This weeks Makeover Monday tackles National Debt. Let’s start by looking at the original visualisation.
Apparently the US National Debt is one-third of the global total. Showing these two values in a pie chart is a good idea as it quickly shows the proportions involved. However the pie chart chosen does have a strange white think slice between the two colours and a black crescent / shadow effect on its outside edge which add no real value (in fact the white slice added a bit of confusion for me).
The visualisation then goes on to show $19.5 trillion dollars in proportion to several other (equally meaningless) large figures. The figures do add some perspective on just how big that figure is and the use of $100 billion blocks in the unit chart does allow an easy comparison. One slightly critical feature, if we were to pick holes in the visualisation, is that half-way through the view starts showing the shaded blocks to compare to the 19.5 trillion, whereas before it doesn’t.
Achieving consistency is important in data visualisation as it lets the reader know what to expect and gives them a consistent view each time to aid comparisons. So making a design decision to add shaded blocks across each comparison would perhaps have been a better choice as opposed to switching half way through.
Visualising Small Data
The dataset provided for the weeks makeover has simply two rows, showing the debt for each area (US and Rest of the World).
Clearly this presents a visualisation challenge. Visualising small datasets is hard, as there are limited choices. One can attempt to include secondary datasets to show the numbers in context, as the original author has done but another, simpler choice, might be to show them relative to each other – similar the original’s pie chart. One might even attempt to show how the data corresponds to the population of the US or the world, attempting to bring the figure down to something manageable (in the US the debt is a more comprehensible $61,000 per head).
Before we attempt to visualise something though we need to think about the audience and message we want to provide. Are we simply trying to show the figures without any comment? or do we want to focus on how large they are? or are we commenting on how large the US debt is to the rest of the world and making a social / political comment?
With a dataset so small any editorial comment is difficult though. For example we have no context on the direction of movement of these figures. The US might be quickly bringing it’s debt under control, while the ROW grows, or the opposite might be true. The ROW figure might be dominated by other developed countries, or might be shared equally. How can we comment without further analysis on temporal change or the context of this figure?
If we can’t comment editorially then we are left with simply showing how huge these numbers are. My criticism of the original is that the number it shows in comparison are equally huge, and equally incomprehensible for a lay person. Given this visualisation is published on a website Visual Capitalist perhaps their audience is more familiar with global oil production or the size of companies but for any visualistion published away from the site a more meaningful figure is needed. Personally I think the amount per head is an especially powerful metaphor. In the US $61,000 dollars each would be required to clear the debt, the ROW world would just have to pay a little over $5.
To Visualise or not to Visualise
Now there is an important decision here, how to effectively show those figures in context. However with such small data is there any point in doing so? Everyone can quickly see $5 is much less than $61,000 – we don’t need a bar chart or bubble to show that, and we certainly don’t need a unit chart or anything even more complex. This is the problem with small datasets, any visual comparison is slightly academic given we can quickly mentally interpret the numbers.
One might be tempted to argue that a data visualisation is needed to engage our audience. Perhaps a beautiful and engaging data visual might do a good job of this, however so would the use of non-data images like the the below.
Defining Data Visualisation
Makeover Monday is a weekly social data project, should a visual that includes only text be included?
What if the pile of dollars in the image above had exactly 61,000 dollar bills would that make it any more of a data visualisation than one that contained a random amount? What if, instead, we added as a unit chart with 12,200 units of $5 bills? These accompanying items don’t help us visualise the difference any better than the text. One could argue where the main purpose of a visualisation isn’t to inform or add meaning or context, and is instead used as a way of engaging the user, then it becomes no different to any other image used in this way. Therefore adding any more data related visualisations to the above text wouldn’t make the image any more of a data visualisation than the one above.
Semantic arguments that attempt to define data visualisation are interesting but academic. Ultimately each project that uses data does so because it needs to inform its audience, and it is the success of the transaction from author to audience that deems how successful the project is.
So should we define a data visualisation as more (or less) successful because of its accompanying “window decoration” (or lack thereof)? In my opinion yes. Accompanying visuals and text help provide information to the audience and can help speed up the transfer of information by giving visual and textual clues.
Do charts / visuals that make no attempt (or poor attempts) to inform the audience add any more value to a data visualisation project simply because they use data? In my opinion, no. This isn’t the same same thing as saying they have no value but simply producing a beautiful unit chart, say. with the data for this Makeover Monday project would add no intrinsic extra value in educating the audience and therefore would be no more valuable than any other picture or image.
Is the above image a successful Data Visualisation? Let’s wait and see on that one. I’m intrigued to see what the community makes of a purely text based “visualisation”.
Does it do a better job at informing the audience than the original? Again this is hard to answer but I believe I understand more about the size of the debt when it is visualised in terms of dollars per head. By bringing these numbers down to values I understand I did’t need to add any more visualisation elements in the same way as the original author, therefore you might say mine is more successful because it manages to pass across information in a simpler, more succinct transaction.