The Future of Data Visualisation

Okay, we have all watched enough science fiction to know that there is not one future, but multiple futures. All options existing as if taken in parallel universes. Today’s post is not about a specific graphic, but rather a short article in the New York Times examining data visualisation. Through the work of Eric Rodenbeck of Stamen Design, it looks at how we may need to change our current vocabulary, if you will. Naturally the article offers a counterpoint nearer the end about how older forms are still useful.

Visual candy to entice you to read
Visual candy to entice you to read

Where do you fall?

Infographics and Digital Posters

Today is the odd day where I don’t have an actual graphic to share, but rather one of those abstract theory wishy washy brainheady thinky things. It’s an article in Fast Co. that discusses an essay written by Kim Rees and Dino Citraro wherein they define the concept of digital poster. Think big, vertical, scrolly infographics.

Maps Are Not Silver Bullets

I make a lot of maps in my line of work. Often times, they are not particularly interesting. Mostly because they follow similar patterns to this. More stuff is bought and sold where there are more people. More stuff is bought and sold where more people have more money. Et cetera, et cetera.

Maps are not always helpful
Maps are not always helpful

Maps are sometimes very useful. But I have a saying when people ask for a map of some kind of data tied to geographies: Maps are not silver bullets. That is to say, just because you throw data about countries, states, or counties onto a map does not mean you are going to see anything worthwhile let alone new or unexpected.

Credit for the XKCD piece goes to Randall Munroe

Venn Diagrams. Let’s Go Back to Grade School.

Last week Mitt Romney’s campaign released a series of infographic adverts. They were Venn Diagrams with messages attacking President Obama by highlighting what the Romney campaign called gaps between what the president has said he would do and what he has in fact done.

Debt Gap
Debt Gap
Deficit Gap
Deficit Gap
Healthcare Gap
Healthcare Gap
Unemployment Gap
Unemployment Gap

The problem with these is that they are all wrong. Do not misunderstand me, the Romney campaign certainly has valid points in these statements. And to use an infographic to communicate their points is a valid approach. But whoever designed these adverts clearly did not know how a Venn Diagram works.

Here is a brief refresher course for those interested.

How Venn Diagrams Work
How Venn Diagrams Work

Unfortunately, the Romney campaign’s message is being lost in a failed medium. It’s like watching a clown give a doctoral thesis in rocket science. He sure might be making a good point. But it’s a clown. People laugh at clowns. People won’t take the clown seriously. The Romney campaign is making good points, but that message is being lost because the campaign cannot master one of the simplest types of charts.

Credit for the originals go to the Romney campaign. The bit on How Venn Diagrams Work is mine.

Pie Charts v Bar Charts, Round…Some Really High Number

Not strictly a commentary on a piece or project, instead, this is a link to an interesting opinion piece about the Great Infographic Debate, i.e., most loosely and least helpfully, substance vs. style, vis-a-vis the use of pie charts and such vs. bar charts. Where does one draw the line between clear communication and, frankly, just getting somebody’s attention so that one can communicate?

From the article, an illustration of just how bar charts are significantly better than pie charts at clearly communicating data such as which is the largest data point.

The largest datapoint is obvious, but what about the order of those that follow?
The largest datapoint is obvious, but what about the order of those that follow?

Thanks to Ben for the heads up.

Growing Pains

An information graphic looking at obesity in the United States
An information graphic looking at obesity in the United States

This piece comes from my coworker, Ben, who found the graphic in Scientific American. Broadly speaking the piece is looking at the obese and the overweight in the United States, comparing the numbers of both children and adults in 1980 to 2008. These numbers are supplemented by the risk of death posed to both men and women from a few different causes. (I know at least diabetes is linked to weight, but as to whether the others are linked I am unaware.)

I have a few quibbles with the piece; for in general I think educating the public about the health risks of obesity a worthy endeavour. From a more scientific-ish point of view, as I recall, BMI (body mass index) is not a particularly useful tool in determining obesity because it fails to differentiate people who are heavy with fat from those who are heavy with muscles. A strong and regular weight-lifter is not necessarily overweight, but simply has a lot of muscles. Does that make the weight-lifter less healthy than those with lots of body fat? Methinks not.

From the data side, I am curious to know why only the two years? It may very well be that they are the only two years for which relevant data exists. But I doubt that. 1980 compared to 2008 is interesting, but perhaps already well-known. What would perhaps be more interesting is whether over the past few years, the increasing attention paid to weight and other health issues has begun to affect the growth of the obesity problem—poor pun very much intended.

The accompanying text makes a point about the number of adult Americans being obese. Certainly the dots as a percentage of the population achieve that goal of showing percents—though I hasten to add that their arrangement around the body in the centre does very little to aid in comparing the adults of 1980 to 2000 let alone the children. And as to the children, the article points out that they are growing fastest. At this, however, I can only take the authors at their word for the graphic does nothing to visualise this statement. Perhaps they outgrew the adults—but then the adults were themselves at one point children, but that is another matter—but their growth could now be slowing as a recent turn of events. But since we only have two years, we cannot know for certain.

The risk of death by [type of death] is interesting. But running bar charts as more of a radial chart could become a bit confusing. Is there any reason the bars grow in width as they extend further out? Or was that part of an all-too-obvious play on the problem. After all, the growth in area could be significant; a simple line of constant stroke to a point along the radial distance markers would have sufficed. And then I would be particularly curious to know whether any of these types of death are related to obesity. Neither the article nor the graphic provide any clues besides whatever knowledge the viewer brings to the table. (Okay, I think I am done with the puns.) And if one happens across the article with almost no knowledge of what diseases or medical conditions are caused by obesity, how does the graphic tie into the cost of healthcare costs brought upon the country by obesity.

Overall, I think the graphic is well-intentioned. The public is becoming more accustomed to seeing data visualised. However, we need to make certain that we are communicating clearly by making datapoints easier to compare. (Looking at things across half of a circle is a bit tricky.) And then we need to make certain that the data we are visualising supports our statements. (Are children really the fastest growing? Over what span of time?) And then take the time to explain to the audience those things that may not be common knowledge. Does that mean dumb a piece down to the lowest common denominator of someone who has absolutely no knowledge? No. Design needs to elevate and educate its audience. Perhaps some of the finer details remain unexplained because of sheer complexity, but when amidst a host of details well-understood, that confounding bit may push an unsure viewer to do some additional research and educate him- or herself about the subject matter. And that, surely, is not a bad thing.

Clerks. And Not the Death Star Discussing Type.

The New York Times has a story about the clerks supporting the Supreme Court justices. And how, surprisingly, the Supreme Court is polarised. Truly surprising considering how unpolarised—or would it be depolarised—the remaining two branches of government are these days. Sarcasm aside, the staff at the Times put together a diagram to explain the polarity.

Where all the clerks go
Where all the clerks go

My only real concern, however, is the potential for an audience disconnect. While you and I may know who John Marshall and William Brennan are, would the rest of the infographic’s readers? Does that mean not to include the justices? Personally, I always believe that design should lift and educate people and that designers should always avoid ‘dumbing things down’ for their audiences. Maybe not having the information in the diagram helps, and it will spur casual readers to do their own research. Or perhaps the targeted audience are those who have a grasp of the history of the Supreme Court.

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.