This past weekend I saw the film Darkest Hour with one of my mates. The film focuses on Winston Churchill at the very beginning of his term as prime minister. Coincidentally I was walking through some of the very rooms and corridors depicted in the film—and rather accurately I should say—just one week prior.
One of the things in the real place that caught my eye in particular was the Map Room Annex. Most people know about the Map Room proper, from which the British Empire’s war effort was coordinated, but the annex contained data on wartime casualties, material production, &c. Consequently the walls were lined with displays of that data. But this was also the early 1940s and so none of it was computerised. Instead, we had handmade charts.
Alas, the space is quite narrow and the museum was quite crowded. So I only managed a snapshot or two, but I think this one does some justice to the hardworking folks producing charts about the war.
Credit for the piece goes to some junior officer/staffer back in the day.
Last week we saw a lot of news break, and then here at Coffeespoons we had the usual American Thanksgiving holiday with which to contend. So now that things are creeping back to a new normal, let us dive back into some of the things we missed.
How about those German coalition government talks?
Remember two months ago when we looked at Die Welt and the German election results? Well it turns out that the FDP, the liberal (in the more classical sense that makes them more centre-right) Free Democrats, have walked away from coalition talks with Chancellor Angela Merkel’s CDU/CSU party (it’s actually two separate parties that have an alliance) and the Green Party. That leaves Merkel with the the Social Democrats as the only other option to form a majority government. (She could attempt to hold a minority government, but from her own statements that appears unlikely.) But the Social Democrats do not appear too keen on joining up in a grand coalition.
So where does Germany stand? Well thankfully the Economist put together a short article with a few graphics to help show just how tricky putting together a new coalition government will be.
In terms of design, there is not too much to stay here. The colours are determined by the colours used by the political parties. And the 50% vote threshold is a common, but very useful and workable, convention. The only thing I may have done to emphasise the lack of change in the polling data is a line chart to show the percentage point movement or lack thereof.
Credit for the piece goes to the Economist Data Team.
Today is Election Day here in the States, but neither for the presidency nor for Congress. 2017 is an off-year, but it does have a few interesting races worth following. One is the New Jersey gubernatorial election across the river here from Philadelphia. Further down the Northeast Corridor we have the gubernatorial election in Virginia. And then I am going to be following the special election for a Seattle suburb’s state-level district. Why? Because it all gets to setting the table for 2022.
These three elections are all important for one reason, they relate to the idea of solid political control of a state government. The analogy is what we have in Washington, DC where the Republicans control the executive branch and both chambers of the legislative branch. In New Jersey, Democrats control the state legislature while (in?)famous Chris Christie, a Republican, is governor. In Virginia, Terry McAuliffe, a Democrat, is governor whilst the General Assembly is solidly Republican—we will get to that in a minute, trust me—and finally in Washington, the governorship is Democratic, the lower chamber of the state legislature is Democratic, but the state senate is Republican by one seat. And one of those very seats is up for a special election today.
So why am I making the big deal about this? Because solid political control of a state allows for biased redistricting, or gerrymandering, in 2020, when the US Census will reapportion seats to states, and thereby electoral college votes. If the Republicans win in Virginia, which is possible in what the polls basically have as a toss-up, they can redistrict Virginia to make it even harder for Democrats to win. And if the Democrats win in New Jersey and Washington, as they are expected to, they will be able to redistrict the state in their favour. Conversely, if the Democrats win in Virginia, and Republicans in New Jersey and Washington, they can thwart overly gerrymandered districts.
Which gets us to Virginia and today’s post. (It took awhile, apologies.) But as the state of Virginia changes, look at the dynamic growth in northern part of the state over the past decade, how will the changing demographics and socio-economics impact the state’s vote? Well, we have a great piece from the Washington Post to examine that.
It does a really nice job of showing where the votes are, in northern Virginia, and where the jobs are, again in northern Virginia. But how southern Virginia and Republicans in the north, might have just enough votes to defeat Democratic candidate Ralph Northam. The last polls I saw showed a very narrow lead for him over Republican Ed Gillespie. Interestingly, Gillespie is the very same Gillespie who architected the Republican’s massive victory in 2010 that obviously shifted the House of Representatives to the Republicans, but more importantly, shifted state legislatures and governorships to the Republicans.
That shift allowed for the Republicans to essentially stack the deck for the coming decade. And so even though in 2016, Democrats won more votes for the House of Representatives, they have far fewer seats. Even if there is a groundswell of new support for them in 2018, that same gerrymandering will make it near impossible for the Democrats to win the House. And so these votes in Virginia, New Jersey, and Washington state are fun to follow tonight—I will be—but they could also lay the groundwork for the elections in 2022 and 2024.
Basically, I just used today’s post to talk about why these three elections are important not for today, but for the votes in a few years’ time. But you really should check out the graphic. It makes nice use of layout, especially with the job bar chart organised by Virginia region. Overall, a solid and terrific piece.
Credit for the piece goes to Darla Cameron and Ted Mellnik.
I know I have said it before, but I like the increasing number of graphics-led articles published by Politico. Many policy and politics stories are driven—or should be driven—by data. But, myself included, we cannot hit it out of the park at every plate appearance. And that is what we have from Politico today, actually last week.
The graphic focuses on the healthcare industry and its need for a larger labour force in coming years as the baby boomers continue to age and start to retire. If their own doctors retire along with them, who will be their new doctors?
But there are two components of the graphic on which I want to focus. The first is the projection of the number of registered nurses (RNs) in 2024 compared to a 2014 baseline.
The story focuses on the future condition, but that colour is set to the lighter green thus drawing the reader’s eyes to the 2014 data point. Flipping those two colours would shift the focus of the chart to the 2024 timeframe, which would better match the text above.
Then we have the design decision to include a line chart for the growth rate, presumably total, for each category of RN from 2014 to 2024. The problem is that the chart itself does not sit on any baseline. While I do not care for the dual axis chart, that format at least keeps an axis legend on the right side of the chart. (You still have the problem of implying certain things based on what scale you choose to use relative to the first data series.) Here, because there is no chart lines associated with the growth data, I wonder if a table below the x-axis labels would be more efficient? Home health care, a very small category, will have the highest growth (a small change from a small base will beat the same small change or even slightly bigger changes from a far larger base) but the eye has the furthest to travel to reach the 61% number from the top of the bars or the labelling.
The other component I wanted to discuss is the scatter plot that compares the number of jobs to their average salary.
But this is a bubble chart, not a scatter plot, and so we have a third variable encoded in the size of the dot/bubble. The first thing I looked for was a scale for the size of the circles. What magnitude is the RN circle vs. the Personal Care Aides circle? There is none, but unfortunately that seems to be a common practice with bubble chart. But after failing to find that, I noticed that the circles decrease in size from right to left. That was when I looked to the legend and saw the y-axis in numbers of jobs and the x-axis in average salary. But then the circles are sized in proportion to the average salary of each profession to the other. In other words, the circles are basically re-plotting the x-axis. The physical therapist circle should be roughly twice as large, by area, than the vocational nurses. But we can also just see by the x-axis coordinates. The bubble chart-ness of the chart is unnecessary and the data could be told more clearly by stripping that away and making a straight-up scatter plot where all the circles are sized the same.
Credit for the piece goes to Christina Animashaun.
Yesterday the New York Times published a piece looking at the potential impacts of the proposed tax reforms on Americans. Big caveat, not a lot has been detailed about what the reforms entail. Instead, much remains vague. But using the bits that are clear, the Tax Policy Centre has explored some possible impacts and the Times has visualised them.
I like the opening graphic, though all are informative, that cycles through various proposals. It highlights which group benefits most from the proposals. The quick takeaway is that while all would moderately benefit, the rich do really well.
Yesterday Hurricane Ophelia hit Ireland and the United Kingdom. Yes, the two islands get hit with ferocious storms from time to time, but rarely do they enjoy the hurricanes like we do on the eastern seaboards of Canada, Mexico, and the United States.
Earlier this hurricane season the US had to deal with Harvey, Irma, and Maria. And in early October the Wall Street Journal published a piece that looked at the economic impact of the former two hurricanes as exhibited in economic data.
Overall the piece does a nice job explaining how hurricanes impact different sectors of the economy, well, differently. And wouldn’t you know it that leisure and hospitality is the hardest hit? But then they put together this stacked bar chart showing the impact of the hurricanes on both Florida/Georgia and Texas for Irma and Harvey, respectively.
The problem is that the stacked bar chart does not allow us to examine each hurricane as a specific data set. Because the Harvey data set is first, we have the common baseline and can compare the lengths of the magenta-ish bars. But what about the blue sets for Irma? How large is natural resources and mining compared to professional and business services? It is incredibly difficult to tell because neither bar starts at the same point. You must mentally move the bars to the same baseline and then hope your brain can accurately capture the length.
Instead, a split bar chart with each sector having two bars would have been preferable. Or, barring that, two plots under the same title. Then you could even sort the data sets and make it even easier to see which sectors were more important in the impacted areas.
Stacked bar charts work when you are trying to show total magnitude and the breakdowns are incidental to the point. But as soon as the comparison of the breakdowns becomes important, it’s time to make another chart.
I rarely watch American football. But I do like charts about it. So today’s post looks at a piece from Benjamin Morris who explored the scenarios in which a team should opt for the two-point conversion. For those of you who know even less about American football, you can attempt such a conversion after your team scores a touchdown. More often than not your team will go for the far safer and more assured one-point conversion, which if made makes a touchdown of seven points.
It turns out that teams should probably be looking for those two points a wee bit more often than they presently do. And to help teams figure that out, Morris made a small multiple chart looking at many different scenarios.
Less than a week after posting about the satellite views showing entire villages razed to the ground, we have a piece from the Economist looking at refugee outflows. And they are worse than the outflow of refugees during the Rwandan genocide back in 1994.
To be clear, they are not saying that nearly a million people have been killed—though there is quite a bit of evidence to say the Burmese security forces are cleansing the state of Rakhine of one of its primary ethnic groups.
But when it comes to the chart, I am not quite sure what I feel about it. It uses both the x and y axis to show the impact of the refugee outflow. But the problem is that we are generally rubbish at comparing areas. Compounding that, we have the total number of refugees represented by circles, another notorious way of displaying areas. (Often people will confuse the circle’s area with its radius or diameter and get the scale wrong.)
I wonder, would a more straight forward display that broke the dataset into two charts would be clearer? What if the designers had kept the Marimekko-like outflow display, but represented each crisis and its total outflow as a straight bar chart to the right of the timeline? (I do think the timeline is particularly good context, especially since it highlights the earlier persecution of the Rohingya.)
Credit for the piece goes to the Economist’s Data Team.
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.
Your author is on holiday today and is actually writing today’s post on a Thursday night train to Boston. But by the time he returns late Sunday night—a Monday morning post is not guaranteed—Hurricane Irma will have likely made landfall somewhere along the Florida coast.
Thursday the Guardian published a nice article looking at the potential tracks for Irma. And while the specific routes will certainly be amended and updated over the weekend, the article is worth looking at prior to Irma’s arrival at Florida. As of my writing the track has shifted ever slightly westward and the current predicted path looks for Irma to land south and west of Miami. Ergo this screenshot is already a little outdated.
The remarkable thing about this graphic, which is just a cleaner version of the standard meteorological maps through more a more considered palette, is that there is not just one path of winds, but three. Following quickly on the heels of Irma are Katia and Jose, the latter the one taking the nearly same path as Irma while Katia spins towards Mexico.
But the graphic I really wanted to look at is the one ending the piece.
This looks at the countries in Irma’s path as of Thursday morning. What I do not understand is the vertical axis of the bars. What does the height represent? To simply show the rank of countries able to cope with natural disasters, a more straight-forward table could have been used. A dot plot would also make some sense, but again, it would require an understanding of the underlying metrics driving the chart.
The graphic is saved by the annotations, in particular the more/less vulnerable directional arrows. Because I do not understand why countries are grouped into the particular buckets, I find the coloured bins out of place.
I think the concept of showing the most vulnerable countries is terribly important, however, the graphic itself needed a little more thought to be a little more clear in presenting the concept.
Credit for the piece goes to the Guardian graphics department.