How Mass Shootings Have Changed

A few weeks ago here in the United States, we had the mass shootings in El Paso, Texas and Dayton, Ohio. The Washington Post put together a piece looking at how mass shootings have changed since 1966. And unfortunately one of the key takeaways is that since 1999 they are far too common.

The biggest graphic from the article is its timeline.

Getting worse over time
Getting worse over time

It captures the total number of people killed per event. But, it also breaks down the shootings by admittedly arbitrary time periods. Here it looks at three distinct ones. The first begins at the beginning of the dataset: 1966. The second begins with Columbine High School in 1999, when two high school teenagers killed 13 fellow students. Then the third begins with the killing of 9 worshippers in a African Episcopal Methodist church in Charlestown, South Carolina.

Within each time period, the peaks become more extreme, and they occur more frequently. The beige boxes do a good job of calling out just how frequently they occur. And then the annotations call out the unfortunate historic events where record numbers of people were killed.

The above is a screenshot of a digital presentation. However, I hope the print piece did a full-page printing of the timeline and showed the entire timeline in sequence. Here, the timeline is chopped up into two separate lines. I like how the thin grey rule breaks the second from the third segment. But the reader loses the vertical comparison of the bars in the first segment to those in the second and third.

Later on in the graphic, the article uses a dot plot to examine the age of the mass shooters. There it could have perhaps used smaller dots that did not feature as much overlap. Or a histogram could have been useful as infrequently used type of chart.

Lastly it uses small multiples of line charts to show the change in frequency of particular types of locations.

Overall it’s a solid piece. But the timeline is its jewel. Unfortunately, I will end up talking about similar graphics about mass shootings far too soon in the future.

Credit for the piece goes to Bonnie Berkowitz, Adrian Blanco, Brittany Renee Mayes, Klara Auerbach, and Danielle Rindler.

Water, Water Everywhere Nor Any Drop to Drink Part II

Yesterday we looked at the New York Times coverage of some water stress climate data and how some US cities fit within the context of the world’s largest cities. Well today we look at how the Washington Post covered the same data set. This time, however, they took a more domestic-centred approach and focused on the US, but at the state level.

Still no reason to move to the Southwest
Still no reason to move to the Southwest

Both pieces start with a map to anchor the piece. However, whereas the Times began with a world map, the Post uses a map of the United States. And instead of highlighting particular cities, it labels states mentioned in the following article.

Interestingly, whereas the Times piece showed areas of No Data, including sections of the desert southwest, here the Post appears to be labelling those areas as “arid area”. We also see two different approaches to handling the data display and the bin ranges. Whereas the Times used a continuous gradient the Post opts for a discrete gradient, with sharply defined edges from one bin to the next. Of course, a close examination of the Times map shows how they used a continuous gradient in the legend, but a discrete application. The discrete application makes it far easier to compare areas directly. Gradients are, by definition, harder to distinguish between relatively close areas.

The next biggest distinguishing characteristic is that the Post’s approach is not interactive. Instead, we have only static graphics. But more importantly, the Post opts for a state-level approach. The second graphic looks at the water stress level, but then plots it against daily per capita water use.

California is pretty outlying
California is pretty outlying

My question is from the data side. Whence does the water use data come? It is not exactly specified. Nor does the graphic provide any axis limits for either the x- or the y-axis. What this graphic did make me curious about, however, was the cause of the high water consumption. How much consumption is due to water-intensive agricultural purposes? That might be a better use of the colour dimension of the graphic than tying it to the water stress levels.

The third graphic looks at the international dimension of the dataset, which is where the Times started.

China and India are really big
China and India are really big

Here we have an interesting use of area to size population. In the second graphic, each state is sized by population. Here, we have countries sized by population as well. Except, the note at the bottom of the graphic notes that neither China nor India are sized to scale. And that make sense since both countries have over a billion people. But, if the graphic is trying to use size in the one dimension, it should be consistent and make China and India enormous. If anything, it would show the scale of the problem of being high stress countries with enormous populations.

I also like how in this graphic, while it is static in nature, breaks each country into a regional classification based upon the continent where the country is located.

Overall this, like the Times piece, is a solid graphic with a few little flaws. But the fascinating bit is how the same dataset can create two stories with two different foci. One with an international flavour like that of the Times, and one of a domestic flavour like this of the Post.

Credit for the piece goes to Bonnie Berkowitz and Adrian Blanco.

Water, Water Everywhere Nor Any Drop to Drink

Most of Earth’s surface is covered by water. But, as any of you who have swallowed seawater can attest, it is not exactly drinkable. Instead, mankind evolved to drink freshwater. And as some new data suggests, that might not be as plentiful in the future because some areas are already under extreme stress. Yesterday the New York Times published an article looking at the findings.

More reasons for me not to move to the desert southwest
More reasons for me not to move to the desert southwest

The piece leads with a large map showing the degree of water stress across the globe. It uses a fairly standard yellow to red spectrum, but note the division of the labels. The High range dwarfs that of the Low, but instead of continuing on, the Extremely High range then shrinks. Unfortunately, the article does not go into the methodology behind that decision and it makes me wonder why the difference in bin sizes.

Of course, any big map makes one wonder about their own local condition. How stressed is Philadelphia, for example? Thankfully, the designers kept that in mind and created an interactive dot plot that marks where each large city falls according to the established bins.

Not so great, Philly
Not so great, Philly

At this scale, it is difficult to find a particular city. I would have liked a quick text search ability to find Philadelphia. Instead, I had to open the source code and search the text there for Philadelphia. But more curiously, I am not certain the graphic shows what the subheading says.

To understand what a third of major urban areas is, we would need to know the total number of said cities. If we knew that, a small number adjacent to the categorisation could be used to create a quick sum. Or a separate graphic showing the breakdown strictly by number of cities could also work. Because seeing where each city falls is both interesting and valuable, especially given how the shown cities are mentioned in the text—it just doesn’t fit the subheading.

But, for those of you from Chicago, I included my former home as a different screenshot. Though I didn’t need to search the source code, because I just happened across it scrolling through the article.

It helps having Lake Michigan right there
It helps having Lake Michigan right there

Credit for the piece goes to Somini Sengupta and Weiyi Cai.

The Tory Leadership Race: The Favourite and All the Also Rans

This piece was published Monday, so it’s one round out of date, but it still holds true. It looks at the betting odds of each of the candidates looking to enter No. 10 Downing Street. And yeah, it’s going to be Boris.

That's a pretty sizable gap
That’s a pretty sizable gap

The thing that strikes me as odd about this piece however, is note the size of the circles. Why are they larger for Boris Johnson and Rory Stewart? It cannot be proportional to their odds of victory or else Boris’ head would be…even bigger. Is that even possible? Maybe it relates to their predicted placement of first and second, the two of which go to the broader Tory party for a vote. It’s really unclear and deserves some explanation.

The graphic also includes a standard line chart. It falls down because of spaghettification in that all those also rans have about the same odds, i.e. slim, to beat Boris.

Perhaps the most interesting thing to follow is who will be the other person on the ballot. But then who remembers Andrea Leadsom was the runner up to Theresa May?

Credit for the piece goes to the Economist graphics department.

Studying Will Be the Death of Me

At least in certain fields. Happy Thursday all. For me, however, it’s more of a Friday. I am on holiday the next several days, so until I resume posting mid-next week, I will leave you with an xkcd graphic that looks at how what you study can kill you. I think all my economist colleagues are safe.

Where's design though?
Where’s design though?

Credit for the piece goes to Randall Munroe.

How Does the UK View Their Political Parties?

The United Kingdom crashes out of the European Union on Friday. That means there is no deal to safeguard continuity of trading arrangements, healthcare, air traffic control, security and intelligence deals, &c. Oh, and it will likely wreck the economy. No big deal, Theresa. But what do UK voters think about their leading political parties in this climate? Thankfully Politico is starting to collect some survey data from areas of marginal constituencies, what Americans might call battleground districts, ahead of the eventual next election.

And it turns out the Tories aren’t doing well. Though it’s not like Labour is performing any better, because polling indicates the public sees Corbyn as an even worse leader than Theresa May. But this post is more to talk about the visualisation of the results.

Of course I naturally wonder the perception of the smaller parties like the Liberal Democrats or Change UK (the Independent Group)
Of course I naturally wonder the perception of the smaller parties like the Liberal Democrats or Change UK (the Independent Group)

The graphics above are a screenshot where blue represents the Conservatives (Tories) and red Labour. The key thing about these results is that the questions were framed around a 0–10 scale. But look at the axes. Everything looks nice and evenly spread, until you realise the maximum on the y-axis is only six. The minimum is two. It gives the wrong impression that things are spread out neatly around the midpoint, which here appears to be four. But what happens if you plot it on a full axis? Well, the awfulness of the parties becomes more readily apparent.

Neither party looks very good here…
Neither party looks very good here…

Labour might be scoring around a five on Health, but its score is pretty miserable in these other two categories. And don’t worry, the article has more.  But this quick reimagination goes to show you how important placing an axis’ minimum and maximum values can be.

Credit for the piece goes to the Politico graphics department.

Per Sense

For some levity given today is Friday, let us get to the really contentious matters of late. Is the percentage sign acceptable in text? According to the AP, it now is. Thankfully, xkcd was on it and took a look at the acceptability of various forms of expressing a percentage.

I mean it could also be: p¢
I mean it could also be: p¢

Credit for the piece goes to Randall Munroe.

Angry Birds? Bad Birds

Baseball is almost upon us. And oh boy do the Baltimore Orioles look bad. How bad? Historically bad. FiveThirtyEight went so far as to chart the expected WAR, wins above replacement, of each position of all teams since 1973. And the expected Orioles lineup looks remarkably bad.

They are going to be so bad.
They are going to be so bad.

What is nice about this graphic is the use of the medium grey for each team/year combination. I may have used a filled orange dot instead of open, but the dots do at least standout and show the poor positioning of just about everything but the second baseman.

Credit for the piece goes to the FiveThirtyEight graphics department.

Individualistic Immigrants

As many of you know, genealogy and family history is a topic that interests me greatly. This past weekend I spent quite a bit of time trying to sort through a puzzle—though I am not yet finished. It centred on identifying the correct lineages of a family living in a remote part of western Pennsylvania. The problem is the surname was prevalent if not common—something to be expected if just one family unit has 13 kids—and that the first names given to the children were often the same across family units. Combine that with some less than extensive records, at least those available online, and you are left with a mess. The biggest hiccup was the commonality of the names, however. It’s easier to track a Quinton Smith than a John Smith.

Taking a break from that for a bit yesterday, I was reminded of this piece from the Economist about two weeks ago. It looked at the individualism of the United States and how that might track with names. The article is a fascinating read on how the commonness or lack thereof for Danish names can be used as a proxy to measure the individualism of migrants to the United States in the 19th century. It then compares that to those who remained behind and the commonness of their names.

But where are the Brendans?
But where are the Brendans?

The scatter plot above is what the piece uses to introduce the reader to the narrative. And it is what it is, a solid scatter plot with a line of best fit for a select group of rich countries. But further on in the piece, the designers opted for some interesting dot plots and bar charts to showcase the dataset.

Now I do have some issues with the methodology. Would this hold up for Irish, English, German, or Italian immigrants in the 19th century? What about non-European immigrants? Nonetheless it is a fascinating idea.

Credit for the piece goes to the Economist Data Team.

Another Week, Another Brexit Day

Well we have another week and so we have another fraught day of House of Commons votes on Brexit. Once again, it looks like HM Government will lose all the votes, but the question is by how much? Significant defeats means there will be little support, but smaller defeats might show the European Union that it needs to open up the Brexit Withdrawal Agreement and renegotiate it.

But that’s not all. As this piece last week from the Economist shows, the Withdrawal Agreement is just one piece—an admittedly very large piece—of many pieces of legislation that need to be passed into law to manage the UK’s withdrawal from the EU. And while some have indeed been passed, many others are languishing.

So much to do, only a handful of business days in which to do it…
So much to do, only a handful of business days in which to do it…

The piece overall is effective. It clusters the bills into those that have been passed and those still in the works. And then within each of those, the various stages of the British legislative process exist as colour-coded dots. My quibble would be with those dots. There are a few instances where dots overlap and I would have either made the dots transparent or stacked them vertically above and below the line, just to make it clearer to the reader where the dots are located.

Credit for the piece goes to the Economist Data Team.