I was reading the Sunday paper yesterday and whilst I normally skip the sports section, especially during baseball’s offseason, this time a brightly coloured map caught my attention. Of course then I had to read the article, but I am glad that I did.
On Sunday the New York Times ran a print piece—I mean I assume I can find it online (I did.)—about CBS chooses which American football matches to air in the country’s markets. It is a wee bit complicated. And if you can find it, you should read it. The process is fascinating.
But I want to quickly talk about the design of the thing. Remember how I said a map caught my attention. That was pretty important, because the map was not the largest part of the article. Instead that went to a nice big photo. But the information designer I am, well, my eyes went straight to the map below that.
There is nothing too special about the map in particular. It is a choropleth where media markets are coloured by the game being aired yesterday. (The piece explains the blackout rules that changed a few years ago from what I remember growing up.)
But then on the inside, the article takes up another page, this time fully. It runs maps down the side to highlight the matches and scenarios the author discusses, reusing the same map as above, but because this is an interior page, in black and white. It probably looks even better online as they likely kept the colour. (They did. But the maps are smaller.)
Overall, I really enjoyed the piece and the maps and visuals not only drew me into the piece, but helped contextualise the story.
Yesterday we looked at an article about exporting guns from one state to another. After writing the article I sat down and recalled that the copy of the Economist sitting by the sofa had a small multiple chart looking at murders in a select set of US cities. It turns out that while there was a spike, it appears that lately the murder rate has been flat.
It’s a solid chart that does its job well. That is probably why I neglected to mention it until I realised it fit in with the map of Illinois and talk about gun crimes yesterday. Because there is plenty of other news through data visualisation that we can talk about this week.
Credit for the piece goes to the Economist Data Team.
And I’m not talking about walking into a bar late at night. Instead, I am talking about the ratio of likes to retweets to replies, which, for those of you unfamiliar with the service, refers to engagement with a person’s tweets on Twitter.
The Ratio does not come from FiveThirtyEight—read the article for the full background on the concept, it is well worth the read—but they applied it to President Trump, whom we all know has a penchant for tweeting. The basic premise of the ratio is that you want more retweets and likes than replies. Think of it like customer reviews. Rarely do people bother to put the effort in to complement good service, but they will often write scathing reviews if something does not fit their expectations. Same in Twitter. If I do not care for what you say, I will let you know. But if I do, it is easy for me to like it, or even retweet it.
Anyway, the point is they took this and applied it to the tweets of Donald Trump and received this chart.
What I truly enjoy is the interactivity. Each dot reflects a tweet, and you can reveal that tweet by hovering over it. (I would be curious to know if the dots move. That is, do they, say, refresh daily with new tabulations on the updated numbers of likes, retweets, and replies?)
But the post goes on using the same chart form, in both other interactive displays and as static, small multiple pieces, to explore the political realm of previous tweeting presidents and current senators.
A solid article with some really nice graphics to boot.
Credit for the piece goes to Oliver Roeder, Dhrumil Mehta, and Gus Wezerek.
Last week the Economist published an article looking at the attitudes of the young at university in the United States. The examination was sparked by the recent-ish waves of news about stifled speech on campuses. Thankfully, we have a long-running survey from those on the ground in our universities and it reveals some interesting facts. You should head on over to the article if you want the full set, but in general, to perhaps nobody’s surprise, the media is exaggerating the confrontations we have seen.
My only quibble with the graphic is the height of the small multiples. I probably would have increased the height a little bit to allow any real fluctuations over the years to show more readily. But, for all I know, that could have been a limitation of the space in which the designers had to work, i.e. converting a print graphic to work on their blog.
Credit for the piece goes to the Economist’s Data Team.
Like I said yesterday, the Red Sox season is over. And the coverage on offseason needs began in the morning papers. But I wanted to follow up on the data from yesterday and delve a bit more deeply into the offence.
Yes, we know it was roughly league average across the team. And we know it took a hit with David Ortiz’s retirement at the end of last year. But what happened? Well, I took those same OPS+ numbers for the starting nine and compared 2017 to 2016. I then looked further back to see how those same players performed throughout their careers (admittedly I skipped Hanley Ramirez’s 2 plate appearances in 2005.)
You should take a look at the full graphic, but the short version, pretty much everyone had an off year. And when everyone has an off year, it is a pretty safe bet the team will have an off year.
I meant to post this yesterday, but accidentally saved it as a draft. So let’s try this again.
Yesterday the New York Times published a print piece that explored how the Cassidy-Graham bill would change the healthcare system. This would, of course, be another attempt to repeal and replace Obamacare. And like previous efforts, this bill would do real damage to the aim of covering individuals. We know the dollar amounts in terms of changes to aid given to states, but in terms of the numbers of people likely to lose their coverage, that would have to wait for a CBO score.
The graphic makes really nice use of the tall vertical space afforded by two columns. (You can kind of see this too in the online version of the article.) At the beginning of the article, above the title even, are two maps that locate the states with the biggest funding gains and cuts. I wonder if the two maps could have been combined into one or if a small table, like in the online version, would have worked better. The map does not read well in the print version as the non-highlighted states are very faint.
The designer chose to repeatedly use the same chart, but highlight different states based on different conditions. This makes the small multiples that appear below the big version useful despite their small size. Any question about the particular length can be referenced in the big chart at the top.
With the exception of the maps at the top of the piece, this was a great piece that used its space on the page very well.
I added Chelsea to make doubly certain for my Philadelphia audience that you did not think I was referring to Philly’s Kensington. Why? Because today’s piece comes from the Guardian and refers to the neighbourhood where the Grenfell Tower caught fire and the inferno killed dozens of people.
This is not the most complex piece, but I really like the annotations and notes on the choropleth. They add a great amount of detail and context to a graphic that I imagine many places would be okay leave as is. I can see why the colour palette differs for the two maps, but I wonder if it could have been made to work as a unified palette.
Credit for the piece goes to the Guardian graphics department.
This past weekend, I came upon a neat little graphic in the New York Times supporting an article about the impact of climate change on temperatures. The article basically lays out the argument that summers are getting hotter. And as a cold-weather person, that is dreadful news.
But the good news is the graphic was well done. It uses the outline of the baseline data as a constant juxtaposition against the date interval examined. And the colour breaks remain in place to show that compared to what we consider “normal”, we are seeing a shift to the higher end of the spectrum.
Credit for the piece goes to the New York Times graphics department.
Last week the Economist posted an intriguing article about the relationship between culinary choices/preferences and education and income. It began with an article by David Brooks in the Times, which I have not read, talking about how culture can create inequality as much as economics or government policy. The Economist then conducted a survey looking at the relationship between food preferences and both education and income. This is a screenshot of some of their results.
Yes, correlation is not causation, but these are some fascinating findings that suggest we should perhaps explore the idea in more depth.
As to the graphics, we have nothing super sophisticated, just a matrix of small multiples. But that goes to the point of “simple” graphics sometimes can do wonders for a story.
Credit for the piece goes to the Economist graphics department.
You all know that I love small multiples. And we have been seeing them more often as representations of the United States. But today we look at a small multiple map of London. The piece comes from the Economist and looks at the declining numbers of pubs in London. With the exception of the borough of Hackney, boroughs all across London are seeing declines, though the outer boroughs have seen the largest declines.
The only thing that does not work for me is the bubble in each tile that represents the number of pubs. That gets lost easily among the blue backgrounds. Additionally, the number itself might suffice.
Credit for the piece goes to the Economist graphics department.