Well the Democratic DC primaries were last Tuesday and Hillary Clinton won. So now we start looking ahead towards the July conventions and then the November elections. Consequently, if a day is an eternity in politics we have many lifespans to witness before November. But that does not mean we cannot start playing around with electoral college scenarios.
The Wall Street Journal has a nice scenario prediction page that leads with the 2012 results map, in both traditional map and cartogram form. You can play god and flip the various states to either red or blue. But from the interaction side the designers did something really interesting. Flipping a state requires you to click and hold the state. But the speed with which it then flips is not equal for all states. Instead, the length of hold time depends upon the state’s likelihood to be a flippable state, based on the state’s partisan voter index. For example, if you try and flip Kansas, you will have to wait awhile to see the state turn blue. But try and flip North Carolina and the flip is near instantaneous.
While the geographic component remains on the right, the left-hand column features either text, or as in this other screenshot, smaller charts that illustrate the points more specifically.
Taken all together, the piece does a really nice job of presenting users with a tool to make predictions of their own. The different sections with concepts and analysis guide the user to see what scenarios fall within the realm of reason. But, what takes the cake is that flipping interaction. Using a delay to represent the likelihood of a flip is brilliant.
Credit for the piece goes to Aaron Zitner, Randy Yeip, Julia Wolfe, Chris Canipe, Jessia Ma, and Renée Rigdon.
Monday I examined a chart from the BBC that in my mind needlessly added confusing visual components to what could have been a straight table. So here we take a look at some other options that could have been used to tell the same story. The first is the straight forward table approach. Here I emphasised the important number, that of those killed. I opted to de-emphasise the years and the injured in the table. Also, since the bulk of my audience is from the United States, I used the two-letter states codes.
But let us presume we want a graphic because everyone wants everything to be visual and graphic. Here are two different options. The first takes the table/graphic from the BBC and converts it into a straight stacked bar chart, again with emphasis on the dead. I consolidated the list into a single column so one need not split their reading across both the horizontal and vertical.
And then if you examine the dates, one can find an interesting component of the data. Of the top-eight shootings, all but two occurred within the last ten years. So the second version takes the graphic component of the stacked bars from the first and places them on a timeline.
For those that wonder about the additional effort needed to create three different options from one data set, I limited myself to an hour’s worth of time. A little bit of thought after examining the data set can save a lot of time when trying to design the data display.
Over the weekend I found myself curious about the notion of a growing global middle class. So I dug up some data from the Pew Research Center and did some analysis. The linked piece here details that analysis.
I go into more detail than just a map. Hopefully you enjoy the piece and find the analysis informative if not useful.
Today we look at a piece that focuses on my native (and favourite) state: the Commonwealth of Pennsylvania. (Along with Virginia, Massachusetts, and Kentucky, we self-identify as a commonwealth and not a state.) FiveThirtyEight examines how Pennsylvania and its shifting political preferences might just be the key (get it? keystone) to the election for both candidates. The crux of the article can be seen in the map, but the whole piece is worth the read. If only because it mentions Pennsyltucky by name.
My apologies to you for the blog being down the last week and a half. This is what happens when I get 33,000 spam comments in the span of 24 hours: the blog crashes. Rest assured, I have lots of things to post.
But for today, we are picking up after a yuuugge night for Donald Trump so let’s get on with the data visualisations. Trump decisively won Connecticut, Delaware, Maryland, Pennsylvania, and Rhode Island with a majority of votes in every state. As he made sure to point out, winning 50–60% in a three-way race is quite difficult to do. Simply put, Cruz and Kasich got destroyed.
Why is that? Well a few days ago—can you tell I meant to post this then?—David Wasserman over at FiveThirtyEight posted an insightful article about the various counties thus far contested and how, when divided into quadrants based on socioeconomics and conservativeness, Trump has won three out of four quadrants. The whole article is worth the read.
Flags are cool. And I will openly admit I may have designed several of my own over the years. So thanks to my good friend for pointing me in the direction of this project from ferdio that breaks down flags across the world. If you are at all curious about how many flags use particular colours, shapes, sizes, you need not go any further.
Yesterday I took a look at the Alaskan Airlines and Virgin America merger. Part of the disappointment on the internets centres around the service and experience delivered by Virgin. I mean who doesn’t like mood lighting, right? Well the Economist took a look at international airlines by both price and service. And if we use Virgin Atlantic as the best proxy for Virgin America, you can see why people prefer it over American carriers.
As I mentioned earlier this week, I visited London for work for a week and then took some rollover holiday time to stay around London and then visit Dublin. But now I am back. And this week that has meant all the jet lag. And while everybody experiences jet lag and recovers from it differently, I wanted to take a look at my experience. The data and such is below. But the basic point, it is about four days before I return to normal.
What is missing, unfortunately, is the Chicago-to-London data. Because anecdotally, that was far, far worse than the return flight.
It’s Monday, folks. And for most of us that means going back to work. Which means dressing appropriately. And that’s about as far as I’ve got introducing this subject matter, because I wear a dress shirt and tie everyday. Not a t-shirt. But we’re talking t-shirts. Specifically their sizing.
Threadbase is a New York startup looking to do some cool things with data about t-shirts. But that requires having data with which to play. And they are starting to do just that. Their opening blog post has quite a few data visualisations.
The dot plot above charts the sizes by dimension for various brands and makes. I might quibble with the particular colours as the red and purple are a bit on the difficult side to distinguish. Symbols could be away around the issue. But the only real issue is that on my monitors the full image runs long and I lose the reference point of the actual dimensions in inches.
But the piece is worth the read for the cyclical changes in dimensions.
Mostly it’s just a pity that I’m not a jeans and t-shirt sort of guy.
Sorry for not writing the last few weeks, but I was on a much needed holiday. But I’m back now. And first things first, one of my good mates got engaged whilst I was back in Philadelphia. And so in honour of that we have today’s piece.
As the graphic might hint, it’s about marriage. The piece dates from September of last year—2015 and I think I will have to get used to that for a few weeks—and looks at the demographics of marriage mostly in the United States. The chart above in particular looks at men that are married at every age by year, i.e. how many men aged 30 were married in 1960 versus 2013.