Out with the New, In with the Old

After twenty years out of power, the Taliban in Afghanistan are back in power as the Afghan government collapsed spectacularly this past weekend. In most provinces and districts, government forces surrendered without firing a shot. And if you’re going to beat an army in the field, you generally need to, you know, fight if you expect to beat them.

I held off on posting anything about the Taliban takeover of Afghanistan simply because it happened so quick. It was not even two months ago when they began their offensive. But whenever I started to prepare a post, things would be drastically different by the next morning.

And so this timeline graphic from the BBC does a good job of capturing the rapid collapse of the Afghan state. It starts in early July with a mixture of blue, orange, and red—we’ll come back to the colours a bit later. Blue represents the Afghan government, red the Taliban, and orange contested areas.

The start of the summer offensive

The graphic includes some controls at the bottom, a play/pause and forward/backward skip buttons. The geographic units are districts, sub-provincial level units that I would imagine are roughly analogous to US counties, but that’s supposition on my part. Additionally the map includes little markers for some of the country’s key cities. Finally in the lower right we have a little scorecard of sorts, showing how many of the nearly 400 districts were in the control of which group.

Skip forward five weeks and the situation could not be more different.

So much for 20 years.

Almost all of Afghanistan is under the control of the Taliban. There’s not a whole lot else to say about that fact. The army largely surrendered without firing a shot. Though some special forces and commando units held out under siege, notably in Kandahar where a commando unit held the airport until after the government fell only to be evacuated to the still-US-held Hamid Karzai International Airport in Kabul.

My personal thoughts, well you can blame Biden and the US for a rushed US exodus that looks bad optically, but the American withdrawal plan, initiated by Trump let’s not forget, counted on the Afghan army actually fighting the Taliban and the government negotiating some kind of settlement with the Taliban. Neither happened. And so the end came far quicker than anyone thought possible.

But we’re here to talk graphics.

In general I like this. I prefer this district-level map to some of the similar province-level maps I have seen, because this gives a more granular view of the situation on the ground. Ideally I would have included a thicker line weight to denote the provinces, but again if it’s one or the other I’d opt for district-level data.

That said, I’d probably have used white lines instead of black. If you look in the east, especially south and east of Kabul, the geographically small areas begin to clump up into a mass of shapes made dark by the black outlines. That black is, of course, darker than the reds, blues, and yellows. If the designers had opted for white or even a light shade of grey, we would enhance the user’s ability to see the district-level data by dropping the borders to the back of the visual hierarchy.

Finally with colours, I’m not sure I understand the rationale behind the red, blue, yellow here. Let’s compare the BBC’s colour choice to that of the Economist. (Initially I was going to focus on the Economist’s graphics, but last minute change of plans.)

Another day, more losses for the government

Here we see a similar scheme: red for the Taliban, blue for the government. But notably the designers coloured the contested areas grey, not yellow. We also have more desaturated colours here, not the bright and vibrant reds, blues, and yellows of the BBC maps above.

First the grey vs. yellow. It depends on what the designers wanted to show. The grey moves the contested districts into the background, focusing the reader’s attention on the (dwindling) number of districts under government control. If the goal is to show where the fighting is occurring, i.e. the contest, the yellow works well as it draws the reader’s attention. But if the goal is to show which parts of the country the Taliban control and which parts the government, the grey works better. It’s a subtle difference, I know, but that’s why it would be important to know the designer’s goal.

I’ll also add that the Economist map here shows the provincial capitals and uses a darker, more saturated red dot to indicate if they’d fallen to the Taliban. Contrast that with the BBC’s simple black dots. We had a subtler story than “Taliban overruns country” in Afghanistan where the Taliban largely did hold the rural, lower populated districts outside the major cities, but that the cities like the aforementioned Kandahar, Herat, Mazar-i-Sharif held out a little bit longer, usually behind commando units or local militia. Personally I would have added a darker, more saturated blue dot for cities like Kabul, which at the time of the Economist’s map, was not under threat.

Then we have the saturation element of the red and blue.

Should the reds be brighter, vibrant and attention grabbing or ought they be lighter and restrained, more muted? It’s actually a fairly complex answer and the answer is ultimately “it depends”. I know that’s the cheap way out, but let me explain in the context of these maps.

Choropleth maps like this, i.e. maps where a geographical unit is coloured based on some kind of data point, in this instance political/military control, are, broadly speaking, comprised of large shapes or blocks of colour. In other words, they are not dot plots or line charts where we have small or thin instances of colour.

Now, I’m certain that in the past you’ve seen a wall or a painting or an advert for something where the artist or designer used a large, vast area of a bright colour, so bright that it hurt your eyes to look at the area. I mean imagine if the walls in your room were painted that bright yellow colour of warning signs or taxis.

That same concept also applies to maps, data visualisation, and design. We use bright colours to draw attention, but ideally do so sparingly. Larger areas or fields of colours often warrant more muted colours, leaving any bright uses to highlight particular areas of attention or concern.

Imagine that the designers wanted to highlight a particular district in the maps above. The Economist’s map is better designed to handle that need, a district could have its red turned to 11, so to speak, to visually separate it from the other red districts. But with the BBC map, that option is largely off the table because the colours are already at 11.

Why do we have bright colours? Well over the years I’ve heard a number of reasons. Clients ask for graphics to be “exciting”, “flashy”, “make it sizzle” because colours like the Economist’s are “boring”, “not sexy”.

The point of good data visualisation, however, is not to make things sexy, exciting, or flashy. Rather the goal is clear communication. And a more restrained palette leaves more options for further clarification. The architect Mies van der Rohe famously said “less is more”. Just as there are different styles of architecture we have different styles of design. And personally my style is of the more restrained variety. Using less leaves room for more.

Note how the Economist’s map is able to layer labels and annotations atop the map. The more muted and desaturated reds, blues, and greys also allow for text and other artwork to layer atop the map but, crucially, still be legible. Imagine trying to read the same sorts of labels on the BBC map. It’s difficult to do, and you know that it is because the BBC designers needed to move the city labels off the map itself in order to make them legible.

Both sets of maps are strong in their own right. But the ultimate loser here is going to be the Afghan people. Though it is pretty clear that this was the ultimate result. There just wasn’t enough support in the broader country to prop up a Western style liberal democracy. Or else somebody would have fought for it.

Credit for the BBC piece goes to the BBC graphics department.

Credit for the Economist piece goes to the Economist graphics department.

Cheesesteaks and Politics

For those unaware, Pennsylvania matters in the 2020 election. And it has mattered for years as a perennial swing state. There are of course the visits to steel mill cities like Pittsburgh, deindustrialised places like Johnstown, and unions love visits to places in Lackawanna and Luzerne. (You can read more about Pennsylvania as a swing state in my latest analysis here.)

But I want to focus on visits to Philadelphia. Because they inevitably involve the candidate consuming a cheesesteak. The Economist’s sister magazine, 1843, recently published an article on this very subject. And the whole thing is worth a read.

How have I managed to find this relevant to a blog about data visualisation? Well, they included a recipe to help people understand just what goes into the traditional Philadelphia dish.

Personally, I always have to confess, I’ve never been a huge fan. But, I’ll take provolone over whiz any day.

Credit for the piece goes to Jake Read.

Different Paths to Density

Yesterday we looked at the expansion of city footprints by sprawl, in modern years largely thanks to the automobile. Today, I want to go back to another article I’ve been saving for a wee bit. This one comes from the Economist, though it dates only back to the beginning of October.

This article looks at the different ways a city can achieve density. Usually one things of soaring skyscrapers, but there are other paths. For those interested, the article is a short read and I won’t cover it here. But for the sake of the graphic below, there are three basic paths: coverage, height, and crowding. Or to put in other terms, how much of the city is covered by homes, how tall those homes go, and how many people fit into each home.

Reticulating splines
Reticulating splines

I really like this graphic. It does a great job of using small multiples to compare and contrast three cities that exemplify the different paths. Notably, it keeps each city footprint at the same scale, making it easier to see things such as why Hong Kong builds skyward. Because it has little land. (It is, after all, an island and the tip of a peninsula.)

One area where I wish the graphic had kept to the small multiples is its display of Minneapolis. There, the scale shifts (note the lines for 5 km below vs. Minneapolis’ 10 km). I think I understand why, because the sprawling city would not have fit within the confines of the graphic, but that would have also hammered home the point of sprawl.

I should also point out that the article begins with a graphic I chose not to screenshot, but that I also really enjoy. It uses small multiples to compare cities density over time, running population on the x-axis and people per hectare on the y-. It is not a perfect graphic (it uses I think unnecessary arrowheads at the end of the line), but scatter plots over time are, I think, an underused graphic to show how two variables (ideally related) have moved in tandem over time.

Overall, this is a strong piece from the Economist.

Credit for the piece goes to the Economist 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.

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.

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.

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 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.

Living in the Dark

Earlier this month the Economist published an article that looked at a different way of measuring the economic output of North Korea. The state is so secretive that the publicly available data we all rely on for almost every country is not available. Nor would we necessarily believe their figures. So we have to rely on other measures to estimate the North Korean economy.

The article is about how luminosity, i.e. the lights on seen from space at night, can be used as a proxy for economic activity in the reclusive state.

No lights to guide me home
No lights to guide me home

The article is a fascinating read and uses a scatter plot to show the correlation between luminosity and GDP per capita then how that translates to North Korea, comparing it to older models.

Credit for the piece goes to the Economist graphics department.

Bad Endings

Turns out I was not the only one to look at plotting the ratings of the final series of Game of Thrones. The Economist looked at IMDB ratings, but just prior to the finale on Sunday. They, however, took it a step further and compared Game of Thrones to the final series of other well regarded shows.

All good things…
All good things…

From a design standpoint, I’m not a huge fan of breaking the y-axis at 6. While the data action is all happening at the high range of the scale, that is also the point. Each show is at the top of its class, which makes the precipitous falls of Game of Thrones, Dexter, and House of Cards all the more…wait for it…stark.

I do like the shading behind the line to indicate the final series. That certainly makes it easier to differentiate between the final episodes and those that came before.

But again, I’ll just say, I like how Game of Thrones ended.

Credit for the piece goes to the Economist graphics department.