critique – Page 9 – Coffee Spoons

Britain Bombing in Eurovision

Last weekend was not only the Game of Thrones finale, but also the Eurovision final. For the Americans not familiar with it, it’s a part music, part theatrics competition between all European countries and then sometimes guest countries like Australia or Israel. The winner is chosen by the total number of points their act receives. The UK, as one of the largest countries in Europe, is one of the few countries that is guaranteed a spot.

But that doesn’t mean the UK performs well. Last weekend, the UK bombed. The winner, the Netherlands, scored 498 points. The UK? 11. But the UK has been terrible for years now. And unlike in American baseball, it’s not because tanking gets you coveted draft picks for new talent. The BBC charted the placement of the British entries since its last win in 1997, the height of Cool Britannia.

Consistently bad over the last several years

Design wise, I wonder about the horizontal movement of places. A top-to-bottom movement might make more sense. The labelling here is also a bit too much. My eye immediately settles on the black text for the years, as their tight spacing creates a dark field that overpowers the otherwise nice light blue–dark blue contrast in the graphic. Maybe the beginning and end years could have been labelled with some key intervals, say every five years?

Similarly, the use of the ordinal number over the cardinal on the right hand side puts more emphasis on the labelling than the graphic itself. Here, however, the designers wisely chose a grey for the text so as not to overpower the graphic. But I wonder if the use of a cardinal number could have reduced the extra bits of text at the end and drive more focus to the graphic.

Overall, it’s a neat graphic. But I think a few small tweaks could improve the design. Unfortunately for the UK, they are more than just a few small tweaks away from winning Eurovision 2020.

Credit for the piece goes to the BBC graphics department.

Bad Endings

Turns out I was not the only one to look at plotting the ratings of the final series of Game of Thrones. The Economist looked at IMDB ratings, but just prior to the finale on Sunday. They, however, took it a step further and compared Game of Thrones to the final series of other well regarded shows.

From a design standpoint, I’m not a huge fan of breaking the y-axis at 6. While the data action is all happening at the high range of the scale, that is also the point. Each show is at the top of its class, which makes the precipitous falls of Game of Thrones, Dexter, and House of Cards all the more…wait for it…stark.

I do like the shading behind the line to indicate the final series. That certainly makes it easier to differentiate between the final episodes and those that came before.

But again, I’ll just say, I like how Game of Thrones ended.

Credit for the piece goes to the Economist graphics department.

Abortion by State

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.

We've been calling these box maps. It's growing on me. — We’ve been calling these box maps. It’s growing on me.

The gem, however, is a timeline of sorts that shows when states ban abortion based on how long since a woman’s last period.

There are some crazy shifts leftward in this graphic…

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.

Credit for the piece goes to K.K. Rebecca Lai.

Missing Planets

In science news, we turn to graphics about planets and things. Specifically we are talking about exoplanets, i.e. planets that exist outside our solar system. Keep in mind that we have only been able to detect exoplanets since the 1990s. Prior to then, how rare was our system with all our planets? It could have been very rare. Now we know, probably not so much.

But, in all of that discovery, we are missing entire types of planets. This article published by Forbes does a nice job explaining why. But one of the key types of planets that we have been unable to discover heretofore have been: intermediately distant, giant planets. Think the Jupiters and Saturns of our system. Prior to now we could detect massive Jupiter-like planets orbiting super near to their distant stars. Or, we could detect super massive planets orbiting very far away. The in-betweeners? Not so much.

There's still a pretty wide gap out there… — There’s still a pretty wide gap out there…

The above screenshot does a good job of showing where new detection methods have allowed scientists to begin to fill in the gaps. It shows how there is an enormous gap between what we have discovered and how they have been discovered. And the article does a nice job explaining how the science works in that only now with our longer periods of observation will help resolve certain issues.

From a design standpoint, this isn’t a super complicated graphic. It does rely upon a logarithmic scale, which isn’t common in non-scientific or academic papers. But this graphic comes from that environment, so it makes a lot of sense. The article is full of graphics from third-party sources, but I found this the most informative because of that very gap it highlights and how the new work (the stars) begin to fill it in.

Credit for the screenshotted piece goes to E. L. Rickman et al.

Bar Chart Bombshells

Tuesday night/Wednesday morning, the New York Times broke the story that they had some of President Trump’s tax return information. For decades now, US presidents and candidates for that office have released their tax returns for the public to inspect. Trump has refused, often claiming that they are under audit from the IRS and then adding, and falsely claiming, they cannot be released whilst under audit. Consequently, when the Times publishes an article at the secret world of Trump’s finances, it’s a big news thing.

Unfortunately, the Times only had access to what are essentially summary transcripts of the returns. And only for a period in the mid-1980s through mid-1990s. So we cannot get the granular data and make deeper insights. But what we did get was turned into this bold graphic in the middle of the article.

That's a whole lotta red. And not the good kind for a Republican. — That’s a whole lotta red. And not the good kind for a Republican.

Conceptually, there is not much to say. The bar charts are a solid choice to represent this kind of data. Red makes sense given the connotation of “being in the red”. And the annotations providing quotes from Trump about his finances for the years highlighted provide excellent context.

What the screenshot does not truly capture, however, is the massiveness of the chart in the context of the rest of the article. It’s big, bold, and red. That design choice instead of, say, making it a smaller sidebar-like graphic, goes a long way in hitting home the sheer magnitude of these business losses.

Sometimes it’s not always fancy and shiny charts that garner the most attention. Sometimes an old staple can do wonders.

Credit for the piece goes to Rich Harris and Andrew Rossback.

Trump-won Counties Are Winning

Yesterday we looked at how China and the European Union are planning their tariff/trade war retaliation to target Trump voters. Today let’s take a look at how those voters are doing as this article from Bloom does.

Lots of green, but some noticeably red counties in Florida.

The article is not terribly complicated. We have four choropleth maps at the county level. Two of the maps isolate Trump-won counties and the other two are Clinton-won. For each candidate we have a GDP growth and an employment growth map.

In the Trump-won maps, the Clinton-won counties are white, and vice versa. Naturally, because the Democratic vote is greatest in the large cities, which, especially on the East Coast, are in tiny counties, you see a lot less colour in the Clinton maps.

Design wise, I should point out the obvious that green-to-red maps are not usually ideal. But the designers did a nice job of tweaking these specific colours so that when tested, these burnt oranges and green-blues do provide contrast.

Here they appear more of a yellow to grey

But I am really curious to see this data plotted out in a scatter plot. Of course the big counties in the desert southwest are noticeable. But what about Philadelphia County? Cook County? Kings County? A scatter plot would make them equally tiny dots. Well, hopefully not tiny. But then when you compare GDP growth and employment growth and benchmark them against the US average, we might see some interesting patterns emerge that are otherwise masked behind the hugeness of western counties.

But lastly. And always. Where. Are .Alaska. And. Hawaii? (Of course the hugeness problem is of a different scale in Hawaii. Their county equivalents are larger than states combined.)

Credit for the piece goes to the Bloomberg graphics department.

Trade War Retaliation

About a week and a half ago the Economist published an article about the retaliatory actions of the European Union and China against the tariffs imposed by the Trump administration. Of course last week we had a theme of sorts with lineages and ancestry. So this week, back to the fun stuff.

What makes today’s piece particularly relevant is that over the weekend, Trump announced he might increase the tariffs proposed, but not yet implemented, upon Chinese goods. So some economists looked at the retaliatory tariffs proposed by the EU and China.

Ultimately Trump's tariffs are not paid by foreign governments, but by US citizens. — Ultimately Trump’s tariffs are not paid by foreign governments, but by US citizens.

Each targets Trump voters, albeit of different types. But China appears more willing to engage in a brutal fight. Its tariff proposal would not just harm Trump voters, but would also harm Chinese citizens. The EU’s plan appears tailored to maximise the pain on Trump voters, but minimise that felt by its own citizens.

A few minor points. I like how the designers chose to highlight high impact categories with colour. Lower impact shares are two shades of light grey. But after that, the scale changes. I wonder how the maps would compare if each had been set to the same scale. It looks doable as the bottom range of the maximum bin is 6% for the EU and 8% for China. (Their high limit is much higher at 22% compared to the EU’s 10%.)

That said, it does a good job of showing the different geographic footprints of the two retaliatory tariff packages. Tomorrow—barring breaking news—we will look at why that is important.

Credit for the piece goes to the Economist Data Team.

The Great Migration Map

Yesterday in a post about Angela’s forced journey from Africa to Jamestown I mentioned that the Pilgrims arrived at Plymouth Bay just one year later in 1620. From 1620 until 1640 approximately 20,000 people left England and other centres like Leiden in the Netherlands for New England. Unlike places like Jamestown that were founded primarily for economic reasons, New England was settled for religious reasons. Consequently, whereas colonies in Virginia drew young men looking to make it rich—along with slaves to help them—New England saw entire families moving and transplanting parts of towns and England into Massachusetts, Rhode Island, Connecticut, and New Hampshire.

New England kept fantastic records and we know thousands of people. But we do not know whence everyone arrived, but we do know a few thousand. And this mapping project from American Ancestors attempts to capture that information at the English parish level. At its broadest level it is a county-level choropleth that shows, for those for whom we have the information, the majority of the migration, called the Great Migration, came from eastern England, with a few from the southwest.

Quite a few from Norfolk, Suffolk, and Essex

You can also search for specific people, in which case it brings into focus the county and the parishes within that have more detail. In this case I searched for my ancestor Matthew Allyn, who was one of the founders of Hartford, Connecticut. He came from Braunton in Devon and consequently appears as one of the two people connected to that parish.

Devon did not have nearly as many people emigrate as the eastern counties

But was Thomas related to Matthew? We don't know. — But was Thomas related to Matthew? We don’t know.

Overall, it’s a nice way of combining data visualisation and my interest/hobby of genealogy. The map uses the historical boundaries of parishes prior to 1851, which is important given how boundaries are likely to change over the centuries.

This will be a nice tool for those interested in genealogy and that have ancestors that can be traced back to England. I might be biased, but I really like it.

Credit for the piece goes to Robert Charles Anderson, Giovanni Flammia, Peter H. Van Demark.

Angela from Jamestown

Today we move from royalty to slavery. Earlier this week the Washington Post published an article about an African woman (girl?) named Angela. She was forcibly removed from West Africa to Luanda in present-day Angola. From there she was crammed into a slave ship and sent towards Spanish colonies in the Caribbean. Before she arrived, however, her ship was intercepted by English pirates that took her and several others as their spoils to sell to English colonists.

The article is a fascinating read and for our purposes it makes use of two graphics. The one is a bar chart plotting the Atlantic slave trade. It makes use of annotations to provide a rich context for the peaks and valleys—importantly it includes not just the British colonies, but Spanish and Portuguese as well.

My favourite, however, is the Sankey diagram that shows the trade in 1619 specifically, i.e. the year Angela was transported across the Atlantic.

Too many people took similar routes to the New World.

It takes the total number of people leaving Luanda and then breaks those flows into different paths based on their geographic destinations. The width of those lines or flows represents the volume, in this case people being sold into slavery. That Angela made it to Jamestown is surprising. After all, most of her peers were being sent to Vera Cruz.

But the year 1619 is important. Because 2019 marks the 400th anniversary of the first slaves being brought into Jamestown and the Virginia colony. The Pilgrims that found Plymouth Bay Colony will not land on Cape Cod until 1620, a year later. The enslavement of people like Angela was built into the foundation of the American colonies.

The article points out how work is being done to try and find Angela’s remains. If that happens, researchers can learn much more about her. And that leads one researcher to make this powerful statement.

We will know more about this person, and we can reclaim her humanity.

For the record, I don’t necessarily love the textured background in the graphics. But I understand the aesthetic direction the designers chose and it does make sense. I do like, however, how they do not overly distract from the underlying data and the narrative they present.

Credit for the piece goes to Lauren Tierney and Armand Emamdjomeh.

Who Bettors Think Will Sit Upon the Iron Throne

Last night was the third episode of the final series of Game of Thrones and thus marked its midway point. I shall save you from any spoilers, but I thought we could do a lighter post to start the week. This comes from the Economist and simply plots the characters and their implied probability of winning the Iron Throne.

For me, there are too many lines, too many colours and we get the usual spaghettification. But, c’mon, it’s a chart about Game of Thrones. That said, some small multiple grid of characters, sorted by probability would be pretty neat.

Credit for the piece goes to the Economist’s graphics department.