The little of visualisation design: Part 5

This is part of a series of posts about the ‘little of visualisation design’, respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I’m going to focus on just one small matter – a singular good or bad design choice – as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I’m just commenting on one.


The ‘little’ of this next design concerns a nice way, proposed by Tim Brock, of handling some of the concerns that people raise about the potential misleading effect of using of a non-zero value-axis origin in line charts. (You don’t need to start line chart value axes at zero but I’m not going to get into that here and now).

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In an excellent article Tim discusses various approaches that help avoid giving the reader the sense that the ‘bottom’ of a chart should be read as a zero baseline. One solution that catches my eye is the use of a fading effect at the lower end of the chart. By decreasing the opacity for the colouring of the axis line, any tick marks and the value label, this makes visually clearer that the chart’s view is only presenting the observed range of values.

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The little of visualisation design: Part 4

This is part of a series of posts about the ‘little of visualisation design’, respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I’m going to focus on just one small matter – a singular good or bad design choice – as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I’m just commenting on one.


The ‘little’ of this next design concerns a really neat feature demonstrated in a project I reckon 99.9% of visualisation people are well familiar with: Gapminder. Specifically this is a new version of the classic tool, described as being pre-alpha (not sure really what that means).

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The feature I want to point out here is the ‘DATA DOUBTS’ link positioned just below the chart. Data is rarely perfect. The journey it goes through from measurement, processing, statistical treatment and finally on to visualisation will often introduce a need for assumptions, application of counting rules, small inaccuracies, rounding errors etc. ‘Good enough’ is usually a necessary attitude to take otherwise we’d be frozen by the reluctance to publish any information.

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What I love about this feature is that it acknowledges doubts about the data in a very open way: it is acknowledged front and centre, not scuttling around in the shadowy outposts of the site. The trustworthiness of a visualisation has to be of fundamental importance and so this kind of feature is so refreshing to see. Clicking on the link brings up a dialogue box with a brief comment about the reliability of the data, details about some of the necessary adjustments and assumptions and a link to read more in a blog post. Really very sensible and helpful for a reader to get this kind of contextual guidance so transparently before one launches in to forming meaning from the display.

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The little of visualisation design: Part 3

This is part of a series of posts about the ‘little of visualisation design’, respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I’m going to focus on just one small matter – a singular good or bad design choice – as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I’m just commenting on one.


The ‘little’ of design today concerns a matter I’ve discussed before relating to the integration of graphic devices into written sentences, as demonstrated in this graphic about the quality of signings being made by football clubs in China signalling the possible emergence of a new power in the game.

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Specifically, in this project, you will see how the ‘key’ explainers that would normally be segregated by a legend adjacent to the chart are instead incorporated into a written guide explaining what the various marks and attributes used in the chart represent. It might look simple but I’m sure it would have been quite a fiddly task making the sizing and alignment of the small graphics align seamlessly with the text and row size.

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(Via a tweet from John Burn-Murdoch who created this graphic and authored the article)

The little of visualisation design: Part 2

This is part of a series of posts about the ‘little of visualisation design’, respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I’m going to focus on just one small matter – a singular good or bad design choice – as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I’m just commenting on one.


The ‘little’ of this next design concerns clever axis-scaling decisions in this New York Times graphic about the trial of firebombing of refugees in Germany.

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Across the three line charts at the bottom of the graphic the highest value is 76 but this is a single outlier, with 38 of the 39 values presented less than 50. By effectively setting the y-axis maximum range to 50 notice how the recent increase in incidents of violence against refugees becomes even more striking, as the line climbs up to the height of 76, far beyond the height of the chart and almost intruding on the map area. An example of a subtle but smart editorial design decision.

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(Via a tweet from Gregor Aisch who I’m assuming was involved in the creation of this graphic)


ADDENDUM: A perfect suggestion from Walter Rafelsberger:

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…so, to capture the essence of this post series, here’s a little Al Gore on his little cherry picker

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The little of visualisation design: Part 1

This is part of a series of posts about the ‘little of visualisation design’, respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I’m going to focus on just one small matter – a singular good or bad design choice – as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I’m just commenting on one.


Perhaps inevitably, I begin this series looking at a pie chart (side note: don’t blame the pie chart on bad design choices). More specifically the issue here concerns unnecessary duplicate labelling.

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In this example, from the WEF, you see the use of colour legend AND direct labelling to indicate the categories that make up the parts of the whole. You don’t need both. Either directly label or don’t. In this case, due to the colour choices being far too similar, the direct labelling is the better option, which makes the legend entirely redundant.

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(Found via a tweet from Nigel Hawtin)

Data visualisation at its best in a chart about taxes

In my recent ‘Ask Andy Anything’ webinar Andy Cotgreave and I were faced with a particularly challenging pair of questions, one about sharing ‘success stories’ and the other inviting us to offer an elevator pitch for the value of data visualisation. I think this project by the excellent Alvin Chang of Vox is a perfect exhibit of the role of data visualisation. It visualises ‘100 years of tax brackets, in one chart’. Technically, it doesn’t just do this in one chart because it builds up the narrative through a series of carefully introduced sequenced snippets before the big reveal of the full 100 year chart.

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What it shows is the incredibly complex brackets that were in place for so many years – but arguably reflecting a more equal society – and then the hugely reduced and simplified model of the latter years of Reagan’s term, down to just two bands – with middle earners facing the same taxes as the wealthy.

This is data visualisation at its best as a device for facilitating understanding in a way no other form could achieve. You can see the data. You can learn something about the subject if you are new to it, you can confirm what you suspected about the subject if you are not. When next I find myself in an awkward elevator situation being asked about the value of data visualisation, I’m going to have a laminated print-out of this example ready to whip out.

(As a sidenote, take a look at how the responsive design modifies the appearance of the President labels as you widen/narrow the screen)

Quick redesigns of BBC sport graphics

Yesterday I tweeted about a confusing graphic from a BBC article dispelling myths about last season’s Premier League. Its a good article with some really interesting content but some of the accompanying graphics hindered rather than enlightened the points being made.

A couple of people asked what I would do instead. Time rarely allows it but one should always be willing to offer an alternative, even if it is just quickly expressing some ideas verbally. Criticism without suggestion is empty and it is something I fall into all too often. So I thought I would quickly offer a reworking of the two main graphics that I felt caused unnecessary inaccessibility. I assumed the same constraints around space, (similar) typeface and colours as well as the inclusion of the logo and hashtag.

The first concerns the use of a radar chart to demonstrate how Stoke City have evolved from a long-ball team under Tony Pulis, into a more progressive team under Mark Hughes. With Radar Charts, there are almost always better solutions, especially when you are attempting to compare two series of values in the same chart. Radars really only make sense if and when there is some compelling logic for the radial arrangement of values (usually temporal, spatial or, occasionally, intuitive groups).

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The redesign uses a connected dot plot to draw out the differences in rankings between the different measures. The measures themselves are re-ordered to try and group related ones together.

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The second relates to the main graphic I mentioned on Twitter, which looks at dispelling the idea that teams with the most possession have the most success. The donut chart was reasonably fine until the dots landed. Due to their placement within the arcs there is an implication of meaning, especially when we see the two adjacent dots for the call out aggregating the loss and drawn match percentages. Unnecessarily confusing when all we need to learn about is 4 numbers.

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The redesign is very simply a stacked bar-chart. To be honest it still doesn’t add loads of value as a visual, you are essentially getting most of your understanding from the value captions but the stacked bar better aids showing the ‘did not win’ aggregate. I’ve switched the colours to perhaps better suggest bad, medium, good.

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Comparing the before/after versions I suspect that the labelling size and prominence of colour of my redesigns would need fine-tuning. It is interesting to see how faded they look when you shrink the final png file down. In the native Illustrator version they look far more vivid to the naked eye. That’s a good lesson in testing out your prototype designs in the size and setting in which they are likely to exist, to see for yourself how they look. Anyway, I’ve not got time to undertake endless iterations but you get the idea.

Graphic embellishments that add value

A very quick post just to share this graphic from the WSJ that was published a couple of days ago. I adore it.

I’m profiling it on here because I think this is a perfect example of an infographic that includes worthwhile and justifiable visual embellishments: useful chartjunk, if we want to go there.

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The use of the KitKat sticks to represent the bars does not distort the data but adds an extra layer of subject immediacy and attraction. The bitten off chunks are presented where the lengths/values should be (at least they look like they are). The backdrop of the carefully unfolded silver foil wrapper, the inclusion of the fragments of crumbs: we don’t NEED these devices but they are inoffensive, non-gratuitous and ramp up the aesthetic appeal.

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Some might term it gimmickry but for me it entirely transforms the appeal of this analysis that would have otherwise formed around largely utilitarian charts packaged up in a quite non-affecting style. There’s nothing wrong with that of course but, personally, I wouldn’t have taken a second glance at it. By contrast, I still want to eat this chart, and in my thinking that is a measure of success.

Bloomberg visualisation tracks America’s big issues

Really like this piece of visual journalism by Alex Tribou and Keith Collins of the Bloomberg Visual Team who have looked at ‘How Fast America Changes Its Mind‘ over some of the biggest social issues. Given the imminent possibility that the Supreme Court will consider arguments for an extension of the right for same-sex couples to marry nationwide, what is the history of pace of change across other significant issues: interracial marriage, prohibition, women’s suffrage, abortion, same-sex marriage, and recreational marijuana.

The first piece of analysis is a short animated display that maps the number of states that had removed a ban (or, for Prohibition, enacted the ban) since 1781 for each of the 6 big issues. You can see the very different rates of progress between, for example, the rights regarding interracial and same-sex marriage. In 3 (and possibly 4) of the 6 issues there seems to be a tipping point when around two-thirds of states are accepting of positive social change.

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Below this you have an individual breakdown for each of the 6 issues tracking in more detail the individual stories state by state.

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Finally, there is an alternative view which considers the speed of federal action following a major trigger point brought an issue to what I imagine was a mainstream, nationwide consciousness.

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This work follows (in some part) a similar analytical narrative approach to a piece of work I previously profiled, namely the Peyton Manning touchdown pass record published by The Upshot. That begins with a similar overview across history showing cumulative change over time for the subject categories (in that case Quarterbacks). It then explores the individual stories for significant ‘categories’ (previous record holders) before finally considering an alternative perspective based on setting everyone’s starting point at the same zero to compare the rate of change (in their touchdown passing totals).

Stereotropes explores gender and ‘tropes’

Stereotropes is an interactive experiment, developed by the Data Visualisation team at Bocoup, which examines the use of a range of common tropes in TV and movies and how they reflect, shape or counter against gender stereotypes in society.

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As the description on the site explains, it is a really fascinating exploration in to the use of language and connotation, asking the ‘chicken or egg’ question about how tv and film reflects society:

Some of the greatest reflections on society take place in film, through complex characters, often falling into familiar patterns called “Tropes”. Tropes are devices and conventions that a writer can rely on as being present in the audience’s minds.

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The tropes are authored by the community on tvtropes.org and have been selected and categorised based on when they are always associated with males (such as ‘Jerk Jock’ above) or females (such as ‘The Chick’ below).

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There are 4 different entry points into the project: tropes, films, adjectives, and gender.

The first part on the site let’s you explore individual tropes, initially providing a short description of their representation and a timeline history of their use in TV and film. It then delves in to more detail, based on the full description from tvtropes.com, about the use of language, specifically the adjectives, used to describe this trope and whether those are strongly associated with this particular trope or more generic. You can also view other tropes that share the same adjectives in their descriptions. Note that the triangles are essentially an arrow between the text label and the marker on the axis, apart from their colour, they don’t encode any data through their shape, size or direction.

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Next up, the films give you a way to interrogate the tropes used in over a hundred years of individual movies.

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A chord diagram offers a way to view the quantities and connections between all the adjectives used in the trope descriptions and an overview of all the male and female tropes that use that term.

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Finally, there is another slice of analysis that considers usage of adjectives and the strength of association with the male of female gender, based on dominance of reference.

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There is an excellent detailed overview of the workings that went on behind the scenes for this project on the about page.

As a movie fan I’m instantly on board with this project through subject alone but I really love the focused slice of analysis the team have applied to this large topic, providing an excellent tool to investigate the way gender and character is portrayed on screen.