Last week I met a friend for drinks and part of our conversation was about how on a trip to east Asia, he flew from New York and then over the North Pole. The North Pole! I then explained it was cool, but not unique. Instead aircraft typically fly between destinations via great circles. Basically, the shortest distance between two points on the Earth is a straight line, but remember the Earth is not exactly flat. Its spherical nature means that the shortest distance sometimes is what you would see as a curve on a flat map. And sometimes, those curves are shortest when plotted over the North Pole, because unlike a flat map, the east and west ends really do connect.
Lo and behold, yesterday the Economist published a piece about a new non-stop flight between London and Perth, on Australia’s southwest coast. The graphic shows the ten longest commercial flight paths. And what do you know, one of the longest is a soon-to-be flight from New York to Singapore that flies near the North Pole.
Of course the key to this type of diagram is the type of projection. Instead of using the Mercator-like map made popular by direction-focused maps like those of Google, here we see an orthographic presentation. It presents the Earth as if we were to see it from space, allowing us to see the fullness of the flight paths. Tellingly, those that appear to cross the middle of the map are shown as straight lines (Atlanta to Johannesburg), but those nearer the edges show the curvature of the great circles (Houston to Sydney).
Credit for the piece goes to the Economist graphics department.
Earlier this March the Washington Post published a piece looking at the twenty finalist contenders for the second Amazon headquarters. Specifically it explored how the cities rank in metrics that speak to a city’s technology and innovation economy.
That in and of itself, while incredibly fascinating, is not noteworthy in and of itself. Though I will say the article’s online title is neatly presented, split half-and-half with the vertical graphic showing the cities ranked.
But the point that was really neat was the interactivity that followed. Here you can see a dropdown from which the user selects a city of interest—surprise, surprise we are looking at Philadelphia. From that point on, the piece keeps the selected city highlighted in every graphic that follows.
Again, that is nothing truly surprising, but it is neat to see. What would have taken it to the next step is if each of those associated paragraphs were tailored to the specific city. Instead, they appear to be general paragraphs.
But overall, it does a really nice job of comparing the twenty cities—it’s actually fewer because both Washington and New York have multiple sites per metro area—across the different metrics.
The only part that left me scratching my head a bit was the colour choice. I am not certain that it needs the blue-green to yellow-green palette. Those colours seem defined by a city’s placement on the overall list and I am not convinced that the piece would not have still worked if they had been only a single colour, using another colour to define the selected city.
Credit for the piece goes to Darla Cameron and Jonathan O’Connell.
Philadelphia is expecting a little bit of snow today, 20 March. We should not be seeing too much accumulate if anything, but still, flakes will likely be in the air this evening. That made me think of this piece from just last week where the New York Times looked at the change in winter temperatures across the United States for the last almost 120 years.
Of course, I would be remiss if I failed to mention that climate change does not mean that temperatures always rise. Instead, while the general average trends upward, the curve flattens out meaning more extreme events on both the hot and the cold parts of the spectrum. (Actually, the New York Times covered this very subject well back in August.)
Anyway, the map from the Times shows how the biggest changes have been recorded in the north of the Plains states. But the same general shift is subject to local conditions, most notably in the southeast where temperatures are actually a lit bit lower.
Credit for the piece goes to Nadja Popovich and Blacki Migliozzi.
For many years I would often tell people that sometimes a visualisation can be “boring”, because the data itself is boring—a lack of growth in a market, no real mergers, or even steady and consistent but unspectacular growth. Those can all be stories, even if they likely result in very monotone choropleths or straight line charts or perfect steps of bar charts.
And then there are times when the lack of growth or change, when visualised, can be very powerful. I wanted to share this piece from the New York Times with everyone because it does just that.
You really need to click through and see the scale and scope, because the designers behind this did a fantastic job of capturing that sense of lack of change in a very large and expansive piece.
Credit for the piece goes to the New York Times Editorial Board.
Last week I covered the Pennsylvania congressional district map changes quite a bit. Consequently I was not able to share a few good pieces of work. Let’s hope nothing goes terribly wrong this week and maybe we can catch up.
From last Friday we have this nice piece from FiveThirtyEight looking at the spread of influenza this season.
The duller blues and greens give way to a bright red from south to north. Very quickly you can see how from, basically, Christmas on, the flu has been storming across the United States. It looks as if your best bets are to head to either Maine or Montana. Maybe DC, it’s too small to tell, but I kind of doubt that.
As you all know, I am a fan of small multiples and so I love this kind of work. To play Devil’s advocate, however, I wonder if an interactive piece that featured one large map could have worked better? Could the ability to select the week and then the state yield information on how the flu has spread across each state? I am always curious what other other forms and options were under consideration before they chose this path.
Credit for the piece goes to the FiveThirtyEight graphics department.
Baseball season begins next week. For different teams it starts different days, but for the Red Sox at least, pitchers and catchers report to Spring Training on Tuesday. But the Red Sox, along with many other teams throughout baseball, have holes in their roster. Why? Arguably because nearly 100 free agents remain unsigned.
I do not intend to go into the different theories as to why, but this has been a remarkably slow offseason. How do we know? Well using MLB Trade Rumours listing of the top-50 free agents this year, and the signings reported on Baseball Reference, we can look at the upper and middle, or maybe upper-middle, tiers of free agency.
Kind of messy to look at with all the player labels, but we can see here the projected contracts, in both length and total value, along with the contracts players signed, if they have. And for context we can see how those contracts compares to the Qualifying Offer (QO). What’s that? Complicated baseball stuff that is meant to ensure teams that lose stars or highly valuable players are compensated, especially since they might come from smaller market teams that cannot afford superstar prices. The QO is meant to help competitiveness in the sport. How does it do that? Let’s just say complicated baseball stuff. We should also point out that some players, most notably the Yankees’ Masahiro Tanaka, were expected to opt out of their contracts and try the free market. Tanaka did not, which is why his projection was so far off.
So is it true that free agency is or has moved slowly? Consider that approximately 100 free agents remain unsigned as of late Thursday night—please no big signings tomorrow morning—and that of the top 50, 22 of them remain unsigned. And if we take the QO as a proxy for the best players in the game, add in two players who were exempt because baseball stuff, we can say that 8 of the 11 best players remain unsigned. Though, in fairness to ownership, three of those players are reportedly sitting on multi-year offers in the nine-figure range.
But if players are unsigned, does that mean they are competing for lower value contracts? Possibly. If we use MLB Trade Rumours’ projected contracts, because in years past they have proven smart at these things, we can see that for the 28 who have signed, it’s a roughly even split in terms of the number of players who have signed for more or less than their projection. Sometimes however, non-monetary factors come into play. Two notable free agents, Todd Frazier and Addison Reed, both reportedly signed lesser value contracts to play closer to a specified geography, in Frazier’s case the Northeast and in Reed’s the Midwest.
But the telling part in that graphic is not necessarily the vertical movement, i.e. dollars, but the horizontal movement. (Though we should call out the cases of Carlos Santana and Tyler Chatwood, signed by the Phillies and Cubs respectively, who did far better than projected.) Consider that a team might not have a lot of money to spend and so might extend a contract over additional years, offering job security to a player. Or in a bidding war, the length of the contract might be what leads a player to pick one team over another. In those cases we would expect to see more left-to-right movement. So far we have only had one player, Lorenzo Cain, who signed for more years than expected. Most players who have signed for less have also signed for fewer years. Note the cluster of right-to-left, or shorter-than-expected, contracts in the lower tiers versus the small, vertical-only cluster in the same section for those signing greater than projected contracts.
Lastly, are these trends hitting any specific positional type of player? Well maybe. Ignoring the market for catchers because of how small the pool was—though the case of Jonathan Lucroy as the unsigned catcher is fascinating—we can see that the market has really been there for relief pitchers as there are few of the top-50 remaining on the market. Starting pitchers and outfields, while with quite a few still on the market, have generally done better than projected. But infielders lag behind with numerous players unsigned and those that have signed, most have signed for less than projected.
But at the same time, I would fully expect that once these higher level free agents come off the board—while one would think they would certainly be signed, who knows in such a weird offseason as this—the unsigned middle and lower tiers will quickly follow suit.
Of course none of this touches upon age. (Largely because lack of time on my part.) Though, in most cases, getting to free agency in and of itself makes a player older by definition the way baseball’s pre-arbitration and arbitration salary periods work. (Again, more baseball stuff but suffice it to say your first several years you play for peanuts and crackerjacks.)
Hopefully by this afternoon—Friday that is—some of these players will have signed. After all, baseball starts next week. If we are lucky this post will be outdated, at least in terms of the dataset, come Monday. Regardless, it has been a fascinating albeit boring baseball offseason.
Credit for the data goes to MLB Trade Rumours and Baseball Reference.
A few days ago I posted about the front cover graphic for the New York Times that used a choropleth to explore 2017 economic growth. Well, this morning whilst looking for something else, I came across the online version of the story. And I thought it would be neat to compare the two.
Again, nothing too crazy going on here. But the most immediately obvious change is the colour palette. Instead of using that green set, here we get a deep, rich blue that fades to light very nicely. More importantly, that light tan or beige colour contrasts far better against the blue than the green in the print version.
The other big change is to the small multiple set at the bottom. Here they have the space to run all twelve datasets horizontally. In the earlier piece, they were stacked six by two. It worked really well, but this works better. Here it is far easier to compare the height of each bar to the height of bars for other countries.
Here in Pennsylvania this week, the state Supreme Court will hear arguments on the legality of congressional districts drawn by Republicans in 2010. The state is rather evenly split between Republicans and Democrats, e.g. Donald Trump won by less than one percentage point or less than 45,000 votes. But 13 of its 18 congressional districts are represented by Republicans, roughly 72%.
This graphic is from the New York Times Upshot and it opens a piece that explores gerrymandering in Pennsylvania. The graphic presents the map today as well as a nonpartisan map and an “extreme” gerrymander. The thing most noticeable to me was that even with the nonpartisan geography, the Democrats are still below what they might expect for a near 50-50 split. Why? One need only look at Philadelphia and Pittsburgh where, using the Times’ language, the Democrats “waste” votes with enormous margins, leaving the suburban and rural parts of the state open for Republican gains.
Credit for the piece goes to Quoctrung Bui and Nate Cohn.
In the United Kingdom, the month of January has been less than stellar for the National Health Service, the NHS, as surgeries have been cancelled or delayed, patients left waiting in corridors, and a shortage of staff to cope with higher-than-usual demand.
But another problem is the shortage of hospital beds, which compounds problems elsewhere in hospitals and health services. The Guardian did a nice job last week of capturing the state of bed capacity in some hospitals. Overall, the piece uses line charts and scatter plots to tell the story, but this screenshot in particular is a lovely small multiples set that shows how even with surge capacity, the beds in orange, many hospitals are running at near 100% capacity.
Whilst away, I came upon this piece in the following of my offseason baseball news. The New York Times published it between Christmas and New Years and the piece looks at the origins of sports persons in European football leagues compared to several American sports leagues, including American football, baseball, and basketball.
The piece features an opening set of small multiples comparing all the leagues. Maddeningly, I wanted details and mouseovers and annotations at the start. Fortunately, as the reader continues through the article, each small multiple becomes big and the reader can explore the details of the league.
Credit for the piece goes to Gregor Aisch, Kevin Quealy, and Rory Smith.