Golden Buttered Popcorn

We are in the midst of basketball playoffs right now. And one of the teams participating is the Golden State Warriors. They are pretty good at this whole basketball thing. One of the reasons is their star player Steph Curry. And it turns out that he is an enormous fan of popcorn. So much so that despite the widespread focus on power foods and healthy eating and wellness lifestyle, he devours the stuff before matches. So much so that NBA minders had to remove it from his hands during an all-star match last year.

He agreed to a request from the New York Times to rank each stadium, from 1 to 29, on the best popcorn. But he then went further and suggested that he rank the popcorn on a five-point scale on five different metrics: freshness, saltiness, crunchiness, butter and presentation. Naturally, the Times agreed. And he prepared a dataset that the Times turned into this heat map.

He's so honest, his own stadium doesn't rank in the top five.
He’s so honest, his own stadium doesn’t rank in the top five.

The whole article is well worth a read for more insights into the player and his take on popcorn. I don’t know a thing about basketball, but if a player agrees to a request to rank stadiums based on their popcorn, but then goes further to create additional data that can be used to turn into a visualisation, he’s probably my favourite player. If only someone had asked this of Pedro, Nomar, or Big Papi back in the day. Here’s looking at you, Laser Show.

Happy Friday, everyone.

Credit for the piece goes to Steph Curry and Marc Stein.

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…
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.

Far too many boxes on the right…
Far too many boxes on the right…

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.

Pennsylvania’s Population Shifts

Last month the US Census Bureau published their first batch of 2018 population estimates for states and counties. Pennsylvania is one of those states that is growing, but rather slowly. It will likely lose out to southern and western states in the 2020 census after which House seats will be reapportioned and electoral college votes subtracted.

From 2018 to 2010, the Commonwealth has grown 0.8%. Like I said, not a whole lot. But unlike some states (Illinois), it is at least growing. But Pennsylvania is a very diverse state. It has very rural agricultural communities and then also one of the densest and largest cities in the entire country with the whole lot in between . Where is the growth happening—or not—throughout the state? Fortunately we have county-level data to look at and here we go.

Some definite geographic patterns here…
Some definite geographic patterns here…

The most immediate takeaway is that the bulk of the growth is clearly happening in the southeastern part of the state, that is, broadly along the Keystone Corridor, the Amtrak line linking Harrisburg and Philadelphia. It’s also happening up north of Philadelphia into the exurbs and satellite cities.

We see two growth outliers. The one in the centre of the state is Centre County, home to the main campus of Pennsylvania State University. And then we have Butler County in the west, just north of Pittsburgh.

The lightest of reds are the lowest declines, in percentage terms. And those seem to be clustered around Scranton and Pittsburgh, along with the counties surrounding Centre County.

Everywhere else in the state is shrinking and by not insignificant amounts. Of course this data does not say where people are moving to from these counties. Nor does it say why. But come 2020, if the pattern holds, the state will need to take a look at its future planning. (Regional transit spending, I’m looking at you.)

Natural Disasters

Today’s piece is another piece set against a black background. Today we look at one on natural disasters, created by both weather and geography/geology alike.

The Washington Post mapped a number of different disaster types: flooding, temperature, fire, lightning, earthquakes, &c. and plotted them geographically. Pretty clear patterns emerge pretty quickly. I was torn between which screenshots to share, but ultimately I decided on this one of temperature. (The earthquake and volcano graphic was a very near second.)

Pretty clear where I'd prefer to be…
Pretty clear where I’d prefer to be…

It isn’t complicated. Colder temperatures are in a cool blue and warmer temperatures in a warm red. The brighter the respective colour, the more intense the extreme temperatures. As you all know, I am averse to warm weather and so I will naturally default to living somewhere in the upper Midwest or maybe Maine. It is pretty clear that I will not really countenance moving to the desert southwest or Texas. But places such as Philadelphia, New York, and Washington are squarely in the blacked out or at least very dark grey range of, not super bad.

Credit for the piece goes to Tim Meko.

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.

Disc Space

One of my current projects is consolidating and organising all my genealogy files spread across multiple devices and drives into one central location. So I’ve been spending quite a bit of time looking at file sizes and things. And that is why this piece from xkcd made me laugh.

So true.
So true.

Happy Friday, all.

Credit for the piece goes to Randall Munroe.

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.
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.

Not a whole lot to see here…
Not a whole lot to see here…

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
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.

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
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
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.