Greenland Is Melting

There is a lot going on in the world—here’s looking at you Brexit vote today—but I did not want to miss this frightening article from the BBC on the melting of Greenland’s ice. It’s happening. And it’s happening faster than thought.

There are several insightful graphics, including the standard photo slider of before and after, a line chart showing the forecast rise of sea levels within the possible range. But this one caught my eye.

Alarming rates along the coast.
Alarming rates along the coast.

The colour palette here works fairly well. The darkest reds are not matched by a dark blue, but that is because the ice gain does not match the ice loss. Usually we might see a dark blue just to pair with a dark red, but again, we don’t because the designers recognised that, as another chart shows, the ice loss is outweighing the gains, though there are some to be found most notably at the centre of the ice sheets. This is a small detail, but something that struck me as impressive.

My only nitpick is that the legend does not quantify the amounts of gain or loss. That could show the extremes and reinforce the point that the loss is dwarfing the gain.

Credit for the piece goes to the BBC graphics department.

Hong Kong Identity

One of the things I have been following closely the last few months has been the protests in Hong Kong. The city is one of China’s few Special Administrative Regions—basically the former British colony of Hong Kong and the former Portuguese colony of Macau, two cities bordering mainland China and separated by the Pearl River estuary.

Long story short, but since 1997 Hong Kong should enjoy 50 years of a legal system that is more aligned to that of its former status of a British colony than that of mainland China. But increasingly since Xi Jinping took power, he has been eroding those rights and the youth of Hong Kong have taken to the streets to protest, a right they enjoy but not the rest of mainland Chinese.

And so we have a survey looking at the identity by which those people living in Hong Kong choose to identify.

And it’s not Chinese.

Not a good trend for Beijing
Not a good trend for Beijing

From a news perspective, this poses problems for a Beijing-based Chinese government that is making pains to promote a greater Chinese identity throughout the world, least of all by pushing for a reunification with Taiwan by force if necessary. A generation of several million Hong Kongers and the way they raise their children, in addition to their friends and supporters abroad, weakens the authority of Beijing.

Hence the threat of a Tiananmen Square style crackdown on Hong Konger protestors.

Alas, the United States has been far more concerned with its trade dispute than it has been the democratic and human rights of several million people. At least, that is the impression given by the White House.

But, as to the design, I do not love the spaghettification of the line charts. Though I do appreciate that the Hong Kong identity has been separated by the maroon-coloured line. I wonder if labelling the lines in the small multiples is necessary given the decision to include the legend at the top of the chart.

The other tricky thing with this type of chart is that the data series is a population cohort. And yet the data is based on a time series. And so the cohorts vary over time. It might not be entirely clear to the audience that this (appears to be)/is a sample of people of an age at a particular date. How do those people change over the years? It’s hard to see that trend by separating out the data.

Overall, it’s a solid piece. And it’s important given the gravity of the protests in Hong Kong.

Credit for the piece goes to the Economist Data team.

Pub Trivia Scores

So today we have pub trivia scores.

It’s been a little while since I’ve posted from my data recording of my Wednesday night’s team trivia pub scores. For the very few of us who know what this means, here you go.

We're on a downward trend
We’re on a downward trend

Essentially, our ability to score points on music in the last round remains pretty bad. Hence the general downward trend.

Credit for this piece goes to me.

A Very Loud Tube

As all my readers probably know, I love London. And in loving London, I love the Tube and the Oyster Card and all that goes along with Transport for London. But, I have noticed that sometimes when I take the Underground, there are segments where it gets a bit loud, especially with the windows open. The Economist covered this in a recent article where they looked at some data from a London-based design firm that makes noise protective gear. (For purposes of bias, that seems important to mention here.)

The data looks at decibels in a few Underground lines and when the levels reach potentially harmful levels. I took a screenshot of the Bakerloo line, with which I am familiar. (At least from Paddington to Lambeth.) Not surprisingly, there are a few segments that are quite loud.

I definitely recall it being loud
I definitely recall it being loud

I like this graphic—but like I said about bias, I’m biased. The graphic does a good job of using the above the 85-decibel line area fill to show the regions where it gets loud. And in general it works. However, if you look at the beginning of the Bakerloo line noise levels the jumps up in down in noise levels, because they happen so quickly in succession, begin to appear as a solid fill. It masks the importance of those periods where the noise levels are, in fact, potentially dangerous.

I have had to deal with this problem often in my work at the Fed, where some data over decades is available on a weekly basis. One trick that works, besides averaging the data, is thinning out the stroke of the line so the overlaps do not appear so thick. It could make it difficult to read, but it avoids the density issues at the beginning of that chart.

All in all, though, I would love a London-like transport system here in Philly. I’d rather some loud noises than polluting cars on the road.

Credit for the piece goes to the Economist Data Team.

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 Warm Will It Get?

In Philadelphia, this summer has been warmer than average. But with most recent years being warmer than average, that might not mean much. However, a valid question is that with climate change, how much warmer will the city get on average? The BBC recently published an article that explored the temperature changes in cities around the world according to several different models for best to worst case scenarios.

The raw data so to speak
The raw data so to speak

It does a nice job via scrolling of showing how the averages work as a rolling average and the increase over time. It runs through each scenario, from best case to worst case, as a dotted line and then plots each in comparison to each other to show the range of possible outcomes.

Ew. Just ew.
Ew. Just ew.

I know that dark or black background is in style for big pieces. But I still do not love them. Thankfully the choice of these two colours work here. The dotted lines also work for showing the projections. And in the intermediate steps, not screencaptured, the previous projections go dark and only the current one is highlighted.

Thankfully the text boxes to the right capture the critical numbers: the actual projection numbers for the monthly average. And they tie them to the lines via the colours used.

Not shown here are a few other elements of the piece. The top of the article starts with a spinning globe that shows how the average temperature across the globe has already changed. Spoiler: not well. While the spinning globe adds some interactivity to the article, it by definition cannot display the entire world all at once, like flat, two-dimensional projections do. This makes it difficult to see impacts across the globe simultaneously. A more standard projection map could have worked really well.

Lastly, the article closes with a few stories about specific locations and how these temperature increases will impact them. These use more illustrations and text. The exception, however, is a graphic of the Arctic that shows how summer sea ice coverage has collapsed over the last few decades.

Overall this is a strong piece that shows some global impacts while allowing the user to dive down into the more granular data and see the impact on some of the world’s largest cities.

Credit for the piece goes to BBC Visual and Data Journalism team.

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.

United in Gun Control

Today’s piece is nothing more than a line chart. But in the aftermath of this past weekend’s gun violence—and the inability of this country to enact gun control legislation to try and reduce instances like them—the Economist published a piece looking at public polling on gun control legislation. Perhaps surprisingly, the data shows people are broadly in favour of more restrictive gun laws, including the outlawing of military-style, semi-automatic weapons.

These trendlines are heading in the right direction
These trendlines are heading in the right direction

In this graphic, we have a line chart. However the import parts to note are the dots, which is when the survey was conducted. The lines, in this sense, can be seen as a bit misleading. For example, consider that from late 2013 through late 2015 the AP–NORC Centre conducted no surveys. It is entirely possible that support for stricter laws fell, or spiked, but then fell back to the near 60% register it held in 2015.

On the other hand, given the gaps in the dataset, lines would be useful to guide the reader across the graphic. So I can see the need for some visual aid.

Regardless, support for stricter gun laws is higher than your author believed it to be.

Credit for the piece goes to the Economist 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.

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.