charting – Page 7 – Coffee Spoons

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.

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.

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

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.

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.

The Ebola Outbreak in the Congo

Ebola, which killed 11,000 people in West Africa in 2014 (which I covered in a couple of different posts), is back and this time ravaging the Congo region, specifically the Democratic Republic of the Congo (DRC). The BBC published an article looking at the outbreak, which at 1,400 deaths is still far short of the West Africa outbreak, but is still very significant.

That's looking like a tenuous border right now… — That’s looking like a tenuous border right now…

The piece uses a small multiples of choropleths for western Congo. The map is effective, using white as the background for the no case districts. However, I wonder, would be more telling if it were cases per month? That would allow the user to see to where the outbreak is spreading as well as getting a sense of if the outbreak is accelerating or decelerating.

The rest of the article features four other graphics. One is a line chart that also looks at cumulative cases and deaths. And again, that makes it more difficult to see if the outbreak is slowing or speeding up. Another is how the virus works and then two are about dealing with the virus in terms of suits and the containment camps. But those are graphics the BBC has previously produced, one of which is in the above links.

Credit for the piece goes to the BBC graphics department.

Or Just Don’t Be a Dick

Long before I worked as a designer, I was a busboy. After that I was a dishwasher. After that I was a barista. Then I became a designer. This graphic from Indexed resonated with me, because, yeah, at a more basic level, don’t fuck with your servers.

Or, in simpler terms, don't be a dick… — Or, in simpler terms, don’t be a dick…

Credit for the piece goes to Jessica Hagy.

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.

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

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.

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.

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.

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 Rise of White Nationalist Terrorism

Whilst I was on holiday, a terrorist killed nearly fifty people in Christchurch, New Zealand. Except this time, he was a white man and the victims were all Muslims. Admittedly, I really did not read much about it until I returned to the States, but it clearly is not a thing I was expecting out of New Zealand. But the Economist looked at the question of whether this shooting is more of another in a pattern or a one-off.

The graphic does a fairly good job of showing the increasing frequency of right-wing/white nationalist terror attacks. From a design standpoint, the nice touch is the use of transparency to show overlapping events. For example, the concentric circles for Utoya and Oslo show the two Anders Breivik attacks in Norway.

You could arguably say the treatment begins to fail, however, in the US/Canada timeline. Here, regrettably, there are often too many attacks in too close proximity that the dots are too overlaid. Here I wonder if some other method of stacking or offsetting the incidents could work.

Credit for the piece goes to the Economist data team.