Visualising the Olympics

This weekend was pretty busy. We had another earthquake in Latin America—if one includes Haiti as part of Latin America—and the closing of the Olympics. Both have prompted some information graphics that are worth noting and comparing. I am going to leave the New York Times’ explanation of the Chilean earthquake to another post and instead focus here on the Olympics.

I wanted to look at three different visualisations of the Olympics, chiefly centred on the always popular medal count.

First we have CNN, which dedicates an entire special coverage site to the Winter Olympics. The site has the 2.0-esque feel with different boxes providing the user with different types of material: text-based stories, video, access to an interactive map, and a medal count. The map is what first strikes me because of its warning of reds and oranges and yellows. When I clicked to access the map, however, I felt disappointed in what appeared. And then I wonder why I am being warned about the US and Canada.

medal-map

Generally speaking, a lot of the world’s landmass did not participate. And of those that did, not a lot won any medals. The vast emptiness of the grey map does a disservice to those much smaller areas of the world, particularly in northern and eastern Europe, that did win but are difficult to see. And, personally speaking, as a fan of Antarctica, I was disappointed to see they neither contributed athletes nor won any medals.

What does work, however, is the idea of highlighting those nations that competed. Perhaps not everybody knows that not every nation competes. The map could have been better executed or even a more stylised visualisation of percentages of regions that competed. 7 nations on the African continent, by my quick count, did compete, while three or four European countries did not. The point is important to make. The visualisation, however, does not support it as well it could.

The medal count is also quite interesting from CNN. The special coverage site maintains a snapshot of the leader board that links to the Sports Illustrated medal count. From Sports Illustrated, we are provided a simple table-driven display of who won what with little bar charts to highlight the total medal count. By clicking on a country you see the historical details of the country’s performance, again presented as a table with a bar chart for the total medals won in each year. What I find lacking, however is ranking the countries only by total medals. If someone is more interested in the best gold-medal-winning countries, one has to work to find the data.  A simple ability to sort by medal type would be a valuable addition.

medal-count-92

An interesting situation arises, however, when looking at the historical figures. I am no expert on the Olympics. I did not watch any of them this year. Nor have I really watched them in the past. However, I do know that sports are added and subtracted. So, by clicking on Germany, for example, we see six medals won in 1936 versus the 36 in 2002. In 1936, however, I count only 51 medals total. In 2002, I count 234 medals. 12% compared to 15%. It does not seem so drastic an increase when put into context.

The BBC also has its own special coverage of the Olympics and has its own charts and tables to support the medal count. The main table they chose to use is interesting to me because it is particularly dark compared to the white and grey aesthetic of CNN and the New York Times (below). It certainly stands out. However, when I say ‘it’ I am referring not to the medal count but to the horizontal bands of dark greys and blacks. Between the dark colours, the small type for the medals, and the large flags to identify the countries, I find the medal count last. And when I find it difficult to sort by anything other than the default. But the default is not the total medal count as with CNN. Instead, it is the gold medal count. An interesting choice.

medal-count

The chart the BBC provides for total medal winnings is also interesting. You can compare across countries, years, and medals. Some of the interface makes it a bit tricky. For several minutes I was trying to figure out which years were which at the outset only to finally realise that I was looking at all years. Perhaps if even all the years were a light weight and then the year selected made bold it would be easier. Or a more noticeable shift in colour.

And then I started trying to discern between countries and was left with three-letter abbreviations for each country. And I could not. Perhaps my inability to figure out which country was which stems from my being an American. But I do like to think that I am pretty good at identifying countries. I thought if I moused over one of the stacked bars the abbreviation would reveal its true meaning to me. But it did not. And so I am left looking at stacks of medals for countries I cannot identify.

The stacked bars are interesting for they are not ideal in comparing medal counts within each country—but the mouseover state provides the required detail. Switching to the country comparison makes it a tad easier, but one must still filter medal by medal. Then aesthetically I do not care for the polished, faux-three-dimensional appearance. Nor do I think most of the bars need to be as wide as they are.

Overall, I like where the BBC was trying to go with lots of cross-comparing and filtering to break down medal counts and historical performance. However, aesthetically, I find too many elements large, bulky and distracting. From large flags and small type to black–grey bands and thick stacked bars, the interface is a tad too cluttered to really allow what the BBC did to show.

medal-comparison

Lastly, I want to look at the New York Times. Like CNN, the Times has created a map of the medal-winning countries in the Olympics, both current and historical. Unlike CNN’s map, however, the map by the Times is not based strictly on geography and each country is represented by a ‘bubble’ whose size corresponds to the number of medals won. I am not particularly keen on bubbles for displaying values. However, by eliminating those parts of the world not participating, the user can instead focus on the results. And those results, while not on a map, are spatially arranged to indicate their regional groupings. For example, the European countries are grouped together and coloured differently from those in North America. Switching tabs quickly reorganises the bubbles into rows ordered by numbers of medals won irrespective of geography.These are interesting design decisions and while I have reservations about using bubbles, the exact figures are provided as one moves his or her mouse over the bubble.

medal-map

For the medal count, we see another table. The numbers are cleanly presented with small flags to the left—and without the bars of Sports Illustrated. Like both CNN and the BBC, one can dive deeper into a nation’s particular results by clicking on that nation. Yet, the experience of clicking into a nation is much smoother with the Times than with the others. No additional pages load and I am not watching my screen jump about to and fro. Instead, the additional information loads in a panel to the right of my click. It is a quick and clean transition that does not leave my frustrated by jumping browser windows.

medal-list

Yet, the additional information loaded is quite text heavy. And with the exception of the headers for each sport, nothing in particular stands out. Not the sport, not the medal won, not the athlete who won the medal. While the quiet type is aesthetically pleasing, it lacks punch. Instead of reading the words gold, silver or bronze—since we are looking at the medals—perhaps just their icons could be used. Perhaps the athlete is in bold. I would just like something to which my eyes can be drawn in the table.

canada-detail

The New York Times effort is brilliant work. Clean and simple design brings out the information—even if such information is presented in the forms of bubbles and such. Tables are rather clean and easy to read. I think their effort a success. Not perfect, as I find the more detailed table insufficient in terms of visual distinction, but still a success.

All of the efforts are significant and worthy of mention. Each has some flaws and each has some strengths. And admittedly, each is more complex and detailed than I have described and commented upon here. But I have to stop somewhere.

Looking at the Haitian Earthquake

The BBC has posted an article addressing the causes for the horrible death toll in the Haitian earthquake last month. Charts and data-driven graphics supplement the text and provide a parallel, though not synchronous, visual story.

screenshot of BBC article

I applaud an intensive use of graphics, especially data-driven graphics, to better relate a story. Perhaps especially because not everybody can learn something simply by reading it. Many among us are instead visual learners, and by incorporating graphics into stories, be them online news articles, printed magazine articles, or interactive experiences, we can reach a larger audience and hopefully inform, impact, and influence said audience.

The graphics for the BBC article do some things well and some things not so well. Firstly, as a series, the graphics are varied well enough to not be overly repetitive. We have two sets of bar charts, three sets of area-of-circle charts (I presume), and what Nathan Yau at FlowingData called the everything chart. And if we look at the placement of the charts within the article (B for bar, A for area, and E for everything) we see A–B–E–A–B–A. The placement of the graphic types within the structure for the visual component of the article is sufficiently varied not to bore the audience.

Secondly, the graphics are well done through the restrained use of colour. Each graphic is composed of tints of grey and red. Grey is largely used as the base colour for the graphic while red or its tints accent the key elements of the visual argument. For example, when comparing the number of people dead among the affected people, a single square is coloured red. In the larger areas of grey for China and Italy, that single square or single person, while not lost, is not necessarily readily important. Instead, it serves as a link across the countries for when people look at Haiti and that red square stands out among fourteen other grey squares.

On a few other fronts, however, the graphics stumble where, if a bit more attention had been paid, they could have really strode to the front of the pack.

From the perspective of representing data, I have three qualms with the graphics. The first is somewhat minor—for it may have well been intentional. Although one of the underlying points of the article is the sheer difference in scale of the impact, the Italian earthquake is often difficult to visually compare in detail next to the Chinese and Haitian earthquakes. Why? Because the Italian numbers are visually insignificant. Could this have been resolved in a different fashion? Yes. However, as aforementioned that could have been an intentional design decision and I only point it out as something worth considering.

Secondly, and more importantly, I have concerns about the area-of-the-circle charts. Chief among those concerns is that nowhere is it specified that we are looking at area instead of, as is sometimes the case, the radius of the circle. This error is significant and I shall illustrate below. If we take, for the sake of example, a circle of radius 5 units and compare it to a circle of radius 10 units, we have a difference of 5 radius units. Circle 2 is, by one measure, twice the size of Circle 1.

And while some compare radii of circles, others instead compare areas of circle—as I assume but cannot be certain of in this instance. However, if we look at 5 units and 10 units as the radius for the areas of Circles 1 and 2, we arrive at a difference of four times the size.

diagrammatic explanation of radius vs. area

This is not a problem specific to this article—although it could have been partially solved by stating area comparison or providing a scale. Unfortunately, this problem appears with an unsettling degree of frequency. But the problem is also compounded because people are not the best judges of area. It is far easier for an individual to compare two bars and see one as twice the size than it is to compare the area of two circle and see the same difference.

The problem of comparing length to length times width for a datapoint has the fair counterpart of visual repetition aforementioned. One of the three circle charts could easily be replaced by a bar chart. The two others could also be replaced, albeit with a little more work. However, the article would then be visually boring for it would be replete with bar charts.

And so the problem of comparing areas is one to be handled carefully. This piece has some potentially serious problems in comparing data. However, the quick and easy fixes would make the piece less visually interesting and thus, perhaps, cause the reader to lose interest. And with interest lost, the reader clicks away and the article fails to make an impact. On a case-by-case basis, the designer must often make the decision between fidelity to data versus capturing the reader’s attention. And here, I think the pendulum swung just a bit too far to capture the reader’s attention.

My third major point of contention, however, concerns the data itself and an incongruity in the graphics. Understandably, the earthquake in Haiti is a recent event. And indeed, the rebuilding and thus the cost is a current and ongoing event. Therefore, nobody should expect any institution to have anything but cost estimates. The final graphic makes note of the fact that we have no hard figures and that all figures are estimates.

Yet, the graphic provides two visual estimates hinting at two substantially different figures. The straight cost is represented on a bar chart that states “estimated at several billion” but visually depicts $40 billion. The cost as relative to GDP includes an asterisk for a footnote that shows the cost as but $2 billion.

Using the estimates for an early comparison of the financial cost of the earthquake is perfectly valid so long as the numbers are noted as estimates—as they are here. However, one should use the same estimate consistently across the piece. Because here, the use of different estimates can be used to imply that the straight cost is nearly 15 times more than that of Italy’s cost. But when using the cost to compare the cost relative to the economy, Haiti’s straight cost is $500 million less than that of Italy’s cost. If anything, either the bar chart should have had no bar for Haiti, or made use of the dashed red line at the $2 billion line.

And so while I certainly have reservations about the graphics as a whole, I support the decision to use data-driven graphics to support the story and the argument.

And How Should I Begin?

I have measured out my life with coffee spoons…
—T.S. Eliot, The Love Song of J. Alfred Prufrock

Modern life in the Western world revolves around data that then becomes misinformation, disinformation, or, more rarely, information. In theory, we use this information to inform our decision-making process and then live fitter, happier lives. (Please hold all comments about how well the theory of Communism is working in the Soviet Union until the end.) However, for most of us, gleaning any kind of information from row after row after column after column of data is too laborious and too time consuming to be worth our time. And so most of us have turned to an emerging field that is known by many names but is perhaps best described as data visualisation.

Data can come in many different forms. It can be the gross domestic product of the United States. It can be the preferred term for carbonated beverages across the state of Pennsylvania. (Although I think we can all agree it is soda, not pop.) Data can be the route of Philadelphia’s regional rail lines. (And how late they happen to be on any given day.) Data can even be the price paid for a cup of tea in a bookstore.

And with all these different forms, one can have perhaps an even greater number of forms in which that data is visualised—either alone or in comparison to other datapoints. One can look at the GDP of the US as part of a bar chart against the GDPs of China and India. One can look at a map of Pennsylvania and see the barbaric lexicon near Pittsburgh in their preference for pop. One can look at a non-geographic map of SEPTA’s regional rail lines and all the stops of the Main Line. (And the lines coloured not by their destination but their on-time-ness.) One can even see the price of a cup of tea printed on a piece of paper in an itemised list. And each of those forms and the many, many others has inherent benefits and drawbacks. Each can be used appropriately. Or not. Some look good. Others not.

An interesting dividing line in this nascent field concerns the efficiency of any visualisation in communicating the data. Some statisticians argue for stripped down charts and tables with relatively little care put towards the aesthetics of the visualisation. On the other side, some designers care more for what many non-designers call the ‘prettification’ of data. That is to say, placing care towards the presentation and legibility of the visualisation perhaps at the expense of clearly communicating all the data. So where do I stand? Well damn it, Jim, I’m a designer, not a statistician. While I do believe in maintaining a level of fidelity to the data, that data must still be clearly communicated to reach the designated audience. The piece in question must still grab the attention of the audience enough to get them to delve into the layers of data.

The ultimate goal of this blog is to examine data visualisations, information graphics, charts, whatevers and break them down to see what may have been done well and not so well—all from my aforenoted perspective. Naturally, some may disagree and I wholly encourage dissent from the party line. My party line. If you find something interesting, please send it my way.

Modern life in the Western world revolves around data. Data that we create each and every day. We are aware of some of the data we create while we remain ignorant of some other data. We create it through every click of the mouse. Every swipe of a card. Every change of a channel. Every mobile phone dialed. Each and every day our lives are broken down into rows and columns. We may no longer measure our lives in coffee spoons, but we measure them nonetheless.