The little of visualisation design: Part 29

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 the use of small annotated marks to help draw a reader towards exceptional ‘values’. For the second consecutive #LittleVis piece I’m referring to work from the Financial Times, this time from an article titled ‘How prepared is Britain for extreme weather?‘ written by Oliver Ralph. The graphic is at the bottom of the page and was produced by John Burn-Murdoch. It looks at the patterns of monthly rainfall across Great Britain up to 2014 and going back to 1960. It is tall in dimension so this is just an excerpt from the top.

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With such a dense display formed by 660 individual map panels, there is a lot for the readers’ eyes to process. Even with the known capability of the eye to efficiently scan such displays for the key patterns, it significantly helps the reader to include annotated markings (‘editorial overlays’), in this case colour-coded squares, to quickly emphasise the main periods of significance to this story.

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12 notable US election visualisations

The (nuclear) dust settles at the end of a seismic week in history, as the fallout and angry finger-pointed fall outs continue to dominate the media and social media landscape, with people looking to make some sense of the non-sense by finding that single person or single thing that should be to blame. Pundits, pollsters, polling methods and data modelling techniques are certainly taking lots of heat. Data visualisation may not be directly in the firing line but I suppose there is a guilt by association.

The fact is, of course, that a visual and its underlying data carry a shared responsibility for understanding. A visualisation is only as good as the data it portrays. This principle stands just as important as ever today.

Given the deluge of different visualisations used during the build up, the event itself and now in the post-mortem phase of the election, it is understandable that some people have expressed severe fatigue from seeing so many election-related visuals (I imagine heightened by the outcome, for many). However, I do feel that we should acknowledge some of the excellence that has been produced during this period. After all, this is a 4-yearly event which, for many journalism organisations, represents the biggest platform to mobilise their focus and talents.

I have therefore picked out a collection of twelve projects that I’ve seen from across (some of) the journalism landscape (admittedly I’ve not seen or had access to ALL election graphic work) that I felt demonstrated the best of this art during this event.

I’m not going to provide design commentary for these, it’s more a collection of inspiration. However, the first project listed below does require some further analysis and I will follow up with a piece about the gauge chart specifically. The order of those listed is entirely incidental but loosely grouped around those used during and viewed after the election.

ON-THE-NIGHT

New York Times – Forecast Gauges

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FiveThirtyEight – Snake Chart

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The Guardian – Election Dashboard

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Financial Times – Weighted Dot Map

(Inspired by The Guardian)

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AFTERMATH

New York Times – Arrows Map

(Building on their previous work for the 2012 Election)

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Washington Post – Vote peaks

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New York Times & Wall Street Journal – Scribble Plots

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Financial Times – Tower Chart

Inspired by this 2000 Election Graphic by NYT

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Wall Street Journal – Scatter Plot

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STORIES

ABC – Story of the Night

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Financial Times – Video Explainer

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New York Times – Precint Stories

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The little of visualisation design: Part 28

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 the use of reading guides to help the reader understand how to interpret the meaning of a heatmap display. The project in focus here was produced by the Financial Times and from an article titled ‘Frankfurt vies for UK banking jobs post-Brexit‘ and shows analysis of the presence of different banks across a range of major European cities.

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To some readers, the task of parsing meaning through the colours at the intersection of two categorical variables can be a little tricky. Knowing this, the creators include a small guide above the chart (and therefore, through its placement, intended to be seen before reading) that quickly indicates the interpretation to draw from seeing specific patterns down each column and across each row.

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7 innovative, interactive static visualisations

Hmmm, you’re thinking, this post title smacks of cynical click bait? “You won’t believe the ways people are interacting with static visualisations!” was the first option but I eventually toned it down. Anyway, as the title suggests, this is a small collection of five six seven innovative ways that I’ve seen applied to make static, non-digital visualisations become ‘interactive’ in different ways. I’d love to see other examples you might have seen too.

1. SCRATCH OFF GRAPHS

Developed by Stephanie Evergreen, these charts are printed on thick paper or card and then the category value labels are painted over using a scratch-off paint mix. This allows them to then be scratched off using a coin perhaps as the big reveal associated with asking people to guess the values associated with each bar.

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2. THROWING A DART

A well-known piece by the Economist, this printable sheets uses the likelihood of a thrown dart landing in different red regions to represent the statistic of the likelihood of being murdered in different countries during a given year.

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3. ASSEMBLABLE 3D STATISTICAL BALL

From the Times of Oman, this frankly incredible concept involves 32 international football team profiles that are printable, foldable and assemblable as truncated pyramids that collectively create the form a ball but individually display statistics about world cup performances that can be paired with other countries to facilitate comparison. It deservedly jointly-won the ‘best in show‘ award at the 2015 Malofiej awards.

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4. PRINTED, LIFE-SIZED COMPARATOR

Produced by the Hollywood Reporter, this is a printable, life-sized outline of Donald Trump’s hand so you can overlay your own hand to measure up against the orange one’s apparently tiny paws.

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5. TACTILE WOOD MAP

I profiled these in a post from 2013 but worth including in this collection. The Inuit wood maps provide(d) a hand-held tactile mapped representation of the relief of the coastline of Greenland, allowing the Inuit to appreciate and identify the contours, islands and glaciers (etc.) of the coastline.

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6. DIY DATA COLOURING BOOK

Created by Chelsea Carlson, motivated by the sense of the US election debates causing people to stress out, this project allows you to download a colouring ‘book’ of three different data visualiation templates for you to record your data feelings as while you watch the debate.

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7. POP-UP INFOGRAPHICS

Created by Elena Turtas, this project about ‘The Four Books of Visualising Sustainability’ covers important data relating to subjects like global warming, emissions, resources, forests, water, energy, food via the form of pop-up infographics that leap from the flat of the page, offering different interactive mechanics to play and customise the experience.

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It goes beyond just being interactive non-digitals but here’s a super collection of (currently 256) physical visualisations.

The little of visualisation design: Part 27

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 the inclusion of a ‘reset’ button. The project in focus here was produced by FiveThirtyEight and is titled ‘What Would It Take To Turn [Red/Blue] States [Blue/Red]?‘, offering an amazing (and rightly celebrated) multi-faceted tool to simulate the 2016 presidential election outcome based on the potential party preferences and turnout % of different demographic groups.

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The main interactive operators exist as draggable scatterplotted marks, representing the turnout % (y-pos) and overall party voting % (x-pos) for several major demographic groups. When you reposition the point markers the red/blue state swings in the grid maps above are modified accordingly as is the overall outcome. As you change the positions a feint ghost marker is left behind indicating the starting values (I guess based on current polling).

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I believe the inclusion of the ‘Reset’ button is of critical value here. In offering the means to intricately alter several value settings, it is very useful to be quickly able to return the state of an interactive right back to its starting calibration (and do it in a way that doesn’t require an inelegant browser page refresh). Surprisingly, I find this is an oft-neglected feature but certainly very useful.

The little of visualisation design: Part 26

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 small-multiple grid maps and a neat way of providing a state legend. The project in focus here was produced by Nathan Yau and maps the spread of obesity across the US over the past 30 years.

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As you can see, in the top left there is a reference guide that explains which grid cell relates to which state. With any grid map there will always be some degree of state position/neighbour compromise so this helps the reader to immediately (being the first thing you logically see) orientate themselves before they then move through the sequence of yearly patterns.

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This feels a far less repetitive solution and not as visually intrusive as it would be to include the 2-digit state labels in each map and in cell. I can imagine Nathan toyed with the idea of having cell borders around each state label but I like that he didn’t do this – it is sufficiently legible through just relying on white space.

The little of visualisation design: Part 25

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 the decisions involved when including arrows in your work to act as pointing or directional devices. A recent project that uses arrows comes from the Guardian, titled ‘Where is the riskiest place to live?‘ by Josh Holder, based on the world risk index. As you can see in the project there are several features of the chart that have associated captions, connected by arrows, to help draw to the surface some key or interesting insights from the data. (Note, there is a second use of arrows to indicate the general ‘Low’ or ‘High’ risk direction along the x-axis but I’m going to focusing on the ‘pointer’ arrows in the body of the chart.)

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There isn’t an instruction manual for the most effective way to design and integrate arrows into your display. Instead, as with many things in data visualisation, it is about judging what will offer the most legible and elegant solution to suit the overall visual balance. There is a really nicely judged lightness to the visual weight of the arrows in this piece. They are short in length and consistent in appearance, which ensures they supporting the display not dominate it. Perhaps the most unusual feature is how the arrow head points towards the caption, rather than the other way round.

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Arriving at the ‘best’ choice involves a surprisingly high number of decisions: What arrowhead* to use? What colour? How long/how far away should the caption be from the item of interest? What weight to apply to the line? Will it be straight or curved? Which is the start and which is the end? Where specifically to point the arrow to and where from? The next time you see an arrow, just remember how much thought has gone into what you might otherwise think is a straightforward choice.

(* Reminds me that Jane Pong ran a quick survey on peoples’ preferences for the arrows available in Illustrator)

The little of visualisation design: Part 24

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 the challenges of handling long labels. In this post I’m possible breaking the theme of this series as I’m not so much offering a solution, rather more a ‘heads up’ to flag the possibility of this issue in your work. I’m referring to one of my own recent projects here, ‘Filmographics‘, looking at the ebb and flow of the fortunes of actors’ movie careers. As you can see in the screen shot below, when you choose an actor from the menus at the top an illustration of their face is displayed in the page body and their name is presented within this image.

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The challenge faced in this case was judging the best font size for the actor’s name label within the (self-imposed) space constraint of the image width. We found a nice size for working with 59/60 values but then we had Arnie: at 21 characters in length, ARNOLD SCHWARTZENEGGER would be the single instance whereby the preferred label font size would cause the surname to be split over two lines. To make it small enough to accommodate Arnie would have the consequence of the name’s being (in our view) an insufficiently prominent title/identifier for all the other actors in the dataset.

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In the end, with time running out, we made a decision to accept the rather inelegant compromise of sticking with our preferred font size that would be suitable for all except Arnie: ‘Good enough’, ‘It’ll do’ is often a call you have to embrace when time resources diminish.

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With the benefit of hindsight we maybe could have looked to programmatically handle a custom font size for Arnie alone. We could also have handled the placement of the actor’s name entirely differently so as not to present us with a challenge of dealing with this spatial constraint. The main point to make here is to reinforce the importance of developing a deep and early acquaintance with all the physical properties of your data values, not just the range of quantitative values you’re facing but also the length of potential labelling assets.

It’s the little things like this that cause the big headaches.

The little of visualisation design: Part 23

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 an interesting approach to labelling y-axis scale values. The project in focus here comes from the Washington Post, titled ‘How terrorism in the West compares to terrorism everywhere else‘ by Lazaro Gamio and Tim Meko, putting into context the relative levels of terrorist-related deaths in the West.

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As you will see, the labels for the y-axis intervals are located within the chart. Specifically, they occupy the vertical space aligned with January 2016. Normally, we would locate these annotations to the left of the chart, maybe to the right, sometimes on both sides to assist the reader perceiving the chart’s values. It has never occurred to me before to think about positioning these labelling devices somewhere in the middle of the chart but I think it is a really innovative way of optimising their value.

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The specific choice of using the January 2016 horizontal position to present these labels is interesting because January isn’t right in the middle of the chart. It may be a logical month to choose in order to create a subtle breakpoint or divide between judgments of 2015 and 2016 values. Most likely, it could be influenced by the presence of ‘nothing’ – the zero value of terrorist-related deaths in Europe/North America during this month creates the ideal (*) emptiness to occupy the labels. This approach continues in the bottom chart, where there ARE values to plot in this space, but the interval labels only begin from where the bar heights end, thus there is no obfuscation.

(* yes, I’m aware how the notion of design decisions being helped by zero death values and, conversely, being impeded by higher death values sounds – just remember I am just looking at this solely through the lens of a design challenge, not what the subject represents)

New project: The Pursuit of Faster 2016

To mark the completion of the Rio 2016 Olympic Games, I have been working, with my trusted lieutenant Andrew Witherley, on designing a new version of ‘The Pursuit of Faster‘ project. This visualisation explores the evolution of medal winning performances across all Olympic Games since 1896 as athletes strive for that ultimate pursuit of being faster than the rest.

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It portrays the patterns of improvements in the results of time-based events whether it be on foot, in water or on water. There are several sporting events where relative speed determines medal success but this analysis is purely concerned with results from races where absolute speed is the measure of success.

Choose a sport and select an event to see how Gold, Silver and Bronze winning times have changed over the years, for both men and women. Hover over the medal markers to reveal the actual results (you can switch on/off this semi-transparent pop-up, though, using the provided toggle). Below the main chart you can learn about the most successful countries in each event, the gaps that exist in result times between genders, a normalised measure of improvement over time, and analysis about the margins of victory between Gold and Silver medallists. To learn more about the background to the project, how to read/use it and notes on how data has been handled, you can click on the project’s ‘About’ button.

This project was originally launched in 2012 as part of a pre-London Olympics visualisation contest (securing a runners-up prize) but I wanted to employ a fresh design and incorporate all the subsequent results data from the 2012 and 2016 Games. The project now has a more adaptive design for desktop, tablet or phone. The image above shows the full-screen view on desktop, this next image shows the full-screen view of the mobile version. The only difference is that the main chart is transposed and the mouseover results are switched off in the mobile version.

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