One of the things discussed during the election season—though very minorly compared to other things—is the national debt. Debt itself is not scary. Look at student loans, home loans, auto loans, &c. Look at the credit cards in your wallet. But running a country is far more difficult and complex than a household budget. That said, our national debt is high, though of late it has been trending in a positive direction, i.e. flattening out its growth curve.
So what would electing either Clinton or Trump do to the debt? Well, nothing great. According to this piece from the Washington Post, we would be talking about increasing the debt because of plans that are not fully funded or revenue cuts that fail to match spending cuts. But as the graphic shows with a really nice piece of layout between text and image, one option is far worse than the other for the issue of the national debt.
The opening graphic above draws the reader into the overall piece, but the remainder of the piece breaks down policies and implications with additional graphics. If you want to understand the differences between the candidates and the impact of those differences, this is a good read.
Credit for the piece goes to Kevin Uhrmacher and Jim Tankersley.
In case you missed it, two weeks ago President Duterte of the Philippines had some interesting things to say regarding the relationship between the Philippines and the United States. “America has lost” and “separation from the U.S.” were among the two big lines he spoke to a Chinese audience. But the Philippines are an important part of President Obama’s pivot to Asia strategy as we have been spending money and time improving defence ties. Naturally issues like the the pivot underpin Trump’s claims about poor judgment when it comes to the Obama/Clinton foreign policy.
The pivot’s improving defence ties come at a time of region-wide increases in defence spending. Thankfully Bloomberg put together an article with some nice graphics earlier this year. As someone who has always had an interest in naval things if not military things, see my numerous posts on that here, I thoroughly enjoyed reading the article and digesting the graphics. The one below compares the strengths of the Chinese fleets to those American fleets permanently assigned to the Pacific Ocean region.
Of course the question becomes, beyond making our military stronger, just what would Trump do to counter or affect the arms race in the Asia Pacific region?
Credit for the piece goes to the Bloomberg graphics department.
70+ million people watched the debate last week. But, 2.5 million people have already voted. Me? Well in Pennsylvania there is no early voting, so you queue up on Election Day. But that also means I will have had the full election season to brush up on candidates for president and all the other offices. But what about early voters? Well the Washington Post put together an article last week about the numbers of early voters—hence my figures in the opening—and the amount of information they might have missed.
From a design standpoint, it is a really nice article that blends together large centre-piece graphics such as the above to smaller in-line graphics to margin graphics. None are interactive; all are static. But in these cases, users do not need the freedom to interact with the charts. Instead, the designers have selected the points in time or data points more relevant to the story.
Overall the piece is solid work.
Credit for the piece goes to Kevin Uhrmacher and Lazaro Gamio.
Last week the Washington Post published a fascinating article on the data visualisation work of the Donald Trump media campaign. In my last job I frequently harped on the importance of displaying the baseline and/or setting the baseline to zero. When you fail to do so you distort the data. But maybe that is the point of this, for lack of a better term, political data visualisation.
My favourite author is George Orwell of 1984 and Animal Farm fame. But Orwell also penned numerous essays, one of which has struck me as particularly relevant in this election cycle: Politics and the English Language. In concluding the essay Orwell wrote:
Political language…is designed to make lies sound truthful and murder respectable, and to give an appearance of solidity to pure wind.
And so political data visualisation? Well I believe it exists to serve the same purpose. The article goes into detail about how the designers behind the graphics fudged the numbers. Now did the campaign intend to mislead people with the data visualisation graphics? It is hard to say, because some of their graphics actually diminish leads that Trump has among certain demographics. Could it be the designer behind the graphics simply does not understand what he or she is doing? Perhaps. We clearly cannot know for certain.
Either way, it points to a need for more understanding of the importance and value of data visualisation in the political discourse. And then the natural follow-up of how to best design and create said visualisations to best inform the public.
But I highly recommend going to the Post and reading the entirety of the article.
Credit for the original work goes to the Trump campaign graphics department, the criticism to John Muyskens of the Washington Post.
Well that does it for the three presidential debates. Didn’t they seem very presidential with all those interruptions and interjections? Thankfully after the debate, FiveThirtyEight put together a quick graphic highlighting the total number of each per candidate per debate.
Credit for the piece goes to the FiveThirtyEight graphics department.
Today’s post is about religion. One of the two things you are never supposed to talk about in good company. And since the other is politics and since I cover that here frequently, let’s just go all in, shall we?
FiveThirtyEight has an interesting piece about religious diversity and a corresponding lack of religiousness. From a graphics standpoint, the central piece is this chart below.
What I would love, however, is for the plot to be interactive. It would be great to let people check out their own individual home states and see how they compare to the everyone else.
Credit for the piece goes to the FiveThirtyEight graphics department.
The Olympics are over and Team GB did rather well, coming in second in the medals table with 27 gold medals, more than they won back in 2012 when they hosted the Olympics. (See my piece four years ago where a colleague of mine and I accurately predicted the UK’s total medal count.)
Consequently the BBC put together an article with several data-driven graphics exploring the performance and underpinnings of Team GB. This screenshot captures a ranking chart that generally works well.
However, the use of the numbers within the dots is redundant and distracting. A better decision would have been to label the lines and let the eye follow the movement of the lines. A good decision, however, was to label the grey lines for those countries entering and falling out of the Top-5.
Credit for the piece goes to the BBC graphics department.
One of the things I like about Chicago’s WGN network is its weather blog. They often include infographic-like content to explain weather trends or stories. But as someone working in the same field of data visualisation and information design, I sometimes find myself truly confused. That happened with this piece last Friday.
The map in the upper-right in particular caught my attention and not in the good way. The overall piece discusses the heavy rainfall in the Chicago area on Thursday and the map looks at the percentage increase in extreme weather rainfall precipitation. All so far so good. But then I look at the map itself. I see blue and thing blue > water > rainfall. The darker/more the blue, the greater the increase. But, no—check out Hawaii. So blue means less rainfall. But also no, look at the Midwest and Southeast. So does green mean anything? Beyond being all positive growth, not that I can tell. As best I can tell, the colour means nothing in terms of rainfall data, but instead delineates the regions of the United States—noting of course they are not the standard US Census Bureau regions.
So here is my quick stab at trying to create a map that explains the percentage growth. I have included a version with and without state borders to help readers distinguish between states and regions.
And what is that at the bottom? A bar chart of course. After all, with only eight regions, is a map truly necessary especially when shown at such an aggregate level? You can make the argument that the extreme rainfall has, broadly speaking, benefitted the eastern half of the United States. But, personally speaking, I would prefer a map for a more granular set of data at the state or municipality level.
Credit for the piece goes to Jennifer Kohnke and Drew Narsutis.
I am on holiday for a few days and am visiting Philadelphia. So what better time to cover some Philadelphia-made content? This interactive piece came out last year from Philly.com alongside coverage of the Philadelphia mayoral contest.
I want to call out the colour palette for the choropleth in particular. We can see a blue to red system with a stop at yellow in the middle—a divergent palette. With this kind of a setup, I would expect that yellow or the light blue to be zero or otherwise straddle the point of divergence. Instead we have dark blue meaning 0 and dark red meaning 401+. The palette confuses me. It could be that the point of divergence—something around the 200 number—could be significant. It could be the city average, an agreed upon number for good neighbourhood relations, or something. But there is no indication of that in the graphic.
Secondly the colour choice itself. I often hesitate using red (and green) because of the often-made Western connotation with bad. Blue here, it works very well with the concept of the thin blue line, NYPD blue, blue-shirted police. If we assume that there is a rationale for the divergent palette, I would probably place the blue on the high-end of the spectrum and a different colour at the negative end.
Lastly, from the perspective of the layout, Philly has a weird shape. And so that means between the bar chart to the right and the city map on the left the piece contains an awkward negative space. The map could be adjusted to make better use of the space by pointing north somewhere other than up.—why is north up?—to align the Delaware River with the bars. Or, the bars could abut West Philly.
The interactions, however, are very smooth. And a nice subtle touch that orients the reader without distracting them is the inclusion of the main roads, e.g. Broad Street. The white lines are sufficiently thin to not distract from the overall piece.
Happy Monday, all. Some big news stories going on today, but I wanted to take a look at this piece from the New York Times. They report on the sale of Yahoo to Verizon for almost $5 billion via a piece that takes short written analysis and blends it with clear and concise charting. The effect is a quickly digestible, but data-driven content piece.