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.

An Ailing Graphic on the Healthcare Labour Force

I know I have said it before, but I like the increasing number of graphics-led articles published by Politico. Many policy and politics stories are driven—or should be driven—by data. But, myself included, we cannot hit it out of the park at every plate appearance. And that is what we have from Politico today, actually last week.

The graphic focuses on the healthcare industry and its need for a larger labour force in coming years as the baby boomers continue to age and start to retire. If their own doctors retire along with them, who will be their new doctors?

But there are two components of the graphic on which I want to focus. The first is the projection of the number of registered nurses (RNs) in 2024 compared to a 2014 baseline.

We need more. Just more.
We need more. Just more.

The story focuses on the future condition, but that colour is set to the lighter green thus drawing the reader’s eyes to the 2014 data point. Flipping those two colours would shift the focus of the chart to the 2024 timeframe, which would better match the text above.

Then we have the design decision to include a line chart for the growth rate, presumably total, for each category of RN from 2014 to 2024. The problem is that the chart itself does not sit on any baseline. While I do not care for the dual axis chart, that format at least keeps an axis legend on the right side of the chart. (You still have the problem of implying certain things based on what scale you choose to use relative to the first data series.) Here, because there is no chart lines associated with the growth data, I wonder if a table below the x-axis labels would be more efficient? Home health care, a very small category, will have the highest growth (a small change from a small base will beat the same small change or even slightly bigger changes from a far larger base) but the eye has the furthest to travel to reach the 61% number from the top of the bars or the labelling.

The other component I wanted to discuss is the scatter plot that compares the number of jobs to their average salary.

Bursting these bubbles…
Bursting these bubbles…

But this is a bubble chart, not a scatter plot, and so we have a third variable encoded in the size of the dot/bubble. The first thing I looked for was a scale for the size of the circles. What magnitude is the RN circle vs. the Personal Care Aides circle? There is none, but unfortunately that seems to be a common practice with bubble chart. But after failing to find that, I noticed that the circles decrease in size from right to left. That was when I looked to the legend and saw the y-axis in numbers of jobs and the x-axis in average salary. But then the circles are sized in proportion to the average salary of each profession to the other. In other words, the circles are basically re-plotting the x-axis. The physical therapist circle should be roughly twice as large, by area, than the vocational nurses. But we can also just see by the x-axis coordinates. The bubble chart-ness of the chart is unnecessary and the data could be told more clearly by stripping that away and making a straight-up scatter plot where all the circles are sized the same.

Credit for the piece goes to Christina Animashaun.

What Does Your Name Mean? (Country-level Edition)

Shoutout to my very good friend Jonathan Fairman for passing this one along.

Browsing the internets, I often find these little adverts saying something about “10 Things You Didn’t Know About Your Name” or “10 Things Your Name Says About You”. They grab my attention because, as you all know, genealogy is kind of a thing that I do and I am curious where lots of names in my family come from.

But where do countries names originate? We all probably know America comes from Amerigo Vespucci. But how about Mexico? Thankfully Quartz put together a piece exploring country name origins. And it turns out that most can be grouped into four different types. Being named after a man, like America, well you guessed it, that’s one of the four.

Tribal names are another…
Tribal names are another…

Credit for the piece goes to Thu-Huong Ha.

Flaking Out of the Senate

This has been a busy week. I am working on a small piece on the Red Sox managers in the free agency period—I thought it would be ready yesterday, but not so much—but news continues to happen outside of the baseball world. Some of the biggest, at least in the US, would have to be the speech by Senator Flake of Arizona who announced he would not seek re-election in 2018.

So cue the politically-themed graphics. Today’s piece comes from the Washington Post. The graphic itself is not terribly complex as it is a scatter plot comparing the liberal/conservativeness of senators with how their respective state voted in 2016.

Scrolling through the piece changes the graphic
Scrolling through the piece changes the graphic

But what the piece does really well is weave a narrative through the chart. Scrolling down the page locks the graphic in place while the text changes to provide new context. And then different dots are highlighted or called out.

It proves that not all the best graphics need to be terribly complex.

Credit for the piece goes to Kevin Schaul and Kevin Uhrmacher.

The Ratio

And I’m not talking about walking into a bar late at night. Instead, I am talking about the ratio of likes to retweets to replies, which, for those of you unfamiliar with the service, refers to engagement with a person’s tweets on Twitter.

The Ratio does not come from FiveThirtyEight—read the article for the full background on the concept, it is well worth the read—but they applied it to President Trump, whom we all know has a penchant for tweeting. The basic premise of the ratio is that you want more retweets and likes than replies. Think of it like customer reviews. Rarely do people bother to put the effort in to complement good service, but they will often write scathing reviews if something does not fit their expectations. Same in Twitter. If I do not care for what you say, I will let you know. But if I do, it is easy for me to like it, or even retweet it.

Anyway, the point is they took this and applied it to the tweets of Donald Trump and received this chart.

The interactivity makes this chart worth checking out
The interactivity makes this chart worth checking out

What I truly enjoy is the interactivity. Each dot reflects a tweet, and you can reveal that tweet by hovering over it. (I would be curious to know if the dots move. That is, do they, say, refresh daily with new tabulations on the updated numbers of likes, retweets, and replies?)

But the post goes on using the same chart form, in both other interactive displays and as static, small multiple pieces, to explore the political realm of previous tweeting presidents and current senators.

A solid article with some really nice graphics to boot.

Credit for the piece goes to Oliver Roeder, Dhrumil Mehta, and Gus Wezerek.

Speaking Freely About Free Speech

Last week the Economist published an article looking at the attitudes of the young at university in the United States. The examination was sparked by the recent-ish waves of news about stifled speech on campuses. Thankfully, we have a long-running survey from those on the ground in our universities and it reveals some interesting facts. You should head on over to the article if you want the full set, but in general, to perhaps nobody’s surprise, the media is exaggerating the confrontations we have seen.

You said what?
You said what?

My only quibble with the graphic is the height of the small multiples. I probably would have increased the height a little bit to allow any real fluctuations over the years to show more readily. But, for all I know, that could have been a limitation of the space in which the designers had to work, i.e. converting a print graphic to work on their blog.

Credit for the piece goes to the Economist’s Data Team.