I was not sure if I wanted to file this under either my humourous Friday posts or my regular weekday posts, but I ultimately decided to go with the weekly postings. Why?
It’s simply a different way of visualising the election results, by separating the two camps into two separate Americas. One is the geography connected by Trump’s victory, the other are those disconnected cities and geographies united around Clinton. A collection of almost Greek-like city states.
And what I can say as someone who often drove from the Chicago Sea to the Acela Channel, the United States is very much divided by economics and by culture. But in theory that is the great advantage of the pluralist, multicultural society—it allows for all people of all different types to cohabit an entire continent. Well, in theory at least.
It just won’t die. Grandma, that is, in front of the death panels of Obamacare. Remember those? Well, even if you don’t, the Affordable Care Act (the actual name for Obamacare) is still around despite repeated attempts to repeal it. So in this piece from Bloomberg, Obamacare is examined from the perspective of leaving 27 million people uninsured. In 2010, there were 47 million Americans without insurance and so the programme worked for 20 million people. But what about those remaining 27?
I am not usually a fan of tree maps, because it is difficult to compare areas. However, in this piece the designers chose to animate each section of the tree as they move along their story. And because the data set remains consistent, e.g. the element of the 20 million who gained insurance, the graphic becomes a familiar part of the article and serves as a branching off point—see what I did there?—to explore different slices of the data.
So in the end, this becomes one of those cases where I actually think the tree map worked to great effect. Now there is a cartogram in the article, that I am less sure about. It uses squares within squares to represent the number of uninsured and ineligible for assistance as a share of the total uninsured.
Some of the visible patterns come from states that refused to expand Medicaid. It was supposed to cover the poorest, but the Supreme Court ruled it was optional not mandatory and 19 states refused to expand the coverage. But surely that could have been done in a clearer fashion than the map?
Credit for the piece goes to Jeremy Scott Diamond, Zachary Tracer, and Chloe Whiteaker.
Again, the election is next week. And since I have moved from Chicago to Philadelphia, I now find myself in a contested state. This piece comes from the New York Times and explores the polling results across the blue-leaning-but-still-a-swing-state. I find it particularly interesting just how much red and purple there is in the suburban counties of Delaware, Chester, Montgomery, and Bucks all surrounding Philadelphia. But that will only make my vote matter more than it would have had were I still living in Chicago.
But you should also check out the piece for some updates on the Senate race we have going on here. The Republican Pat Toomey is running for re-election against the Democrat Katie McGinty. The race can be described as a tossup as the polls seem to be flipping back and forth. But there is some interesting polling data to be found in the article.
In about a week we will see just how Pennsylvania goes for both the presidential election and the Senate election.
Well the election is next Tuesday, and last Friday and this past weekend was…interesting. So one(ish) week to go, and we are going to turn to a few posts that use data visualisation and graphics to explore topics related to the election.
Today we start with the latest tracking polls, released on Friday. The piece comes from the Washington Post and highlights the closing gap between Clinton and Trump with a sudden spike in Republican candidate support. But what I really like about the piece is the plot below. It displays the 0 axis vertically and plots time with the most recent date at the top. And then support for the various demographics can be filtered by selectable controls above the overall plot.
Of course the really interesting bit is going to be how much this changes in the next seven days. And then what that means for the results when we all wake up on Wednesday morning.
Credit for the piece goes to Chris Alcantara, Kevin Uhrmacher, and Emily Guskin.
Well last night was the debate and it was a doozy. But while I was looking for some graphics capturing the debate itself, I came upon an article over on the Washington Post about gerrymandering. For those that do not know, gerrymandering is when state-level politicians draw the maps for congressional districts to preserve or diminish support for various representatives. And Pennsylvania is one of those states with a lot of oddly shaped districts.
Credit for the piece goes to the Washington Post graphics department.
According to this piece from FiveThirtyEight, maybe not as much as they used to be. From a data visualisation standpoint, what stuck out at me was this plot of correlations of how similar various states are. Basically, the closer to the number 1, the more similar, the closer to 0, the less.
I might question the value of placing the numbers within the squares—see what I did there?—because the colours could be used with a legend to indicate the range of similarity. But if this were an interactive piece, it certainly could be done to reveal the number on tap or mouseover.
Anyway, it was interesting to see that among swing states, Pennsylvania is least like Georgia but most like Minnesota. The former, certainly. The latter, who would have guessed, don’t ya know.
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.
And by this title I am not referencing McKinleys, K2s, or Everests. No, the BBC published this piece on the changing average heights of citizens of various countries. This was the graphic they used from the report’s author.
Personally speaking, I do not care for the graphic. It is unclear and puts undue emphasis on the 1914 figure by placing the illustration in the foreground as well as in the darkest colour. I took a thirty-minute stab at re-designing the graphic and have this to offer.
While I admit that it is far from the sexiest graphic, I think it does a better job of showing the growth than decline of national heights by each sex in each of these three select countries. Plus, we have the advantage of not needing to account for the flag emblems. Note how the black bars of Egypt disappear into the black illustration of the person.
Credit for the piece goes to the eLife graphics department.
Last week Philadelphia became the first large US city to introduce a soda tax. (Berkeley introduced one a few years ago, but is 1/10 the size of Philly.) The Guardian has a really nice write-up on how the tax was sold not on health benefits, but of civic benefits to the education system. But the article made me wonder if somebody had published a map looking at obesity in Philadelphia. Turns out Philadelphia Magazine published an article with just such a map from another source, RTI International. (You can find the full interactive map here.)
The map has three views, one of which allows you to see areas of statistically significant clustering. North and West Philly had some bright red clusters, whereas the western suburbs, in particular along the Main Line have some very cold blues.
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.