So two weeks ago I posted about the graphics in a BBC article about how London has surpassed New York in terms of murders, due to a spate of stabbings in the British capital. Well, somehow I missed this: an article from the Economist that rebuts that point. And it does it brilliantly.
Lies, damned lies, and statistics.
I think everybody who works with data knows that adage. Now, I am not using it to say that the BBC—or the numerous other media outlets that ran the story—lied. Just that it is easy to change the story based on the data, how it is presented, or which subsets of the data are selected.
The Economist’s article points out that the surpassing of New York is a short term data point, a worrying short term trend, definitely, but they then look at the data. They select two timeframes and look at them side-by-side.
And that is what I love about this piece. It shows the long-term context of New York having a far-higher medium-term history of murder (some 28 years of data is shown). When I was growing up in the 90s, murders in New York—and to be fair almost all large American cities—was just something that was a known fact. During that time, London hovered below 200 or so, compared to the 2000+ in early 90s New York.
But then they also show the short term, which does point to a steady rise in London murders. But, the data could also show a one-time dip in the murders in New York. But they also show that the total number of deaths is still higher in New York than London, despite the three months of data.
Murder is not good. But these graphics are a good example of how selecting different time series for the same data set, and then showing which parts of the data to show. The earlier BBC piece, and my revision of it, did not show the total deaths. Nor did either piece show the longer timeline of data.
Credit for the piece goes to the Economist graphics department.
Yesterday we looked at the shrinking Denver Post. Today we have a graphic from a related story via Politico. The article explores the idea that President Trump performs better in what the article terms “news deserts”, those counties with a very low level of newspaper circulation. (The article explains the methodology in detail.) This piece we are looking at here shows how those counties performed against the circulation rate and their 2016 presidential election result.
Overall, the work is solid. But I probably would have done a few things differently. First, the orange overlay falls in the middle of one column of dots. Do those dots then fall inside or outside the categorisation of news desert?
Secondly, the dots. If this were perhaps a scatter plot comparing the variables of circulation rates and, perhaps, election vote results as a percent, dots would be perfect. Here, however, they create this slightly distracting pattern in the the main area of counties. When the dots are stacked neatly and apart from other columns, as they are more often on the right, the dots are fine. But in the packed space on the left, not as much.
As I was reading through the article I had a couple of questions. For example, couldn’t the lack of newspapers be reflective of the urban–rural split or the education split, both of which can be seen in the same election results. Thankfully the article does spend time going through those points as well. It is a bit lengthy of a read—with a few other perfectly fine graphics—but well worth it.
I know I’ve looked at the Times a few times this week, but before we get too far into the next week, I did want to show what they printed on Saturday.
It is not too often we get treated to data on the front page or even the section pages. But last Saturday we got just that in the Business Section. Two very large and prominent charts looked at federal government borrowing and the federal deficit. Both are set to grow in the future, largely due to the recently enacted tax cuts.
The great thing about the graphic is just how in-the-face it puts the data. Do two charts with 14 data points (28 total) need to occupy half the page? No. But there is something about the brashness of the piece that I just love.
And then it continues and the rest of the article points, at more normal sizes, to treasury bill yields and car loan rates. The inside is what you would expect and does it well in single colour.
I don’t know if you heard, but the Winter Olympics just concluded. I’m admittedly not a huge fan of the Winter Olympics, but that doesn’t mean I didn’t keep my eye on some of the stories coming out of the coverage. One that I liked was this piece from FiveThirtyEight.
It was about halfway through the Olympics and the US was not doing terribly well. The chart does a great job of showing how various countries were performing, or over- or under-performing, their expected total medal winnings. It did this through a filled bar chart with a bar-specific benchmark line. It was a nice combination of a couple of different techniques to incorporate not just the usual above or below the trend, but also the actual amounts.
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.
Yesterday SpaceX launched the Falcon Heavy rocket on its maiden voyage, and then recaptured several, though not all, of its reusable rockets. The Falcon Heavy represents the most powerful rocket available to mankind today, though NASA’s Saturn V of the Apollo programme era was considerably more powerful. That was all the stuff you could read in the news yesterday and today.
But how much more powerful? Thankfully we have the Economist who put together a nice graphic detailing not just the standard size comparisons of the Falcon series to the Saturn V and other famous rocket systems, e.g. the Space Shuttle and its boosters. The Economist graphic also adds information about the payload capabilities and timeframes for either historical operation or expected service dates.
From the illustrative side, there were three really nice touches. First, the faint Statue of Liberty to give the rocket height context to famous landmark buildings. Two, the little human figure on the left-hand side to give context to ourselves, these things are big. Three, the ridiculousness of the Saturn V is captured by having its peak break the top frame of the chrome or graphic device, i.e. the red bar, standard on Economist graphics.
Overall a solid piece. (Yes, I know these are liquid fueled.)
Credit for the piece goes to the Economist’s graphics team.
Earlier this month I wrote-up a piece from the Economist that looked at 2018 GDP growth globally. I admitted then—and still do now—that it was an oddly sentimental piece given the frequency with which I made graphics just like that in my designer days of youth and yore. Today, we have the redux, a piece from the New York Times. Again, nothing fancy here. As you will see, we are talking about a choropleth map and bar charts in small multiple format. But why am I highlighting it? Front page news.
I just like seeing this kind of simple, but effective data visualisation work on the front page of a leading newspaper.
I personally would have used a slightly different palette to give a bit more hint to the few negative growth countries in the world—here’s lookin’ at you, Venezuela—but overall it works. And the break points in the bin seem a bit arbitrary unless they were chosen to specifically highlight the called-out countries.
Then on the inside we get another small but effective graphic.
It doesn’t consume the whole page, but sits quietly but importantly at the top of the article.
There the small multiples show the year-on-year change—nothing fancy—for the world’s leading economies. A one-colour print, it works well. But, I particularly enjoy the bit with China. Look at how the extreme growth before the Great Recession is handled, just breaking out of the container. Because it isn’t important to read growth as 13.27% (or whatever it was), just that it was extremely high. You could almost say, off the charts.
Overall, it was just a fun read for a Sunday morning.
Credit for the piece goes to Karl Russell and the New York Times graphics department.
Today’s post is a very quick reaction to the news last night about President Trump calling Haiti, El Salvador, and African countries “shitholes” and trying to get rid of immigrants from those countries in favour of immigrants from places like Norway.
Norwegian contributions to American immigrants peaked well before the 21st century. At that time, Norway was poor and lesser developed. The data was hard to find, but on a GDP per capita level, Norway was one of the least developed countries in Western Europe. On a like dollar-for-dollar basis, El Salvador of 2008 is not too far from Norway 1850.
I wish I had more time to develop this graphic for this morning. Alas, it will have to do as is.
The Winter Olympics are creeping ever closer and so this piece from the New York Times caught my eye. It examines the impact of climate change on host cities for the Winter Olympics. Startlingly, a handful of cities from the past almost century are no longer reliable enough, i.e. cold and snow-covered, to host winter games.
This screenshot is of a bar chart that looks at temperatures, because snow and ice obviously require freezing temperatures. The reliability is colour-coded and at first I was not a fan—it seemed unnecessary to me.
But then further down the piece, those same colours are used to reference reliability on a polar projection map.
That was a subtle, but well appreciated design choice. My initial aversion to the graphic and piece was changed by the end of it. That is always great when designers can pull that off.
Credit for the piece goes to Kendra Pierre-Louis and Nadja Popovich
January is the month of forecasts and projections for the year to come. And the Economist is no different. Late last week it published a datagraphic showcasing the GDP growth forecasts of the Economist Intelligence Unit. I used to make this exact type of datagraphic a lot. And I mean a lot. But what I really enjoy is how successfully this piece integrates the map, the bar chart, and the tables to round out the story.
The easy thing to do is always the map, because people like maps. They can be big, and if the data set is robust, full of data and colour. But maps hide and obscure geographically small countries. And then you have to assume that people know all the countries in the world. Problem is, most people do not.
So the bar chart does a good job of showing each country as equals, a slim vertical bar. In such a small space, labelling every country is impossible, but the designers chose a select number of countries that might be of interest and called them out across the entire series.
Lastly, people always like to know who is #winning and who is a #loser. So the tables at the extreme ends of the chart showcast the top and last five.
I may have rearranged some of the elements, and dropped the heavy black rules between the bins on the legend, but overall I consider this piece a success.
Credit for the piece goes to the Economist Data Team.