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.

Quantifying Part of the Opioid Crisis

Two weeks ago the Washington Post published a fascinating article detailing the prescription painkiller market in the United States. The Drug Enforcement Administration made the database available to the public and the Post created graphics to explore the top-line data. But the Post then went further and provided a tool allowing users to explore the data for their own home counties.

The top line data visualisation is what you would expect: choropleth maps showing the prescription and death rates. This article is a great example of when maps tell stories. Here you can clearly see that the heaviest hit areas of the crisis were Appalachia. Though that is not to say other states were not ravaged by the crisis.

There are some clear geographic patterns to see here
There are some clear geographic patterns to see here

For me, however, the true gem in this piece is the tool allowing you the user to find information on your county. Because the data is granular down to county-level information on things like pill shipments from manufacturer to distributor, we can see which pharmacies were receiving the most pills. And, crucially, which manufacturers were flooding the markets. For this screenshot I looked at Philadelphia, though I only moved here in 2016, well after the date range for this data set.

It could be worse
It could be worse

You can clearly see, however, the designers chose simple bar charts to show the top-five. I don’t know if the exact numbers are helpful next to the bars. Visually, it becomes a quick mess of greys, blacks, and burgundies. A quieter approach may have allowed the bars to really shine while leaving the numbers, seemingly down to the tens, for tables. I also cannot figure out why, typographically, the pharmacies are listed in all capitals.

But the because I lived in Chicago for most of the crisis, here is the screenshot for Cook County. Of course, for those not from Chicago, it should be pointed out that Chicago is only a portion of Cook County, there are other small towns there. And some of Chicago is within DuPage County. But, still, this is pretty close.

Better numbers than Philly
Better numbers than Philly

In an unrelated note, the bar charts here do a nice job of showing the market concentration or market power of particular companies. Compare the dominance of Walgreens as a distributor in Cook County compared to McKesson in Philadelphia. Though that same chart also shows how corporate structures can obscure information. I was never far from a big Walgreens sign in Chicago, but I have never seen a McKesson Corporation logo flying outside a pharmacy here in Philadelphia.

Lastly, the neat thing about this tool is that the user can opt to download an image of the top-five chart. I am not sure how useful that bit is. But as a designer, I do like having that functionality available. This is for Pennsylvania as a whole.

For Pennsylvania, state-wide
For Pennsylvania, state-wide

Credit for the piece goes to Armand Emamdjomeh, Kevin Schaul, Jake Crump and Chris Alcantara.

The Sartorial Trump

Happy Friday, all. We made it to the end of the week. Though if you are like me, i.e. living on the East Coast, welcome to Hell. As in so hot and humid.

So last month President Trump visited the United Kingdom on a state visit. He drew attention to himself not just because of his rhetoric, but also for his fashion choices. Consequently, the Washington Post published a piece about those fashion choices from the perspective of a professional tailor.

Look at those shoulders…
Look at those shoulders…

The overall piece is well worth a read if you find presidential fashion fascinating. But how does it qualify for Coffeespoons? A .gif that shows how Trump would look in a properly tailored suit.

Since this is a screenshot, you miss the full impact. The piece is an animation of an existing photo and how that then morphs into this for comparison’s sake.

I really enjoy the animated .gif when it works for data visualisation and story-telling.

Credit for the piece goes to Ezra Paul.

New Orleans Dodges a Bullet

Hurricane/tropical storm Barry has been dumping rain along the Gulf Coast for a few days now. But prior to this weekend, the biggest concern had been for the city of New Orleans, which sits besides the swollen Mississippi River. The river was running already high at 17 feet above normal, and with storm surges and tropical rain levels forecast, planners were concerned not with the integrity of the city’s levee system, rebuilt in the aftermath of Hurricane Katrina, but simply whether they would be tall enough.

So far, they have been.

The Washington Post tracked Barry’s course with the usual graphics showing forecast rainfall amounts and projected tracks. However, the real stunner for me was this cross section illustration of New Orleans that shows just how much of the central city sits below sea level. The cross section sits above a map of the city that shows elevation above/below sea level as well as key flood prevention infrastructure, i.e. levees and pumping stations.

A lot floodable space
A lot floodable space

The unmentioned elephant remains however. The National Oceanic and Atmosphere Administration’s extreme climate change impact forecast says the water around New Orleans might rise by nearly 13 feet by 2100. Clearly, that is still well below the 20 feet levees of today. But what if there were to be a 17 feet high Mississippi River atop the additional 13 feet? 30 feet would flood the city.

Credit for the piece goes to John Muyskens, Armand Emamdjomeh, Aaron Steckelberg, Lauren Tierney, and Laris Karklis.

Put Your Phone Down

This isn’t really a graphic so much as it is an x-ray photograph. But I also can’t get it out of my head. We all know that mobile phones has changed the way we live. But now we have evidence that our use of them is changing us physically. Young people are growing horns or spikes at the back of their skull. Don’t believe, photo:

Cool, but also frightening
Cool, but also frightening

The article in the Washington Post from which I screen captured the image is well worth a read. But I advise you to not do it on a mobile phone.

Credit for the piece goes to the study’s authors.

Natural Disasters

Today’s piece is another piece set against a black background. Today we look at one on natural disasters, created by both weather and geography/geology alike.

The Washington Post mapped a number of different disaster types: flooding, temperature, fire, lightning, earthquakes, &c. and plotted them geographically. Pretty clear patterns emerge pretty quickly. I was torn between which screenshots to share, but ultimately I decided on this one of temperature. (The earthquake and volcano graphic was a very near second.)

Pretty clear where I'd prefer to be…
Pretty clear where I’d prefer to be…

It isn’t complicated. Colder temperatures are in a cool blue and warmer temperatures in a warm red. The brighter the respective colour, the more intense the extreme temperatures. As you all know, I am averse to warm weather and so I will naturally default to living somewhere in the upper Midwest or maybe Maine. It is pretty clear that I will not really countenance moving to the desert southwest or Texas. But places such as Philadelphia, New York, and Washington are squarely in the blacked out or at least very dark grey range of, not super bad.

Credit for the piece goes to Tim Meko.

Angela from Jamestown

Today we move from royalty to slavery. Earlier this week the Washington Post published an article about an African woman (girl?) named Angela. She was forcibly removed from West Africa to Luanda in present-day Angola. From there she was crammed into a slave ship and sent towards Spanish colonies in the Caribbean. Before she arrived, however, her ship was intercepted by English pirates that took her and several others as their spoils to sell to English colonists.

The article is a fascinating read and for our purposes it makes use of two graphics. The one is a bar chart plotting the Atlantic slave trade. It makes use of annotations to provide a rich context for the peaks and valleys—importantly it includes not just the British colonies, but Spanish and Portuguese as well.

My favourite, however, is the Sankey diagram that shows the trade in 1619 specifically, i.e. the year Angela was transported across the Atlantic.

Too many people took similar routes to the New World.
Too many people took similar routes to the New World.

It takes the total number of people leaving Luanda and then breaks those flows into different paths based on their geographic destinations. The width of those lines or flows represents the volume, in this case people being sold into slavery. That Angela made it to Jamestown is surprising. After all, most of her peers were being sent to Vera Cruz.

But the year 1619 is important. Because 2019 marks the 400th anniversary of the first slaves being brought into Jamestown and the Virginia colony. The Pilgrims that found Plymouth Bay Colony will not land on Cape Cod until 1620, a year later. The enslavement of people like Angela was built into the foundation of the American colonies.

The article points out how work is being done to try and find Angela’s remains. If that happens, researchers can learn much more about her. And that leads one researcher to make this powerful statement.

We will know more about this person, and we can reclaim her humanity.

For the record, I don’t necessarily love the textured background in the graphics. But I understand the aesthetic direction the designers chose and it does make sense. I do like, however, how they do not overly distract from the underlying data and the narrative they present.

Credit for the piece goes to Lauren Tierney and Armand Emamdjomeh.

An Illustrated Guide to the Deaths in Game of Thrones

Did something important happen yesterday in the news? We’ll get to it. But for now, it’s Friday. You’ve made it to the weekend. So sit back and binge. On gin or Game of Thrones, whatever.

Last Sunday the hit HBO show Game of Thrones returned for its final series. I did not have time to post about this piece then, but thankfully, not much has changed.

It details all the on-screen deaths in the show. (Spoiler: a lot.) It includes the series in which they died, the manner of their death, who killed them, and some other notable information. Remarkably, it is not limited to the big characters, e.g. Ned Stark. (If that is a spoiler to you, sorry, not sorry.) The piece captures the deaths of secondary and tertiary characters along with background extras. The research into this piece is impressive.

Don’t worry, if you haven’t seen the show, this spoils only some extras and I guess the locations the show has, well, shown.

Don't go beyond the Wall
Don’t go beyond the Wall

Thankfully, by my not so rigourous counting, last week added only four to the totals on the page (to be updated midway and after the finale).

In terms of data visualisation, it’s pretty straightforward. Each major and minor character has an illustration to accompany them—impressive in its own right. And then extras, e.g. soldiers, are counted as an illustration and circles to represent multiples.

For me, the impressive part is the research. There is something like over 60 hours of footage. And you have to stop whenever there is a battle or a a feast gone awry and count all the deaths, their manners, identify the characters, &c.

Credit for the piece goes to Shelley Tan.

The Long and Winding Road

This Washington Post piece caught my eye earlier this week. It takes a look back at all the departures from the Trump administration, which has been beset by one of the highest turnover rates of all time.

So many names.
So many names.

What I like about the piece is how it classifies personnel by whether or not they require Senate confirmation. For example, Ryan Zinke as Interior Secretary had to be approved by the Senate. Nick Ayers, Pence’s former chief of staff, did not.

Importantly each name serves as a link to the story about the person’s departure. It serves as a nice way of leading the user to additional content while keeping them inside the graphic.

The further down the piece you go, there are notable sections where blocks of body copy appear in the centre of the page. These provide much more context to the comings and goings around that part of the timeline.

Credit for the piece goes to Kevin Schaul, Reuben Fischer-Baum, and Kevin Uhrmacher