From Frying Pan to the Fires of a War Zone

Moving away from climate change now, we turn to the lovely land of Afghanistan. While the Trump administration continues to negotiate with the Taliban in hopes of ending the war, the war continues to go worse for Afghanistan, its government, and its allies, including the United States.

It is true that US and NATO ally deaths are down since the withdraw of combat troops in 2014. But, violence and sheer deaths are significantly up. And as this article from the Economist points out, the deaths in Afghanistan are now worse than they are in Syria.

The beginning of the article uses a timeline to chart the history of Afghan conflicts as well as the GDP and number of deaths. And it is a fascinating chart in its own right. But I wanted to share this, a small multiples featuring graphic looking at the geographic spread of deaths throughout the country.

Getting hotter (because red obviously means heat)
Getting hotter (because red obviously means heat)

It does a nice job by chunking Afghanistan into discrete areas shaped as hexagons and bins deaths into those areas. All the while, the shape remains roughly that of Afghanistan with the Hindu Kush mountain range in particular overlaid. (Though, I am not sure why it is made darker in the 2003–04 map.)

To highlight particular cities or areas, hexagons are outlined to draw attention to the population centres of interest. But overall, the rise in violence and deaths is clear and unmistakable. And it has spread from what was once pockets in the south to the whole of the country that isn’t mountains or deserts.

Tamerlane would be proud.

Credit for the piece goes to the Economist graphics department.

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.

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.

It’s Boris Time, Baby

Today Boris Johnson begins his premiership as the next prime minister of the United Kingdom. He might not be popular with the wide body of the British population, but he is quite popular with the Conservative base.

The Economist looked at how Boris polled on several traits, e.g. being more honest than most politicians, compared to his prime minister predecessors before they entered office. And despite being broadly unpopular outside the Tories, he still polls better than most of his predecessors.

Boris rates higher than many previous prime ministers before they came to power
Boris rates higher than many previous prime ministers before they came to power

Design wise, it’s a straight-forward use of small multiples and bar charts. I find the use of the light blue bar a nice device to highlight Boris’ position amongst his peers.

But now we see where Boris goes, most importantly on Brexit.

Credit for the piece goes to the Economist graphics department.

The Rent Is Too Bloody High

This is a graphic from the Guardian that sort of mystified me at first. The article it supports details how the rising rents across England are hurting the rural youth so much so they elect to stay in their small towns instead of moving to the big city.

But all those segments?
But all those segments?

The first thing I noticed is that there really is no description of the data. We have a chart looking at something from 1997 and comparing it to 2018. The title is more of a sentence describing the first pair of bars. And from that title we can infer that these bars are income changes for the specified move, e.g. Sunderland to York, for the specified year. But a casual reader might not pick up on that casual description.

Then we have the issue of the bars themselves. What sort of range are we looking at? What is the min? The max? That too is implied by the data presented in the bars. Well, technically not the bars, but in the numbers at the end of each bar. I will spare you the usual rant about numbers in graphics defeating the purpose of graphics and organisation vs. visual relationship. Instead, the numbers here are essential because we can use them to suss out the scale of the grey bars. After looking at a few bars, we can tell that the white lines separating the grey boxes are most likely 10% increments. And from that we can gather the minimum is about -40% and the maximum 100%. But instead of making the reader work to figure this out, would not some min/max labels at the bottom of the chart be far clearer?

And then there is the issue of the grey boxes/bars themselves. Why are they there in the first place? If the dataset were more about an unmet value, say reservoirs in towns were only at x% of capacity, the grey bars could relate the overall capacity and the coloured bars the actual values. But here, income is not a capacity or similar type of value. It could expand well beyond the 100% or decline beyond the -40%. These bars imply the values are trapped within these ranges. I would instead drop the grey bars entirely and let the coloured bars exist on their own.

Overall this is a confusing graphic for a fascinating article. I wish the graphic had been a little bit clearer.

Credit for the piece goes to the Guardian’s graphics department.

Regional Power Plays

One of the things we missed covering last week whilst I was on holiday? The dust up in the Gulf of Oman, located near the Strait of Hormuz, where two foreign ships were attacked by mines or other explosive devices. The United States blames Iran and, of course, Iran denies it. The thing is, an inordinate amount of oil flows through the Strait, connecting the petroleum-driven economies of the West to the instability in the Middle East. Thankfully we have a graphic from the Guardian to explain just what is going on there.

Not shown: the US, the EU, China, and Russia
Not shown: the US, the EU, China, and Russia

The above is a screenshot from the article, one of several graphics. There is a stacked bar chart showing the total volume of oil in transit, and the Strait’s share of it. Spoiler: it’s significant. We all know how I feel about stacked bars: not the biggest fan.

There are, of course, locator maps showing the locations of the attacked ships. We also have some photographs showing the damage inflicted upon the tankers, as well as some evidence of what the US claims is Iranian activities. (Side note: isn’t it great that when the US really wants the world to trust its intelligence agencies the White House has been doing nothing but trashing said intelligence agencies?)

The above, however, is a simple map showing the political fault line in the Middle East. It gets to the heart of the potential conflict here being not a US vs. Iran war, but a Saudi Arabia vs. Iran war. After all, relations between the Saudis and the Trumps have warmed significantly since the Obama administration. And not shown in the map is the role of Israel, which, again has seen a significant warming in relations between Trump and Netanyahu, and which has also been quietly supporting Saudi Arabia in its undeclared war against Iran, to date fought only with proxies, most notably in Yemen.

In other words, the Middle East is a complicated and complex tinder box, built next to a few nuclear reactors, all of which just happen to sit atop vast reserves of oil and natural gas. So the best thing to do? Clearly start exploding things.

Credit for the piece goes to the Guardian graphics department.

Bar Chart Bombshells

Tuesday night/Wednesday morning, the New York Times broke the story that they had some of President Trump’s tax return information. For decades now, US presidents and candidates for that office have released their tax returns for the public to inspect. Trump has refused, often claiming that they are under audit from the IRS and then adding, and falsely claiming, they cannot be released whilst under audit. Consequently, when the Times publishes an article at the secret world of Trump’s finances, it’s a big news thing.

Unfortunately, the Times only had access to what are essentially summary transcripts of the returns. And only for a period in the mid-1980s through mid-1990s. So we cannot get the granular data and make deeper insights. But what we did get was turned into this bold graphic in the middle of the article.

That's a whole lotta red. And not the good kind for a Republican.
That’s a whole lotta red. And not the good kind for a Republican.

Conceptually, there is not much to say. The bar charts are a solid choice to represent this kind of data. Red makes sense given the connotation of “being in the red”. And the annotations providing quotes from Trump about his finances for the years highlighted provide excellent context.

What the screenshot does not truly capture, however, is the massiveness of the chart in the context of the rest of the article. It’s big, bold, and red. That design choice instead of, say, making it a smaller sidebar-like graphic, goes a long way in hitting home the sheer magnitude of these business losses.

Sometimes it’s not always fancy and shiny charts that garner the most attention. Sometimes an old staple can do wonders.

Credit for the piece goes to Rich Harris and Andrew Rossback.

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.

Redaction Action

Last week, the Department of Justice released the Mueller Report. It was—and still is—sort of a big deal. But this week I want to take a look at a few different approaches to covering the report in the media. We will start with a piece from Vox on the redactions in the report. After all, we only know what we know. And we know there is about 7% of the report we do not know. And we do not know what we do not know.

7% is still a fair amount of black when it's concentrated in one section.
7% is still a fair amount of black when it’s concentrated in one section.

The above graphic looks at overall redactions as images of each page show how much was withheld from the public. Then we have a small donut chart to show that 7.25% was redacted. Did it need to be a donut? No. A simple factette could have worked in its place. It could be worse, though, it could be a similarly sized pie chart.

The rest of the article moves on to a more detailed analysis of the redactions, by section, type, &c. And this screenshot is one of the more interesting ones.

Different coloured sharpies
Different coloured sharpies

Fundamentally we have stacked bars here, with each section’s redactions per page broken down by type. And that is, on the one hand, useful. Of course, I would love to see this data separated out. That is, show me just “investigative technique” and filter out the rest. Imagine if instead of this one chart we had four slightly smaller ones limited to each type of redaction. Or, if we kept this big one and made four smaller ones showing the redaction types.

Overall the article does a really nice job of showing us just what we don’t know. Unfortunately, we ultimately just don’t know what we don’t know.

Credit for the piece goes to Alvin Chang and Javier Zarracina.

The US Flies Alone

On Sunday, a Boeing 737 Max 8 aircraft crashed shortly after taking off from the airport in Addis Ababa, Ethiopia. This was the second crash in less than a year, since the another 737 Max 8 crashed into the sea shortly after taking off from Jakarta, Indonesia. And in the intervening months, there have been numerous reports to American regulators from pilots of problems with aircraft in flight. Unsurprisingly, international regulators have begun to take steps to protect their skies and their passengers from what might be an unsafe aircraft. American regulators, the Federal Aviation Administration, remains unconvinced.

Consequently, the New York Times put together a graphics-driven article that details just how extensive the global grounding of 737 Max 8 aircraft has been in the last 24 hours.

There's a lot more orange than blue.
There’s a lot more orange than blue.

It’s a route map to headline the article. And it shows that almost all aircraft on 737 Max 8 routes, except for those in Canada and the United States, have been grounded.

The rest of the article makes use of more maps highlighting the countries who civil aviation authorities have grounded flights and popular routes. It also includes a bar chart showing how many 737 Max 8 aircraft are in use with each airline and how many of those airlines have had their fleets grounded.

Overall, it’s a strong article that makes great use of graphics to illustrate its point about the magnitude of the grounding and the isolation of the United States and Canada.

Credit for the piece goes to Denise Lu, Allison McCann, Jin Wu, and K.K. Rebecca Lai.