Repealing the Individual Mandate

While I am still looking for a graphic about Zimbabwe, I also want to cover the tax reform plans as they are being discussed visually. But then the Senate went and threw a spanner into the works by incorporating a repeal of Obamacare’s individual mandate. “What is that?”, some of you may ask, especially those not from the States. It is the requirement that everyone have health insurance and it comes with tax penalties if you fail to have coverage.

Thankfully the New York Times put together a piece explaining how the mandate is needed to keep premiums low. Consequently, removing it will actually only increase the premiums paid by the poor, sick, and elderly. The piece does this through illustrations accompanying the text.

Exiting the pool
Exiting the pool

Overall the piece does a nice job of pairing graphics and text to explain just why the mandate, so reviled by some quarters, is so essential to the overall system.

Credit for the piece goes to Haeyoun Park.

Trumping (Most) All on Twitter

Initially I wanted today’s piece to be coverage of the apparent coup d’état in Zimbabwe over night. But while I have found some coverage of the event, I have not yet seen a single graphic trying to explain what happened. Maybe if I have time…

In the meantime, we have the Economist with a short little piece about Trump on Twitter and how he has bested his rivals. Well, most of them at least.

Trumping one's rivals
Trumping one’s rivals

The piece uses a nice set of small multiples to compare Trump’s number of followers to those of his rivals. The multiples come into play as the rivals are segmented into three groups: political, sport, and media. (Or is that fake media?)

Small multiples of course prevent spaghetti charts from developing, and you can easily see how that would have occurred had this been one chart. But I like the use of the reddish-orange line for Trump being the consistent line throughout each. And because the colour was consistent, the labelling could disappear after identifying the data series in the first chart.

And worth calling out too the attention to detail. Look at the line breaks in the chart for the labelling of Fox News and NBA. It prevents the line from interfering with and hindering the legibility of the type. Again, a very small point, but one that goes a long way towards helping the reader.

I think the only thing that could have made this a really standout, stellar piece of work is the inclusion of another referenced data series: the followers of Barack Obama. At 97 million followers, Obama dwarfs Trump’s 42.2 million. Would it not be fantastic to see that line soaring upwards, but cutting away towards the side of the graphic would be the text block of the article continuing on? Probably easier for them to do in their print edition.

Regardless, this is another example of doing solid work at small scale. (Because small multiples, get it?)

Credit for the piece goes to the Economist Data Team.

The Demilitarised Zone

Over the weekend, the American and North Korean leaders got into an argument with the North Korean leader calling President Trump old and the American leader calling Kim Jong Un short and fat. High class diplomacy.

So what holds the North Korean army, by numbers likely not quality one of the largest armed forces in the world, back from sweeping down the Korean coastlines and overrunning Seoul? Well, that would be the role of the Demilitarised Zone, or DMZ. And thankfully yesterday, whilst your humble author was out sick, the Washington Post published a piece looking at the DMZ.

The title of the piece
The title of the piece

The piece uses a giant, illustrated in the background to provide context to the words and imagery sitting in the foreground. (That is how I justified covering it in the blog: map.) Overall the experience was smooth and informative about the sheer amount of destructive power waiting just miles north of Seoul.

Credit for the piece goes to Armand Emamdjomeh, Laris Karklis, and Tim Meko.

The Importance of Cartography

Today I wanted to share with you a piece from the BBC that explores the importance of cartography, or mapmaking, in relief efforts particularly in Malawi, a country located in southeastern Africa.

My favourite still, perhaps, was an image of a hand-drawn map
My favourite still, perhaps, was an image of a hand-drawn map

This is a still from a short video—it clocks in at just a tad under three minutes—that you can watch to see how volunteers are identifying and mapping villages that do not appear on today’s maps. The importance, as they explain, is that if the village does not appear, it is as if the village does not exist. Consequently it can be quite difficult for aid to reach these villages during disasters like the 2015 floods.

Credit for the piece goes to Ruth Evans.

Why So Many Mass Shootings?

Well, the data speaks for itself. I wanted to use this screenshot, however, to show you the story because I think it does a fantastic job. Without having to read the article, the image encapsulates what is to come in the article.

Just the visual impact of the outlier
Just the visual impact of the outlier

That said, there are a few other scatter plots worth checking out if the topic is of interest. And the explanation of the data makes all the more sense.

But I really loved the impact of that homepage.

Credit for the piece goes to Max Fisher and Josh Keller.

Voting in Virginia

Today is Election Day here in the States, but neither for the presidency nor for Congress. 2017 is an off-year, but it does have a few interesting races worth following. One is the New Jersey gubernatorial election across the river here from Philadelphia. Further down the Northeast Corridor we have the gubernatorial election in Virginia. And then I am going to be following the special election for a Seattle suburb’s state-level district. Why? Because it all gets to setting the table for 2022.

These three elections are all important for one reason, they relate to the idea of solid political control of a state government. The analogy is what we have in Washington, DC where the Republicans control the executive branch and both chambers of the legislative branch. In New Jersey, Democrats control the state legislature while (in?)famous Chris Christie, a Republican, is governor. In Virginia, Terry McAuliffe, a Democrat, is governor whilst the General Assembly is solidly Republican—we will get to that in a minute, trust me—and finally in Washington, the governorship is Democratic, the lower chamber of the state legislature is Democratic, but the state senate is Republican by one seat. And one of those very seats is up for a special election today.

So why am I making the big deal about this? Because solid political control of a state allows for biased redistricting, or gerrymandering, in 2020, when the US Census will reapportion seats to states, and thereby electoral college votes. If the Republicans win in Virginia, which is possible in what the polls basically have as a toss-up, they can redistrict Virginia to make it even harder for Democrats to win. And if the Democrats win in New Jersey and Washington, as they are expected to, they will be able to redistrict the state in their favour. Conversely, if the Democrats win in Virginia, and Republicans in New Jersey and Washington, they can thwart overly gerrymandered districts.

Which gets us to Virginia and today’s post. (It took awhile, apologies.) But as the state of Virginia changes, look at the dynamic growth in northern part of the state over the past decade, how will the changing demographics and socio-economics impact the state’s vote? Well, we have a great piece from the Washington Post to examine that.

The growth has been in northern Virginia thus far
The growth has been in northern Virginia thus far

It does a really nice job of showing where the votes are, in northern Virginia, and where the jobs are, again in northern Virginia. But how southern Virginia and Republicans in the north, might have just enough votes to defeat Democratic candidate Ralph Northam. The last polls I saw showed a very narrow lead for him over Republican Ed Gillespie. Interestingly, Gillespie is the very same Gillespie who architected the Republican’s massive victory in 2010 that obviously shifted the House of Representatives to the Republicans, but more importantly, shifted state legislatures and governorships to the Republicans.

That shift allowed for the Republicans to essentially stack the deck for the coming decade. And so even though in 2016, Democrats won more votes for the House of Representatives, they have far fewer seats. Even if there is a groundswell of new support for them in 2018, that same gerrymandering will make it near impossible for the Democrats to win the House. And so these votes in Virginia, New Jersey, and Washington state are fun to follow tonight—I will be—but they could also lay the groundwork for the elections in 2022 and 2024.

Basically, I just used today’s post to talk about why these three elections are important not for today, but for the votes in a few years’ time. But you really should check out the graphic. It makes nice use of layout, especially with the job bar chart organised by Virginia region. Overall, a solid and terrific piece.

Credit for the piece goes to Darla Cameron and Ted Mellnik.

Phillip’s Curves are Flatlining

I’ve worked on a few scatter plots of late and so this piece from the Economist grabbed my attention. It examines the correlation between unemployment rates and inflation rates. Broadly speaking, the theory has been that low unemployment rates lead to high inflation rates. But the United States has had low unemployment rates now for a few years, but inflation is around that ideal 2% realm. This theory is called the Phillips Curve.

Straightening out the curve…
Straightening out the curve…

The graphic does a nice job of showing three data series all in one plot. Normally, I would argue for splitting the chart into three smaller plots, a la the small multiples. But here, the data aligns just well enough that the overlapping is minimal. And smart colour choices mean that each data range appears clearly separate from the rest. A nice thoughtful addition is the annotations to the time period are set in the same colour as the dots themselves.

My only two quibbles: One, I would probably increase the height of the chart to better show the trend line. I find that for scatter plots, a more squarish profile works better than the long rectangle. Overall, though, a really well done chart. Second, I would consider adding a zero line to the x-axis to show 0% cyclical unemployment. But that might also not be terribly useful, because you can see how the curve should move regardless of that natural line.

Full disclosure: the Economist article cites a paper from the Philadelphia Fed Research Department, which employs me.

Credit for the piece goes to the Economist Data Team.

Murder Rates in the US

Yesterday we looked at an article about exporting guns from one state to another. After writing the article I sat down and recalled that the copy of the Economist sitting by the sofa had a small multiple chart looking at murders in a select set of US cities. It turns out that while there was a spike, it appears that lately the murder rate has been flat.

Chicago is higher than Philly, to be fair
Chicago is higher than Philly, to be fair

It’s a solid chart that does its job well. That is probably why I neglected to mention it until I realised it fit in with the map of Illinois and talk about gun crimes yesterday. Because there is plenty of other news through data visualisation that we can talk about this week.

Credit for the piece goes to the Economist Data Team.

Exporting Guns

When I lived in Chicago, people back East would always ask if I was worried about murder and gun crime in Chicago. My reply was always, “no, not really”. Why? Because I lived in generally safe neighbourhoods. But on that topic, the second most numerous question/comment was always, why are the strict gun laws in Chicago not preventing these crimes? More often than not the question had more to do with saying gun control laws were ineffective.

But in Chicago, it seemed to me to be fairly common knowledge that most of the guns people used to commit crimes were, in fact, not purchased in Illinois. Rather, criminals imported them from neighbouring states that had far looser regulations on firearms.

Today’s post from FiveThirtyEight looks at that very topic.

They bring back more than just cheese from Wisconsin…

They bring back more than just cheese from Wisconsin…I am not the biggest fan of the maps that they use, although I understand why. Most Americans would probably not be able to name the states bordering Illinois, California, or Maryland—the two other states examined this way—and this helps ground the readers in that geographically important context. But, thankfully the designers opted for another further down in the article that explores the data set in a more nuanced approach. Surprise, surprise, it’s not that simple of an issue.

Credit for the piece goes to Rachael Dottle.