’t mind that at all.

But I’m attracted to complexity, something striking, engaging and different. As Andy C mentioned in his blog, the original chord diagram was presented in the Graphical Web conference in 2014. It’s highly unlikely that any other visualisations on this page will be showcased at a conference! Now I do know that the definition of a good visualisation is absolutely not the same as a visualisation that gets displayed as a talking point at a conference. But it does coincide with the kind of visualisation I like to explore, discuss and blog about. It’s back to the cliches of art again – “I don’t know if it’s art, but I know what I like …” And if it inspires me to explore, with a little bit of help directing me how to do so, then I’m in my element. I can always back myself to understand and engage with a visualisation if it’s well-designed, well-documented or both. Ultimately these are the kinds of visualisations which have piqued my interest in the field in the first place.

Mind you, when it comes down to it, it depends really …