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.

We’re All Just Palm Trees and Patio Furniture in the Wind

For all my American readers, I hope you all enjoyed their Labour Day holiday. For the rest of you, today is just a Tuesday. Unless you live in the Bahamas, then today is just another nightmarish day as Hurricane Dorian continues his assault on the islands.

The storm will be one for the record books when all is said and done, and not just because of the damage likely to be catastrophic when people can finally emerge and examine what remains. The storm, by several metrics, is one of the most powerful in the Atlantic since we started recording data on hurricanes. If we look at pressure and sustained wind speeds, i.e. not wind gusts, Sam Lillo has plotted the path of Dorian through those metrics and found it sitting scarily in the lower-right corner of this plot.

How low can it go? Probably not much, thankfully.
How low can it go? Probably not much, thankfully.

The graphic does a couple of nice things here. I like the use of colour to indicate the total number of observations in that area. Clearly, we see a lot more of the weaker, higher pressure storms. Hence the dark blue in the upper-left. But then against that we have the star of the graphic, and my favourite part of the plot: the plot over time of Dorian’s progress and intensification as a storm. The final green dot indicates the point of the last observation when the graphic was made.

Overall this is a simple and solid piece that shows in the available historical context just how powerful Dorian is. Unfortunately that correlates with likely heavy damage to the Bahamas.

Credit for the piece I presume goes to Sam Lillo, though with the Twitter one can never be entirely certain.

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.

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.

Missing Planets

In science news, we turn to graphics about planets and things. Specifically we are talking about exoplanets, i.e. planets that exist outside our solar system. Keep in mind that we have only been able to detect exoplanets since the 1990s. Prior to then, how rare was our system with all our planets? It could have been very rare. Now we know, probably not so much.

But, in all of that discovery, we are missing entire types of planets. This article published by Forbes does a nice job explaining why. But one of the key types of planets that we have been unable to discover heretofore have been: intermediately distant, giant planets. Think the Jupiters and Saturns of our system. Prior to now we could detect massive Jupiter-like planets orbiting super near to their distant stars. Or, we could detect super massive planets orbiting very far away. The in-betweeners? Not so much.

There's still a pretty wide gap out there…
There’s still a pretty wide gap out there…

The above screenshot does a good job of showing where new detection methods have allowed scientists to begin to fill in the gaps. It shows how there is an enormous gap between what we have discovered and how they have been discovered. And the article does a nice job explaining how the science works in that only now with our longer periods of observation will help resolve certain issues.

From a design standpoint, this isn’t a super complicated graphic. It does rely upon a logarithmic scale, which isn’t common in non-scientific or academic papers. But this graphic comes from that environment, so it makes a lot of sense. The article is full of graphics from third-party sources, but I found this the most informative because of that very gap it highlights and how the new work (the stars) begin to fill it in.

Credit for the screenshotted piece goes to E. L. Rickman et al.

Trump-won Counties Are Winning

Yesterday we looked at how China and the European Union are planning their tariff/trade war retaliation to target Trump voters. Today let’s take a look at how those voters are doing as this article from Bloom does.

Lots of green, but some noticeably red counties in Florida.
Lots of green, but some noticeably red counties in Florida.

The article is not terribly complicated. We have four choropleth maps at the county level. Two of the maps isolate Trump-won counties and the other two are Clinton-won. For each candidate we have a GDP growth and an employment growth map.

In the Trump-won maps, the Clinton-won counties are white, and vice versa. Naturally, because the Democratic vote is greatest in the large cities, which, especially on the East Coast, are in tiny counties, you see a lot less colour in the Clinton maps.

Not a whole lot to see here…
Not a whole lot to see here…

Design wise, I should point out the obvious that green-to-red maps are not usually ideal. But the designers did a nice job of tweaking these specific colours so that when tested, these burnt oranges and green-blues do provide contrast.

Here they appear more of a yellow to grey
Here they appear more of a yellow to grey

But I am really curious to see this data plotted out in a scatter plot. Of course the big counties in the desert southwest are noticeable. But what about Philadelphia County? Cook County? Kings County? A scatter plot would make them equally tiny dots. Well, hopefully not tiny. But then when you compare GDP growth and employment growth and benchmark them against the US average, we might see some interesting patterns emerge that are otherwise masked behind the hugeness of western counties.

But lastly. And always. Where. Are .Alaska. And. Hawaii? (Of course the hugeness problem is of a different scale in Hawaii. Their county equivalents are larger than states combined.)

Credit for the piece goes to the Bloomberg graphics department.

Trade War Retaliation

About a week and a half ago the Economist published an article about the retaliatory actions of the European Union and China against the tariffs imposed by the Trump administration. Of course last week we had a theme of sorts with lineages and ancestry. So this week, back to the fun stuff.

What makes today’s piece particularly relevant is that over the weekend, Trump announced he might increase the tariffs proposed, but not yet implemented, upon Chinese goods. So some economists looked at the retaliatory tariffs proposed by the EU and China.

Ultimately Trump's tariffs are not paid by foreign governments, but by US citizens.
Ultimately Trump’s tariffs are not paid by foreign governments, but by US citizens.

Each targets Trump voters, albeit of different types. But China appears more willing to engage in a brutal fight. Its tariff proposal would not just harm Trump voters, but would also harm Chinese citizens. The EU’s plan appears tailored to maximise the pain on Trump voters, but minimise that felt by its own citizens.

A few minor points. I like how the designers chose to highlight high impact categories with colour. Lower impact shares are two shades of light grey. But after that, the scale changes. I wonder how the maps would compare if each had been set to the same scale. It looks doable as the bottom range of the maximum bin is 6% for the EU and 8% for China. (Their high limit is much higher at 22% compared to the EU’s 10%.)

That said, it does a good job of showing the different geographic footprints of the two retaliatory tariff packages. Tomorrow—barring breaking news—we will look at why that is important.

Credit for the piece goes to the Economist Data Team.

Why the Faces?

Stepping away from both the Brexit drama and the aircraft drama of the week, let’s look at US political drama. Specifically, the Democratic field and some of the early support for candidates and assumed-to-be candidates.

This piece comes from an article about the bases of various candidates. From a data visualisation perspective it uses a scatter plot to compare the net favourability of the candidate to the share of people who have an opinion about said candidate.

A veritable who's who of the Democratic field
A veritable who’s who of the Democratic field

But what if you don’t know who the candidate is? As in, you don’t know what they look like. Well, then it might be difficult to find Bernie or Elizabeth Warren. This kind of graphic relies on facial recognition. I’m not certain that’s the best, especially when one is talking about a field in which people may not know or have an opinion on the candidates in question.

Another drawback is that the sizes of the faces are large. And, especially in the lower left corner, this makes it easier to obscure candidates. Where exactly is Sherrod Brown? Between a unidentified face and that of Terry McAuliffe.

I think a more simplistic dot/circle approach would have worked far better in this instance.

Credit for the piece goes to the FiveThirtyEight graphics department.

Individualistic Immigrants

As many of you know, genealogy and family history is a topic that interests me greatly. This past weekend I spent quite a bit of time trying to sort through a puzzle—though I am not yet finished. It centred on identifying the correct lineages of a family living in a remote part of western Pennsylvania. The problem is the surname was prevalent if not common—something to be expected if just one family unit has 13 kids—and that the first names given to the children were often the same across family units. Combine that with some less than extensive records, at least those available online, and you are left with a mess. The biggest hiccup was the commonality of the names, however. It’s easier to track a Quinton Smith than a John Smith.

Taking a break from that for a bit yesterday, I was reminded of this piece from the Economist about two weeks ago. It looked at the individualism of the United States and how that might track with names. The article is a fascinating read on how the commonness or lack thereof for Danish names can be used as a proxy to measure the individualism of migrants to the United States in the 19th century. It then compares that to those who remained behind and the commonness of their names.

But where are the Brendans?
But where are the Brendans?

The scatter plot above is what the piece uses to introduce the reader to the narrative. And it is what it is, a solid scatter plot with a line of best fit for a select group of rich countries. But further on in the piece, the designers opted for some interesting dot plots and bar charts to showcase the dataset.

Now I do have some issues with the methodology. Would this hold up for Irish, English, German, or Italian immigrants in the 19th century? What about non-European immigrants? Nonetheless it is a fascinating idea.

Credit for the piece goes to the Economist Data Team.

Are Baseball’s Big Contracts Worth It?

On Tuesday the San Diego Padres signed Manny Machado to a guaranteed contract worth $300 million over the next ten years—though he can opt out after five years. Machado was one of two big free agents on the market, the other being Bryce Harper. One question out there is whether or not these big contracts will be worth it for the signing teams. This piece yesterday from the New York Times tries to look at those contracts and how the players performed during them.

Oh, David Price…
Oh, David Price…

Like the piece we looked at Tuesday, this takes a narrative approach instead of a data exploratory approach—the screenshot above is halfway through the read. Unlike the Post piece, this one does not allow users to explore the data. Unlabelled dots do not reveal the player and there is no way to know who they are.

Overall it is a very strong piece that shows how large and long contracts are risky for baseball teams. The next big question is where, for how long, and how much will Bryce Harper sign?

Credit for the piece goes to Joe Ward and Jeremy Bowers.