So, what were the chances of that?!

So, what were the chances of that?!

This weekend (I’m writing this on a Tuesday evening) there’s the deadline for entering the second feeder contest for Iron Viz. Iron Viz is one of the highlights of every Tableau Conference, where three winners of the feeder contests perform on stage in front of 12000 or so people to produce an amazing viz in just 20 minutes. Possibly the most ridiculous display of nerve and talent you’ll ever see in the world of Tableau, a great spectacle that leaves those watching in awe of the three competitors.

To take part on stage would be absolutely out of my league, but there are still good reasons to enter. Firstly, the task of pushing myself to produce a competition-quality visualisation that wouldn’t seem too out of place in a field of 60 or so competitors will be a great experience. And secondly, if it was possible, just possible, to win the feeder, it would be a free ticket to this year’s Tableau Conference in Vegas, which otherwise I am highly unlikely to attend. And as I mentioned here, and here, it was rather good last year, to say the least.

And thirdly, the Safari theme suits me. If you have to find a dataset to work with and spend a long time on creating a viz, then I would always find something you enjoy or are passionate about. I have long been passionate about wildlife conservation, and so when I was browsing the Environmental Investigation Agency website and found some data about rhino horn seizures, I knew I had the start of what I wanted to to. I was about to enter my first Iron Viz. In fact here’s a photo of me from seven years ago … (and yes, the medal proudly displayed on my horn is a London Marathon completer’s medal)


I wanted to challenge myself in several ways. I wanted to emulate some of the best Tableau Public authors around, to challenge myself in the use of more artistic layouts. 100% floating tiles, transparent images to act as backgrounds, use of photo images, long form infographic style, circular insets to name a few things. I wanted to emulate the master of such visualisations, Jonni Walker:

Dashboard 1-107

Jonni’s viz above is one of many he has done on different animal and bird species. It’s so good, as well as looking like it has come straight out of the pages of a magazine, it actually made me want to go out into Derbyshire and look for kingfishers. We talk about a good visualisation having a call to action. There’s no more literal call to action than that!

Fast forward to last Sunday. My visualisation was making progress. I’d got most of what needed including working reasonably but still with plenty to do. And then, a stunning early entry from my good friend Ken Flerlage dropped on Monday. To emphasise, my version below is far from finished! But, see if you can spot some similarities and differences:


So here goes. Ken’s is amazing, mine is unfinished. Ken’s is black, mine is beige. Ken’s has a hard-hitting message, mine includes the same issue but doesn’t tell the full story, instead with more generic rhino info. Ken’s has great text annotations mine doesn’t (yet). Ken’s is polished, stunning and deserves to win … mine, well you get the point But we have both gone for exactly the same story and the same issue. We’ve even used exactly the same datasets from exactly the same sources. We’ve quoted and recommended almost exactly the same charities. We’ve both gone for circular insets and long form dashboards (of exactly the same length!). Given the only guide was “Safari”, it’s uncanny how similar our submissions are.

Ken’s blog is here – it includes some shocking imagery but I make no apologies for that given the subject matter:

The image on the right is exactly how it was the moment Ken got his early entry in – I mailed it to him to show just how close our thinking has been, and so it wouldn’t look overly suspicious if I entered at the weekend with such a similar idea. So what do I do now? Anything I do will either seem inferior or look like it’s copying. I know mine is far from finished – I need to decide what to do at the bottom, I need to sort out text and annotations, I need to finalise the story, I need to update tooltips and interactivity. And I’m not overly happy with the distribution map, since the specks for the  Javan, Sumatran and Indian (greater one-horned) versions are barely visible. A lot still to do, but now I know there’s such an obvious yardstick!

It doesn’t feel right to enter now. I will finish the viz – otherwise this is too much of an excuse not to. And I entered because I wanted to try my first ever viz of this type and highlight an important issue. Thankfully Ken is highlighting this better and more powerfully than I could have imagined! But if I do enter, it will seem like I am trying to outdo Ken’s viz, and I don’t think that’s even possible. I’ll think about this. If there are things I do that are slightly different (do I focus more on the map and the information on individual instances, do I include a video, for example?), is it because I’m trying to gain an edge? If I focus on things that Ken has included too, is this just imitation?

Ken (sorry Ken, you’re getting a lot of mentions here!) is one of the nicest men I know in the community and has often been very supportive and appreciative, so I know he will think there is nothing funny going on. In fact, he’s encouraged me to enter, despite us both having no idea we were working on identical data and issues!

Incidentally, a couple of weeks ago, a fascinating blog was published on the subject of tile maps and maps of Africa, using the recently published data of world countries’ Internet usage.

It was Ken again! Ken has done some amazing work considering many more regular and irregular ways of representing Africa using tile maps (the link to his blog is further up this article). Only during the last phase of his work did he realise that someone else was doing similar work and blogging about this; er, me – here and here! But he didn’t mind at all and nor did I.

The coincidences continue, perhaps they’re not *quite* so coincidental if you consider that there are two of us out in the blogosphere who are both fascinated by Africa, its maps and its magnificent endangered inhabitants. There is certainly room for the works of two keen data amateurs to explore different ways of visualising the continent of Africa, so perhaps there is room for two takes on the same important issue, if only because it gives twice as many opportunities to highlight the organisations that are doing great work trying to combat crime and poaching in this area. Ken’s been kind enough to call me out in his blog and I’m delighted to do the same for him.

I know who I want to win iron viz! That said, as always I’m excited to see the amazing standard of entries and looking forward to many more. I just hope there are no more on rhino horn trafficking, that might just be one too many …



Why do we visualise using circles?

Why do we visualise using circles?

I should clarify – if the books of Tufte and Few are your visualisation bibles then you may not enjoy this post. Rightly so, the two authors I’ve just mentioned are seen as the foremost proponents of the field of creating professional, analytical dashboard visualisations.

But for those who enjoy data visualisation as data art, who appreciate the aesthetics of data visualisation over and above the rigorous application of best analytical practices, then, like me, you’ll know that the data visualisation field spreads far wider. So I was excited to see the publication of a new book from Manuel Lima, the Book of Circles.

I have done a lot of visualisation using circles – more than I’d realised until I came to contemplating this book and the review, with all of the below featuring in my portfolio, visualising subjects as diverse as football results, 1980s music, Donald Trump’s tweets and animal counts in a zoo inventory.

To summarise this book I was able to read it from cover to cover on a long train journey, which means that (a) it wasn’t too text-heavy that I couldn’t complete in a couple of hours and (b) I was captivated, and once started didn’t want to put it down.  If you don’t want to read further than my overall rating, then my key takeaway is that as a result of reading the book, I wanted to do three things:

  1. Read it all over again
  2. Go away and do some (more) circular visualisations, inspired by those in the book
  3. Recommend this book and tell everyone how good it is.

And I’m doing these three things right away, starting with number 3.

The first thing to note is that this leans more towards a “coffee table” book than a technical reference book. The majority of the book reads like a gallery, with full or half-page high quality visualisations and gallery-style descriptions. But, despite this, strange to say that my favourite page is the page below:


The contents page “taxonomises” circle visualisations into seven different types, with different sub-types within them. Instantly from this I can reference those that i’m familiar with and those less familiar with, and can see potential ideas for adapting work I’ve done to fit a new circular visualisation type. It means that if you want to derive inspiration from concentric ring visualisations (for example), you know just where to turn. Though I challenge you not to be drawn in to all the other visualisation types in the book in the meantime. I do wonder though, which one would best represent my concentric circular bump chart? Maybe an idea for volume two?!

The first fifty or so pages are just the right amount of theory to discuss the aesthetics and psychological use of circles over the years before launching fully into the visualisations for the remainder of the book. But these introductory pages still contain many more examples of circles through the ages. Particularly relevant is the explanation of the metaphor of the circle in four ways:

  1. Perfection (simplicity, balance, harmony, symmetry etc)
  2. Unity (wholeness, completion, containment etc)
  3. Movement (continual force, rotation, cyclicality, periodicity, etc)
  4. Infinity (eternity, immensity, etc)

Lima’s examples throughout the rest of the book span art, astronomy, biology, cartography and of course the more recent notion of data visualisation. Allowing for coverage of such a wide range of genres, he intersperses ancient, classic and modern examples throughout to show how our concept of circular visualisations has lasted over the centuries. I’ve toyed with the idea of picking some favourite visualisations from within the book to show you but I’m not sure that’s fair on the original artists. Beauty is obviously in the eye of the beholder and if you read this while leaving preconceived ideas of data visualisation rules to one side, you’ll see several hundred eye-catching visualisations.

Instead, I’ll leave this circular image – the one chosen for the book’s cover, and suggest this would make a  fine addition to the shelves of any data art enthusiast


Do we publish visualisations just for the attention?

Do we publish visualisations just for the attention?

A week or so ago I wrote three-quarters of a blog post about this, inspired by the attention that my Africa tile map viz was getting. I ran out of time and “parked it”, heading off to the Tableau Conference in London

To summarise my first (unpublished) post: I compared a data visualiser publishing her personal work online to a (traditional) artist displaying her work at a gallery. An data visualiser’s collection of work on his blog, website or Tableau Public profile is like an artist’s portfolio. I was pretty sure I had a good analogy going, but that’s what it was, an analogy. And it was an analogy that meant i was prepared to admit that yes, I do publish visualisations for attention, and this contextualised it nicely for me.

The portfolio analogy of the online data visualiser was a simple and obvious one, but a key one that bears repeating. I’ve often felt that a varied and eye-catching portfolio of visualisation work is a huge help when seen as just that – I know of several people whose portfolio, in Tableau Public or otherwise, has actively helped in the process of getting a job (myself included).

But, as I say, I didn’t quite finish that or draw it to a reasonable conclusion. I went to the conference last week, where in the “Box and Whisker Bistro” (one of the many themed rooms in the venue) I saw this:


It’s worth saying, that at my age (and those with a keen eye and an accurate abacus can work out exactly what that is from above), things can come as a surprise even when you’ve had strong hints dropped about them in advance – I remembered there may be such a display, but had subsequently totally forgotten. It’s great – I can watch my favourite films over and again and still not know what happens! Anyway, back to the blog …

So what’s this? It’s a real actual art gallery (for the duration of the conference). With actual art works. And they picked mine, not once but twice. Now, I’ve always loved the idea that for more creative visualisations on the David McCandless side of the spectrum (look away, Stephen Few and advocates) would be great if framed, blown up in size to large installations, and shown as artworks, but I’ve never seen it done. And it’s fair to say, I bloody loved it.

And they’ve even put biographies for each picture (shame about the date of birth) to make it look like I’m an actual proper artist. A nice touch!

Here’s a photo I took of Tableau’s Andy Cotgreave (there were whispers that he’s written a book recently that might be very good – the Big Book of Dashboards and Charlie Hutcheson – a great friend in the community who has quite possibly the best blog for those learning Tableau. Not only does Andy have a great current book, but I believe he was instrumental in the gallery idea. How many chances will there be to meet them both at the same time and get great photographic proof? But instead I shoved them off to the side so I could sneak in a photo of my pictures in situ in the gallery with people looking at them …


So now before I meander onto the rest of the post I should give some context and a big thank you to Tableau. The context: I’m a non-artist. A non-drawer. My artistic talent is off the scale in a minus direction. I came from a school where we had to submit sketches to our art teacher which would be graded 5 points for an A, 3 for a B and 1 for a C. Lowest scores got shamed and put in a league of shame on the wall, with potential detention for those not meeting a certain threshold. (Different times!). My sketches got ticks. An acknowledgement that they were done and submitted, but saved from the embarrassment of a mark lower than C. Not saved from the embarrassment of a place in the hall of shame on the wall though. So that’s some context having been through school last century as “boy least likely ever to get work shown in an art gallery in his life”. So it’s in that context that I sincerely thank Tableau for doing this – it made this middle-aged artistic klutz taking up a new career in data visualisation / data art very happy and proud of his achievements – an unexpected career highlight for which I thank you sincerely!

My pieces were just two out of eleven and I’m honoured to be in the company of the remaining works, see them here:

The remaining artists are Rody Zakovich, Chris Love, Matt Francis, Pooja Gandhi, Rob Radburn, Adam McCann, Mike Cisneros, Ben Davis, Many of whom I have previously cited as my influences, and the rest of whom I look up just as much. Thanks to the biographies, I know there’s a certain irony in the fact that I always look to Rody for inspiration (code for I quite often steal his work, like an artist of course), yet he is a depressing eighteen years younger than me!

So, apart from this being a wonderful ego-boosting surprise example of an analogy taking form in reality, have we answered the original question? My thoughts about publishing data visualisations for attention were obviously more leaning towards publishing (and publicising) online visualisations for all to see – whether on social media, blog posts or areas such as Tableau Public.

There’s a feeling that people are using things like Makeover Monday to make a name for themselves. Or that people are courting attention from new or highly technical visualisation types. Those of us who are not professional data journalists or visualisers (perhaps, like me, they will use tools such as Tableau professionally, but not to produce the kind of work that they will publish publicly) will produce a visualisation, but what then? Do we just keep it to ourselves, or do we publish it and publicise it with a tweet or a posting?

I agree, that’s what people do. I know this, because I do it. For me, Makeover Monday is a great yardstick. Last week, I published my Africa tile map visualisation. And, with no great subtlety, because it’s one I’m really pleased with, I’m going to show it again here.

Dashboard 2-16

Because work published for MakeoverMonday gets published at roughly the same time each week, to the same group of Twitter followers, I know that certain works are getting more attention than others. And whether we court it or not, (positive) attention is the sign of a visualisation that has worked well. I make no apologies for excitedly watching and counting the number of likes and retweets it garnered through the week, because I could draw an obvious conclusion from it: that was a good one, people liked it.

Week 22: Internet Usage in Africa: 125 likes, 28 retweets

Week 21: Drinking Habits of Britons: 12 likes, 0 retweets

Week 20: Youth Employment in Latin America: 27 likes, 0 retweets

Week 19: Car Colours in the Netherlands (seen above, which I’m not sure if I’ve even included in this blog before, and therefore shown below): 44 likes, 3 retweets

Week 18: Sydney Ferries: 23 likes, 1 retweet

Dashboard 1-105

And so on … whereas it’s not a matter of pimping likes and retweets, the reaction of a visualisation in the public arena is as good an indication as any that we have of how well received our work is. I can look at the above, and see that my week 21 submission was largely overlooked. OK – I have to squint through the numbers and it would be much clearer if I visualised it, but you get the point. Week 20 was also somewhat down on my usual figures. I don’t see it as being competitive, but rather see the quantity of interactions as a critique on my work. Weeks 20/21 , could do better. Week 22, you nailed it – you did something good, or at the very least did something that got liked and noticed.

I don’t see it as desperately looking out for likes/retweets and attention from a numbers point of view. I don’t expect anything to “go viral” – I’ve never put anything on reddit and wouldn’t want to. I follow my reactions enough to know that 125 likes for Africa tile maps is my best ever, but I’m fully aware that in the grand scheme of things that’s not exactly a sensation, nor is it likely to get me included in the next Olympics’ opening ceremony. My cousin’s dog, Sprocket, got national media coverage and 3000 likes in a day, and rightly so. He’s the standout picture in this blog post!


The “work/out of work” dilemma is a difficult one – many people use visualisations and hone their skills in a certain direction while using software at work, but often to a corporate standard or a very closed skillset. Publishing “non work” visualisations can take any form – do you hone these skills further to create corporate style dashboards on a variety of open datasets, or utilise your hidden graphic skills to create a work of beauty? But for anyone who does so, who is their audience?

Chris Love wrote here (and he’s not the only one) about the difficulties of finding an audience for personal projects. A new, ground-breaking or artistic piece of work will tend to be published only to your professional or social media bubble. We create visualisations considering the number one rule “know your audience” but for non-client dashboards you can’t determine your audience. I could create a piece all about golf, or pop music, or the Oscars (all of which I have done) but my audience, by definition is not specific to any of those areas. Rather it is those who have chosen to follow me on social media outlets, for whatever reason that might be. So it’s easy to see the frustration, or the futility even, of just releasing work to an undefined audience, with the only measure of reaction or appreciation being in the likes/retweets or equivalent reaction. So sometimes, unless we have a different “way in”, that’s our only option for “non work” pieces. But I don’t think that’s something to shy away from, or deny that it’s what we do.

At the conference, I was asked to give an interview for the Tableau Wannabe podcast. I was delighted to say yes (after all, this is a post loosely related to the humblebrag and pimping for self-promotion, so in for a penny …!) just to give my perspective, and I found to my surprise, that as someone who doesn’t usually say much, that give me a data visualisation topic and I can waffle for England!

I do believe though that if data visualisation is both your hobby and your profession, then the public forum is the ideal outlet for what you create. One of the things that I mentioned, which I have had the most feedback on since doing the podcast last week, was about working on personal projects and working with data you love. It’s already inspired some work such as this by Sarah Bartlett (or, if it hasn’t, and they were going to do it anyway, they’ve been kind enough to credit me). And I think it’s important to work on personal projects, enjoy yourself, learn your skills as you’re doing them, and get them out there! If that counts as visualising for attention, by soliciting reaction, feedback and “likes” then count me in.

Perhaps because I still think of myself as being very much on the steep, early, upward phase of my learning curve, I see no shame in this. We’re lucky to have a thriving online data visualisation community who help fellow participants to develop. And if any of those and my fellow friends in the community are reading this blog post and enjoying it, then I ask ashamedly, feel free to like and retweet the link!



What is a Marimekko chart (and when should you use one?!)

What is a Marimekko chart (and when should you use one?!)

If you’ve read previous blog posts, you’ll know that I’m a fan of different and unusual chart types. If the chart types are visually interesting and striking, but don’t necessarily follow visualisation best practices, then, for me, sometimes, so much the better! I’ve blogged about, and attempted, sunburst charts, radial bump charts, bump charts, spiral charts, chord charts, spark bar charts … you get the message.

But when I recently found out about the Marimekko chart a few months ago, even I wasn’t sure if I liked it. I couldn’t really think of a use of such a chart other than just for the sake of it, and though that doesn’t always stop me, I wanted to be sure I could make sense of a use case for it.

So what is a Marimekko chart? Below is a simple example from, the first example you might come across using a well-known search engine. Essentially, it is a stacked bar-chart which totals to a whole both horizontally and vertically. To this extent, each vertical bar has a different width proportional to the overall sub-sample.


But this week’s MakeoverMonday dataset got me thinking. The original chart requiring a makeover was the chart below, from the Guardian.

Screen Shot 2017-04-03 at 23.06.30

The chart aims to show the chance of each profession becoming automated within 15 years or so, along with the percentage each profession consists of in the workforce. The main issue, for me, with the original visualisation, was that showing two percentages on the same axis doesn’t really make sense. We’re being asked to compare two percentages which aren’t relevant to each other, so don’t belong on the same scale.

My first thought was a scatterplot – plotting percentage of workforce versus percentage at risk would allow us to cluster together professions, or identify traditional quadrants: which professions are both more likely to be automated and have a higher proportion of the overall UK workforce. Something like this (excuse formatting):


But I wanted a different way to combine likelihood of automation with the overall share of UK workforce for each profession. From a numerical point of view, multiplying the two together will give the overall percentage of the entire UK workforce. And from a visualisation point of view, this can be represented by multiplying height and width together to give area.

Enter the Marimekko chart. With x-axis ranging from 0% to 100% and y-axis also ranging from 0 to 100%, the entire chart area therefore represents 100% of the UK workforce. Realising this as I experimented with visualising the data was the lightbulb moment for me where the Merimekko was concerned. The total area of the bars therefore nicely sums up to the overall percentage of the population at risk, in this case a round figure of 30%.


The influence for creating the chart was from Emma Whyte, who herself drew from the great work done by Jonathan Drummey. Emma blogged about this recently as a recent #WorkoutWednesday challenge in this blog post here. I’ve chosen to use the same technique as her, blending all the non-selected elements to the same shade of grey to look like background. The chart Emma (re-)created was this one below (not hard to see where I got the inspiration):

Marimekko Makeover

Thank you also to the suggestion of Steve Wexler – he has a simper version of the same process here, which will be familiar to any users of his excellent and instructive stacked bar charting method for Likert questions in survey data. Be sure to check it out for his thoughts on the Merimekko (initially sceptical and similar to mine). But where it really comes into its own is in visualising two interconnected percentages, both of which sum to 100%, so the total chart area can represent the sample whole.

How do you visualise chess games?

How do you visualise chess games?

There are lots of ways you can interpret this question. As someone who asks and answers lots of data visualisation related questions, and themes an entire blog around it, I’ll touch on a few of them. Sorry about that …

First of all, chess is a battle game between two armies. So you could say that the game of chess is already a data visualisation. Each piece is a symbolic representation of a particular soldier in the army. Each army is represented by a different colour (which we always refer to as Black and White even if physical colours are different)


(image from – pieces above are the classic Staunton design)

If chess is a symbolised battle, then chess in its diagrammatic form is further visualised to another level. Unless you regularly play over the board games or tournaments, the chances are that most regular players in 2017 are more familiar with chess games on computers, in books or on websites. Below is a visualisation of the beautiful final position of Anderssen vs Dufresne’s “Evergreen” game of 1852.

Screen Shot 2017-04-02 at 20.19.37

For a long while now I’ve been considering a different way of visualising chess games. Not by way of improving traditional chess diagrams – I’ve studied more thousands of these over the years than I care to admit, and “if it ain’t broke, don’t fix it”. But is there a way to visualise an entire chess game in one diagram? Not to study individual moves, pieces or positions, but to represent a full game pictorially.

Discussions among chess players, experts and analysts has often centred around the question: “Is chess an art, or a science?” The fact that it is considered both by different proponents of the game means we shouldn’t be surprised that it is an interest shared by many data visualisation exponents. Many enter the profession as analysts and learn artistic skills, many enter as artists and learn analytic skills. Many have both talents, and more, in abundance. Edward Tufte, in some people’s eyes the most influential name in data visualisation, is, I believe, a keen player and has blogged on chess and chess-related visualisation before here. Of lesser influence perhaps, but of equal importance, my friend and fellow #MakeoverMonday participant blogged here, less than 24 hours ago, about the positioning of participants on the “vizzer” spectrum between art and analysis.

Moving away from art and science, as mentioned above, a game of chess represents the movement of two armies over the course of a tactical battle. This brought to mind one of the most famous and instructional data visualisations of all time, Charles Minard’s famous visualisation (below) chronicling the movement and size of Napoleon’s troops in their 1812 campaign against the Russians.


I won’t go into this in huge detail, suffice it to say that if you’re not familiar with this, it’s sourced from Minard’s entry in Wikipedia, and that would be as good a place as any to learn more about this. It tracks army strength, size and positioning and represents it as a flow on a diagram. Can we do something like this for a chess game? Instead of Anderssen’s finishing position in diagrammatic  form above, can we depict the flow of his pieces (armies) throughout his conquest of Dufresne’s armies?

Another visualisation type that has interested me recently has been the “centre of gravity” chart. These charts show how average position of something on a map has changed over time, thus creating a trail year on year (turn by turn?) To illustrate what I mean, here are a couple of examples:


The first chart (above) is from showing how the average position of the Stanley Cup winning team has gradually moved from Canada over the border towards Detroit.


The second chart (above), from the Economist, shows how the World’s economic centres have moved from Mesopotamia across the continent towards the North Atlantic (as the “gravitational pull” from the US takes effect, before a boomerang effect back over Northern Europe as the pull of far Eastern economies takes effect.

Combining these two effects gives me what I need. Taking the average position of all white and black pieces move by move, weighted by the size of each “army” will show the slow movement of overall mass of white and black pieces. I’ve loaded in data for three classic games, one from each of the last three centuries. The “Evergreen” game, mentioned above, is included, as is the first game of the iconic 1972 World Championship between Spassky and Fischer, and the decisive game in the most recent World Championship – Carlsen’s victory over Karjagan in 2016. I’ve included a slider so that the user can follow along with the game in traditional notation and diagrammatic form, whilst watching the visualisation unfold. More details are shown by hovering on the top right black pawn for information.



I’m pleased with these and the story they tell. The first game (Anderssen vs Dufresne) shows how thin the white line becomes as piece after piece is sacrificed as the remaining armies push forward towards ambushing the black king. The second game (Spassky vs Fischer) shows Black’s downward right push as his bishop grabs material in what ultimately is proven to be an unwise manoeuvre – the long thin trail towards the Queen’s side shows Spassky’s movement towards a winning ending advantage on the opposite side of the board. The thicker lines in Carlsen vs Karjagan show the continual manoeuvring of major pieces (the higher values queens and rooks) and the ultimate win when white’s pieces are positioned so much more on the king’s side of the board than black’s pieces to exploit a quick finish.

They showed what I wanted them to show – an “artistic” capture of a full classic game of chess. But I’m always grateful for feedback or suggestions. In this particular case, suggestions of future games to include would be great – so long as you appreciate that the input of data is a bit of a labour of love! One person I asked specifically for feedback was Nicholas Rougeux, whose work I mentioned was a particular influence here. I see these charts as being quite reminiscent of something he might produce and he expressed a genuine interest to see how these visualisations would turn out. His suggestion was to show a trail for average position of each individual piece, not the whole army en masse. Might that be easier to understand and interpret? Here are the results below, shown at the games’ winning move in each case:

anderssen grab

I love this! First, the evergreen game: the centralisation of white’s rooks to the centre and the diagonal ranges of his queens and bishops are obvious. The futile raid of the thick black line (his queen) and the small dots on both sides representing unmoved pawns tell the story of how short the game was.


Next, Spassky’s win. What sticks out is black’s bishop raid into the bottom right corner to pick off white’s pawn and the gradual move of white’s pieces up the board. Most pawns have moved forward to contest central squares here.


The Carlsen game shows all the manoeuvring of thick trails, particularly black’s queen and rooks jockeying for position, particularly all his rook moves on the (left hand) a-rank (column). Ultimately it was fruitless as White made his move towards black’s king in the back right.

I’ve written a lot about visualisation and chess – thanks for sticking with me if you’ve been following it this far. Thank you to enthusiasts of both for your suggestions, and thank you in advance for those yet to come, please do let me know as I intend to update my online Tableau version with future games and improvements.

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, 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.


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:

Dashboard 1-10

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.


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!


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:


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


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:


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.


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 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.


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:


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.


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:


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


And here’s Ken’s


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.


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.


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 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.



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:


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.