This piece was published Monday, so it’s one round out of date, but it still holds true. It looks at the betting odds of each of the candidates looking to enter No. 10 Downing Street. And yeah, it’s going to be Boris.
The thing that strikes me as odd about this piece however, is note the size of the circles. Why are they larger for Boris Johnson and Rory Stewart? It cannot be proportional to their odds of victory or else Boris’ head would be…even bigger. Is that even possible? Maybe it relates to their predicted placement of first and second, the two of which go to the broader Tory party for a vote. It’s really unclear and deserves some explanation.
The graphic also includes a standard line chart. It falls down because of spaghettification in that all those also rans have about the same odds, i.e. slim, to beat Boris.
Perhaps the most interesting thing to follow is who will be the other person on the ballot. But then who remembers Andrea Leadsom was the runner up to Theresa May?
Credit for the piece goes to the Economist graphics department.
Today is another day in the Tory leadership election that will eventually see approximately 120,000 members of the Conservative Party electing the next prime minister of the 66,000,000 people living in the United Kingdom. The remaining candidates need at least 33 votes from MPs to move on. Those and/or the last place candidate will be eliminated. The question today is whether Dominic Raab, Sajid Javid, and Rory Stewart can move to the next round along with the front runner Boris Johnson and his two not-really-close-but-someone-has-to-be-a-significant competitors, Jeremy Hunt and Michael Gove.
But what happens after today’s vote? The BBC created a graphic explaining it all.
It’s a simple concept: a calendar that uses shades and outline boxes to highlight particular dates.
But the elephant in this particular Westminster cloakroom is that the Tories are using all this time whilst the Brexit clock keeps ticking down to 31 October.
Credit for the piece goes to the BBC graphics department.
In case you did not hear, earlier this week Alabama banned all abortions. And for once, we do not have to add the usual caveat of “except in cases of rape or incest”. In Alabama, even in cases of rape and incest, women will not have the option of having an abortion.
And in Georgia, legislators are debating a bill that will not only strictly limit women’s rights to have an abortion, but will leave them, among other things, liable for criminal charges for travelling out of state to have an abortion.
Consequently, the New York Times created a piece that explores the different abortion bans on a state-by-state basis. It includes several nice graphics including what we increasingly at work called a box map. The map sits above the article and introduces the subject direct from the header that seven states have introduced significant legislation this year. The map highlights those seven states.
The gem, however, is a timeline of sorts that shows when states ban abortion based on how long since a woman’s last period.
It does a nice job of segmenting the number of weeks into not trimesters and highlighting the first, which traditionally had been the lower limit for conservative states. It also uses a nice yellow overlay to indicate the traditional limits determined by the Roe v. Wade decision. I may have introduced a nice thin rule to even further segment the first trimester into the first six week period.
We also have a nice calendar-like small multiple series showing states that have introduced but not passed, passed but vetoed, passed, and pending legislation with the intention of completely banning abortion and also completely banning it after six weeks.
This does a nice job of using the coloured boxes to show the states have passed legislation. However, the grey coloured boxes seem a bit disingenuous in that they still represent a topically significant number: states that have introduced legislation. It almost seems as if the grey should be all 50 states, like in the box map, and that these states should be in some different colour. Because the eight or 15 in the 2019 column are a small percentage of all 50 states, but they could—and likely will—have an oversized impact on women’s rights in the year to come.
That said, it is a solid graphic overall. And taken together the piece overall does a nice job of showing just how restrictive these new pieces of legislation truly are. And how geographically limited in scope they are. Notably, some states people might not associate with seemingly draconian laws are found in surprising places: Pennsylvania, Illinois, Maryland, and New York. But that last point would be best illustrated by another box map.
When Robert Mueller submitted his report a few weeks ago, some interested parties declared it a witch hunt that had wasted time and money. Except, it had done the opposite of that. It had laid bare Russia’s interference in our elections and the contacts between Russian government and quasi-government officials and Trump campaign officials. Said officials then lied about their contacts and, along with other crimes discovered during the course of the investigation, either pleaded guilty or were convicted. And while a few trials are still underway, we also now know 12 other cases have been referred to prosecutors but they remain under wraps.
At the time of submission, the New York Times was able to create this front page graphic.
It highlighted the key figures in the report’s investigation and identified their current status. Many of those charged, essentially all the Russians, are unlikely to ever stand trial because Russia will not extradite them.
Inside the piece we had two full pages covering the report. The graphics were rather simple, like this, although as these were black and white pages, colouring the photographs was not an option. Instead, the designers simply used headers and titles to separate out the rogues’ gallery.
This wasn’t a complicated piece, but it made sense as one of the first pieces. For months we had been told the investigation was “wrapping up soon”, or words to that effect. Then, out of nowhere, it finally did. In one day, and crucially without the actual report yet, work like this reminded us that the report had, in fact, achieved its purpose.
Credit for the piece goes to the New York Times graphics department.
One of the important stories of last week that was not black hole related was that of the re-election of the Likud Party in Israel, a party headed by Benjamin Netanyahu. This will be his fourth consecutive time as prime minister plus a fifth back in the late 1990s. Of course, he is facing an expected arrest and charges on corruption, so how long he might remain in office is yet to be determined.
However, the Economist put together this great piece using a Sankey diagram showing the ebbs and flows of the various political parties in Israel since its founding.
Obviously, this is only a partial screenshot, but it does a great job showing those changes. Most impressive is the designers’ ability to show the continuity of the evolving parties and the name changes and the splits and recombinations.
Credit for the piece goes to the Economist Data Team.
The United Kingdom crashes out of the European Union on Friday. That means there is no deal to safeguard continuity of trading arrangements, healthcare, air traffic control, security and intelligence deals, &c. Oh, and it will likely wreck the economy. No big deal, Theresa. But what do UK voters think about their leading political parties in this climate? Thankfully Politico is starting to collect some survey data from areas of marginal constituencies, what Americans might call battleground districts, ahead of the eventual next election.
And it turns out the Tories aren’t doing well. Though it’s not like Labour is performing any better, because polling indicates the public sees Corbyn as an even worse leader than Theresa May. But this post is more to talk about the visualisation of the results.
The graphics above are a screenshot where blue represents the Conservatives (Tories) and red Labour. The key thing about these results is that the questions were framed around a 0–10 scale. But look at the axes. Everything looks nice and evenly spread, until you realise the maximum on the y-axis is only six. The minimum is two. It gives the wrong impression that things are spread out neatly around the midpoint, which here appears to be four. But what happens if you plot it on a full axis? Well, the awfulness of the parties becomes more readily apparent.
Labour might be scoring around a five on Health, but its score is pretty miserable in these other two categories. And don’t worry, the article has more. But this quick reimagination goes to show you how important placing an axis’ minimum and maximum values can be.
Credit for the piece goes to the Politico graphics department.
It is Monday, so it must be another Brexit vote day. And today we have Indicative Vote Day 2. If you recall from last week, the House of Commons wrestled control over parliamentary business away from the government and created a two-step process to try and see if any alternative to Theresa May’s Brexit plan can receive a workable, sustainable majority in the House.
The first step went about as well as could be expected. Nothing received a majority, but a customs union and a confirmatory vote by the public on the final deal both came very close to a majority: 8 and 27 votes, respectively. Likely, the vote today will be on those options.
But one reason for this lack of majority is that the idea of Europe has always fractured the Conservative Party. And in a recent piece by the Economist, we can see just how fractured the Tories have become.
Maybe a little bit counterintuitively, this plot does not look at an MP’s opinion on Brexit, but just with whom they are more likely to vote. The clearest takeaway is that whilst Labour remains relatively united, the Tories are in a small little divisions across the field.
In terms of design, there is not much to comment upon. It is not a scatter plot in terms of the placement of the dots does not refer to Brexit opinions, as I mentioned. It is more about the groupings of MPs. And in that sense, this does its job.
Credit for the piece goes to the Economist Data Team.
Stepping away from both the Brexit drama and the aircraft drama of the week, let’s look at US political drama. Specifically, the Democratic field and some of the early support for candidates and assumed-to-be candidates.
This piece comes from an article about the bases of various candidates. From a data visualisation perspective it uses a scatter plot to compare the net favourability of the candidate to the share of people who have an opinion about said candidate.
But what if you don’t know who the candidate is? As in, you don’t know what they look like. Well, then it might be difficult to find Bernie or Elizabeth Warren. This kind of graphic relies on facial recognition. I’m not certain that’s the best, especially when one is talking about a field in which people may not know or have an opinion on the candidates in question.
Another drawback is that the sizes of the faces are large. And, especially in the lower left corner, this makes it easier to obscure candidates. Where exactly is Sherrod Brown? Between a unidentified face and that of Terry McAuliffe.
I think a more simplistic dot/circle approach would have worked far better in this instance.
Credit for the piece goes to the FiveThirtyEight graphics department.
This Washington Post piece caught my eye earlier this week. It takes a look back at all the departures from the Trump administration, which has been beset by one of the highest turnover rates of all time.
What I like about the piece is how it classifies personnel by whether or not they require Senate confirmation. For example, Ryan Zinke as Interior Secretary had to be approved by the Senate. Nick Ayers, Pence’s former chief of staff, did not.
Importantly each name serves as a link to the story about the person’s departure. It serves as a nice way of leading the user to additional content while keeping them inside the graphic.
The further down the piece you go, there are notable sections where blocks of body copy appear in the centre of the page. These provide much more context to the comings and goings around that part of the timeline.
Credit for the piece goes to Kevin Schaul, Reuben Fischer-Baum, and Kevin Uhrmacher
As many of you know, genealogy and family history is a topic that interests me greatly. This past weekend I spent quite a bit of time trying to sort through a puzzle—though I am not yet finished. It centred on identifying the correct lineages of a family living in a remote part of western Pennsylvania. The problem is the surname was prevalent if not common—something to be expected if just one family unit has 13 kids—and that the first names given to the children were often the same across family units. Combine that with some less than extensive records, at least those available online, and you are left with a mess. The biggest hiccup was the commonality of the names, however. It’s easier to track a Quinton Smith than a John Smith.
Taking a break from that for a bit yesterday, I was reminded of this piece from the Economist about two weeks ago. It looked at the individualism of the United States and how that might track with names. The article is a fascinating read on how the commonness or lack thereof for Danish names can be used as a proxy to measure the individualism of migrants to the United States in the 19th century. It then compares that to those who remained behind and the commonness of their names.
The scatter plot above is what the piece uses to introduce the reader to the narrative. And it is what it is, a solid scatter plot with a line of best fit for a select group of rich countries. But further on in the piece, the designers opted for some interesting dot plots and bar charts to showcase the dataset.
Now I do have some issues with the methodology. Would this hold up for Irish, English, German, or Italian immigrants in the 19th century? What about non-European immigrants? Nonetheless it is a fascinating idea.
Credit for the piece goes to the Economist Data Team.