Tonight President Trump will give his State of the Union address, the annual speech about the president’s goals and agenda. Today I have a work meeting about management practices. So when I read this piece yesterday by Axios on Trump’s schedule (from a leak of November and December dates), I figured what better piece to highlight here on Coffeespoons.
To be fair, the concept is pretty straightforward. We have a stacked bar chart with each type of time block represented by a colour. Because the focus of the piece is the Executive Time blocks, I really think the designer did a great job summing the other types of time, e.g. travel and meetings, into one bin. And by being a lighter colour on nearly the same scale as the grey, it helps the orange Executive Time pop. Clearly Executive Time dominates the schedule, which as many analysts have been pointing out, is a departure from recent past presidents.
And, if you’re curious how the time blocks compare, elsewhere in the piece is a stacked bar chart summing all the types of time. Not surprisingly, most of his schedule is Executive Time.
January has ended, and with it for, apparently, a very few Britons, Dry January. The Economist looked at alcohol consumption, using a proxy of beer sales, and compared that against the number of times people searched for “Dry January” on Google.
What I really like about this chart is that it does not try to combine the two series into one. Instead, by keeping the series separate on different plots, the reader can clearly examine the trends in both searches and consumption.
You also run into the problem of how to overlay two different scales. By placing one line atop the other, the user might implicitly understand that as higher or better than the lower series when, one, that may not be true. Or, two, the scales are so different they prevent the direct comparison the chart would otherwise imply as possible.
Here, the designers rightly chose to separate the two plots, and then highlighted the month of January. (I also enjoy the annotation of the World Cup.) I might have gone so far as to further limit the palette and make both series the same colour, but I understand the decision to make them distinct.
But, overall, as the piece points out, drinking in Britain seems to correlate to the weather/temperature. People go out to the pubs more on warmer days than colder. But regardless of any post-holiday hangover, they still consumer beer in January.
I’ll drink to that.
Credit for the piece goes to the Economist Data Team.
We move from one manufactured crisis to another today as we look at a piece by the Economist on the number of illegal immigrants arrested at the US southern border. Lately, here in the United States we have been hearing of an invasion on our southern border. Illegal immigrants streaming across the border. Except, that is not true. In fact, illegal immigration is at or near its lowest rate in recent years.
The graphic does one thing really well and that is its unorthodox placement of the map. Instead of the usual orientation, here the designers chose to “tilt” the map so that the border segments roughly align with the sets of charts below them. I might have desaturated the map a little bit and cut off the gradient so Mexico does not bleed through underneath the bars, but the concept overall is really nice.
On the other hand, we have the bar charts arranged like funnels. This does allow the reader to see the slopes trending towards zero, however, it makes it incredibly difficult to see changes in smaller numbers. And without a scale on the axis, the reader has to take the bars and mentally transpose them on top of the grey bars in the bottom right corner. I wonder if a more traditional set of bar charts in small multiples could have worked better beneath the map.
Overall, however, I really do like this piece because of the way the map and the bar charts interact in their positioning.
Credit for the piece goes to the Economist Data Team.
I mentioned this this time last year, but I used to make a lot of datagraphics about GDP growth. The format here has not changed and so there is nothing new to look at there. But, the content is still interesting. And the accompanying Economist article makes the point that high growth rates are not always what they seem. After all, Syria’s high growth rate is because its base is so small.
Credit for the piece goes to the Economist Data Team.
Christmas time is a time when people receive gifts. Well this year was no different and I received a few. One, however, was in a box stuffed with old newspaper pages. And it turns out one of said pages had a graphic on it. So let us spend today looking at this little blast from the past.
The piece looks at PECO outages, PECO being the Philadelphia region’s main electricity supplier. The article is full page and is both headed and footed with photography, the graphic in which we are interested sits centre stage in the middle of the page.
Overall the graphic is fairly compact and works well at showing the distribution of the outages, which the bar chart below the choropleth shows was historically significant. (Despite my years in Chicago, I was somehow in the area for all but the storm written about and can confirm that they were, in fact, disruptive.)
The choropleth works, but I question the colour scheme. The bins diverge at about 50%, which to my knowledge marks no special boundary other than “half”. If that yellow bin represented, say, the average number of outages per storm or the acceptable number of outages per storm, sure, I could buy it. Otherwise, this is really just degrees of severity along one particular axis. I would have either kept the bins all red or all blue and proceeded from a light of either to a dark of either.
I probably would have also dropped Philadelphia entirely from the map, but I can understand how it may be important to geographically anchor readers in the most populous county to orientate themselves to a story about suburbia.
Lastly, I have one data question. With power lines down during an ice storm, I would be curious to see less of the important roadways as the map depicts and other variables. What about things like average temperature during the storm? Was the more urban and built-up Delaware County less susceptible because of an urban heat bubble preventing water from freezing? Or what about trees? Does the impact in the more rural areas have anything to do with increasing numbers of trees as one heads away from the city?
Those last data questions were definitely out of scope for the graphic, but I nevertheless remain curious. But then again, this piece is almost five years old. Just a look at how some graphical forms remain in use because of their solid ability to communicate data. Long live the bar chart. Long live the choropleth.
Credit for the piece goes to the Philadelphia Inquirer graphics department.
The 2018 midterm elections are finally here. Thankfully for political nerds like myself, the New York Times homepage had a link to a guide of when what polls close (as early as 18.00 Eastern).
It makes use of small multiples to show when states close and then afterwards which states have closed and which remain open. It also features a really nice bar chart that looks at when we can expect results. Spoiler: it could very well be a late night.
But what I really wanted to look at was some of the modelling and forecasts. Let’s start with FiveThirtyEight, because back in 2016 they were one of the only outlets forecasting that Donald Trump had a shot—although they still forecast Hillary Clinton to win. They have a lot of tools to look at and for a number of different races: the Senate, the House, and state governorships. (To add further interest, each comes in three flavours: a lite model, the classic, and the deluxe. Super simply, it involves the number of variables and inputs going into the model.)
The above looks at the House race. The first thing I want to point out is the control on the left, outside the main content column. Here is where you can control which model you want to view. For the whimsical, it uses different burger illustrations. As a design decision, it’s an appropriate iconographic choice given the overall tone of the site. It is not something I would have been able to get away with in either place I have worked.
But the good stuff is to the right. The chart at the top shows the percentage of likelihood of a particular outcome. Because there are so many seats—435 are up for vote—every additional seat is between almost 0 and 3%. But taken in total, the 80% confidence band puts the likely Democratic vote tally at what those arrows at the bottom show. In this model that means picking up between 20 and 54 seats with a model median of 36. You will note that this 80% says 20 seats. The Democrats will need 23 to regain the majority. A working majority, however, will require quite a few more. This all goes to show just how hard it will be for the Democrats to gain a workable majority. (And I will spare you a review of the inherent difficulties faced by Democrats because of Republican gerrymandering after the 2010 election and census.) Keep in mind with FiveThirtyEight’s model that they had Trump with a 29% chance of victory on Election Day 2016. Probability and statistics say that just because something is unlikely, e.g. the Democrats gaining less than 20 seats (10% chance in this model), it does not mean it is impossible.
The cartogram below, however, is an interesting choice. Fundamentally I like it. As we established yesterday, geographically large rural districts dominate the traditional map. So here is a cartogram to make every district equal in size. This really lets us see all the urban and suburban districts. And, again, as we talked about yesterday, those suburban districts will be key to any hope of Democratic success. But with FiveThirtyEight’s design, compared to City Lab’s, I have one large quibble. Where are the states?
As a guy who loves geography, I can roughly place, for example, Kentucky. So once I do that I can find the Kentucky 6th, which will have a fascinating early closing race that could be a predictor of blue waviness. But where is Kentucky on the map? If you are not me, it might be difficult to tell. So compared to yesterday’s cartogram, the trade-off is that I can more easily see the data here, but in yesterday’s piece I could more readily find the district for which I wanted the data.
Over on the Senate side, where the Democrats face an even more uphill battle than in the House, the bar chart at the top is much clearer. You can see how each seat breakdown, because there are so fewer seats, has a higher percentage likelihood of success.
The take away? Yeah, it looks like a bad night for the Democrats. The only question will be how bad does it go? A good night will basically be the vote split staying as it is today. A great night is that small chance—20%, again compared to Trump’s 29% in 2016—the Democrats narrowly flip the Senate.
Below the bar chart is a second graphic, a faux-cartogram with a hexagonal bar chart of sorts sitting above it. This shows the geographic distribution of the seats. And you can quickly understand why the Democrats will not do well. They are defending a lot more seats in competitive states than Republicans. And a lot of those seats are in states that Trump won decisively in 2016.
I have some ideas about how this type of data could be displayed differently. But that will probably be a topic for another day. I do like, however, how those seats up for election are divided into their different categories.
Unfortunately my internet was down this morning and so I don’t have time to compare FiveThirtyEight to other sites. So let’s just wrap this up.
Overall, what this all means is that you need to go vote. Polls and modelling and guesswork is all for nought if nobody actually, you know, votes.
Credit for the poll closing time map goes to Astead W. Herndon and Jugal K. Patel.
Credit for the FiveThirtyEight goes to the FiveThirtyEight graphics department.
Tomorrow is Election Day here in the United States and this morning I wanted to look at a piece I’ve had in mind on doing from City Lab. I held off because it looks at the election and what better time to do it than right before the election.
Specifically, the article looks at the density of the different congressional districts across the United States. Whilst education level appears to be the most predictive attribute of today’s political climate—broadly speaking those with higher levels of formal education support the Democrats and those with lower or without tend to support President Trump—the growing urban–rural divide also works. But what about the in-between? The suburbs? The exurbs? And how do we then classify the congressional districts that include those lands.
For that purpose City Lab created its City Lab Congressional Density Index. Very simplistically it scores districts based on their mixture of low- to medium- to high-density neighbourhoods. But visually, which is where this blog is concerned, we get maps with six bins from pure urban to pure rural and all the mixtures in-between. This cartogram will show you.
Now, there are a couple of things I probably would have done differently in terms of the visualisation. But the more I look at this, one of those things would not be to design the hexagons to all fit together nicely. Instead, you get this giant gap right where the plains states begin west of the Mississippi River stretching through the Rockies over to California. If you think about it, however, that is a fairly accurate description of the population distribution of the United States. With a few exceptions, e.g. Denver, there are not many people living in that space. Four geographically enormous states—North Dakota, South Dakota, Montana, and Wyoming—have only one congressional district. Idaho has two. Nebraska three. And then Iowa and Kansas four. So why shouldn’t a map of the United States display the plains and Rocky Mountain interior as a giant people hole?
Like I said, initially I took umbrage at that design decision, but the more I thought about it, the more it made sense. But there are a few others with which I quibble. The labelling here is a big one. First, we have the district labels. They are small, because they have to be to fit within the five hexagons that define the districts’ shapes. But every label is black. Unfortunately, that makes it difficult to read the labels on the darker colours, most notably the dark purple. I probably would have switched out the black labels in those instances for white ones.
But then the state labels are white with black outlines, which makes it difficult to read on either dark or light backgrounds. The designer made the right decision in making the labels larger than the districts, but they need to be legible. For example, the labels of Alaska and Hawaii need not be white with black outlines. They could just be set in black type to be legible. Conversely, Florida’s, sitting atop darker purple districts, could be made white.
The piece makes use of more standard geographic map divided into congressional districts—the type you will see a lot tomorrow night. And it makes use of bar charts to describe the demographics of the various density types. I like the decision there to use a new colour to fill in the bars. They use a dark green because it can cut across each of the six types.
For those of you reading from the States, I hope you all enjoyed your holiday. And for my UK readers, I hope you all enjoyed your summer bank holiday last weekend. So now to the good and uplifting kind of news.
Indeed, a chart about deaths from firearms from the Economist. From a graphical standpoint, we all know how much I loathe stacked bar charts and this shows why. It is difficult for the user to isolate and compare the profiles of certain types of firearm violence against each other. Clearly there are countries where suicide by gun is more prevalent than murder, but most on this list are more murder happy.
And then the line chart that is cleverly spaced within the overall graphic, well, it falls apart. There are too many lines highlighted. Instead, I would have separated these out into a separate chart, made larger, so that the reader can more easily discern which series belongs to which country. Or I would have gone with a set of small multiples isolating those nine countries.
I am also unclear on why certain countries were highlighted in the line chart. Did they all need to be highlighted? Why, for example, is Trinidad & Tobago. It is not mentioned in the article, nor is it in the stacked bar chart.
But the biggest problem I have is with the data itself. But, every one of the countries on that list is among the developing countries or the least developed countries. Except one. And that, of course, is the United States.
Credit for the piece goes to the Economist Data Team.
This is an older piece that I’ve been thinking of posting. It comes from FiveThirtyEight and explores some of the data about Russian trolling in the lead up to, and shortly after, the US presidential election in 2016.
The graphic makes a really nice use of small multiples. The screenshot above focuses on four types of trolling and fits that into the greyed out larger narrative of the overall timeline. You can see that graphic elsewhere in the article in its total glory.
From a design standpoint this is just one of those solid pieces that does things really well. I might have swapped the axes lines for a dotted pattern instead of the solid grey, though I know that seems to be FiveThirtyEight’s house style. Here it conflicts with the grey timeline. But that is far from a dealbreaker here.
By now you may have heard that this Thursday media outlets across the United, joined by some international outlets as well, have all published editorials about the importance of the freedom of the press and the dangers of the office of the President of the United States declaring unflattering but demonstrably true coverage “fake news”. And even more so, declaring journalists, especially those that are critical of the government, “enemies of the people”.
I have commented upon this in the past, so I will refrain from digressing too much, but the sort of open hostility towards objective reality from the president threatens the ability of a citizenry to engage in meaningful debates on public policy. Let us take the clearly controversial idea of gun control; it stirs passions on both sides of the debate. But, before we can have a debate on how much or how little to regulate guns we need to know the data on how many guns are out there, how many people own them, how many are used in crimes, in lethal crimes, are owned legally or illegally. That data, that verifiably true data exists. And it is upon those numbers we should be debating the best way to reduce the numbers of children massacred in American schools. But, this president and this administration, and certain elements of the citizenry refuse to acknowledge data and truth and instead invent their own. And in a world where 2+2=5, no longer 4, who is to say next that no, 2+2=6.
But the one editorial board that started it is that of the Boston Globe. I was dreading how to tie this very important issue into my blog, which you all know tries to focus on data and design. As often as I stand upon my soap box, I try to keep this blog a little less soapy. Thankfully, the Globe incorporated data into their argument.
The end of their post concludes with a small interactive piece that presents survey data. It shows favourability and trustworthiness ratings for several media outlets broken out into their political leanings. The screenshot below is for the New York Times.
The design is simple and effective. The darker the red, the more people believe an outlet to be trustworthy and how favourably they view it.
But before wrapping up today’s post, I also want to share another bit from that same Boston Globe editorial. As some of you may know, George Orwell’s 1984 is one of my favourite books of all time. I watched part of a rambling speech by the president a few weeks ago and was struck at how similar his line was to a theme in that novel. I am glad the Globe caught it as well.
Credit for this piece goes to the Boston Globe design staff.