Trump Keeps Attacking the Special Counsel

Yesterday the New York Times published a fascinating piece looking at the data on how often President Trump has gone after the Special Counsel’s investigation. (Spoiler: over 1100 times.) It makes use of a number of curvy line charts showing the peaks of mentions of topics and people, e.g. Jeff Sessions. But my favourite element was this timeline.

All the dots. So many dots.
All the dots. So many dots.

It’s nothing crazy or fancy, but simple small multiples of a calendar format. The date and the month are not particular important, but rather the frequency of the appearances of the red dots. And often they appear, especially last summer.

Credit for the piece goes to Larry Buchanan and Karen Yourish.

A Macedonia by any Other Name

As someone who loves geography and maps, I have plenty of printed atlases and map books. One year, as a gift, my family gave me an early 20th century atlas. That one in particular is remarkable because of how much the world changed between 1921 and 2019—what was French West Africa is now several independent countries.

But our maps may be changing again as Greece has now formally recognised the Former Yugoslav Republic of Macedonia as North Macedonia, what most of the world simply calls Macedonia. But Greeks do not want you to confuse that Macedonia with the Macedonia (or Macedon) of Ancient Greece and Alexander the Great. The squabble over the name has prevented what will be North Macedonia from joining the European Union and NATO because of Greek objections.

As the Economist recently showed, however, it might take a little while before the name Macedonia catches on with the public at large. (Note, I intended to type North Macedonia but instead went with Macedonia. I opted to leave it incorrect just to show how difficult it will be.)

What's in a name?
What’s in a name?

The plot uses my favourite small multiples to look at six countries whose names have changed. Some of you may be unfamiliar with the originals. Bechuanaland may be the most obscure, but Burma and Ceylon may be far more familiar. Of course the historian in me then wonders why the mentions of countries spiked in books. But small multiples are usually not the place to do detailed annotations to humour an audience of one.

In terms of its design, we have an effective use of colour and line. I may have dropped the thin red line for the max 100 value as it makes the piece a bit busy overall, but that might just be house style.

Of course for this graphic in particular, we will have to wait several years before we can add Macedonia/North Macedonia to the plot.

Credit for the piece goes to the Economist Data Team.

Election Day

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).

I'm not saying you can't keep voting. You just can't keep voting here.
I’m not saying you can’t keep voting. You just can’t keep voting here.

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 Deluxe House model
The Deluxe House 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.

In the Senate, things don't look good for the Democrats
In the Senate, things don’t look good for the Democrats

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.

That's a lot of red states…
That’s a lot of red states…

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.

#MeToo After One Year

One year on and the #meToo movement continues to upend the political, economic, and cultural landscape of the United States. And a few days ago the New York Times published a piece on all the stories they have collected.

From a data visualisation standpoint, this is a fairly simple piece. It takes 201 men (and a few women) who allegedly committed crimes along with their photo (if available) and then shows who replaced them. The screenshot below is of the total number of faces—notably not all men have been replaced—and then divides those who replaced them by gender.

Naturally it starts with Weinstein at the top…
Naturally it starts with Weinstein at the top…

The bit at the bottom shows how the case studies work. A man is on the left and who replaced him is on the right, both in the interim and more permanently, if applicable. A brief text account of the story falls below the alleged offender. And with 200+ stories, you can scroll for days.

Credit for the piece goes to Audrey Carlsen, Maya Salam, Claire Cain Miller, Denise Lu, Ash Ngu, Jugal K. Patel, and Zach Wichter.

First Florence, Now Michael

You may recall a few weeks ago there was a hurricane named Florence that slammed into the Carolina before stalling and dumping voluminous amounts of rain that inundated inland communities in addition to the damage by the storm surge in the coastal communities. At the time I wrote about a New York Times piece that explored housing density in coastal areas, specifically around the Florence impact area.

Well today the New York Times has a print graphic about something similar. It uses the same colours and styles, but swaps in a different data set and then uses a small multiple setup to include the Florida Panhandle. Of course the Florida Panhandle was just struck by Hurricane Michael, a Category 4 storm when it made landfall.

Of course that track for Michael also brought significant rainfall to the areas recovering from Florence for a double whammy
Of course that track for Michael also brought significant rainfall to the areas recovering from Florence for a double whammy

This one instead looks at median income per zip code to highlight the disparity between those living directly on the coast and those inland. In these two most recent landfall areas, the reader can clearly see that the zip codes along the coast have far greater incomes and, by proxy, wealth than those just a few zip codes further inland.

The problem is that rebuilding lives, communities, and infrastructure not only takes time, but also money. And with lower incomes, some of the hardest hit areas over the past several weeks could have a very difficult time recovering.

Regardless, the recoveries on the continental mainlands of the Carolinas and Florida will likely be far quicker and more comprehensive than they have been thus far for Puerto Rico.

The only downside with this graphic is the registration shift, which is why the graphic appears fuzzy as colours are ever so slightly offset whereas the single ink black text in the upper right looks clear and crisp.

Credit for the piece goes to the New York Times graphics department.

Our Lives Are a Mixed Bag

Last Thursday the Economist published an article looking at quality of life across the world. The data came from the Social Progress Imperative and examined quality of life, excluding economic performance. And as the article details, the results were mixed at best.

But, hey, the chart was really nice. We have a small multiple set looking at the overall index across all regions across the world and then the US, China, and India in particular.

Unfortunately the US is heading in the wrong direction…
Unfortunately the US is heading in the wrong direction…

I think this chart hits almost all the right notes. My only qualm would be the component indices being placed alongside the overall index. I wonder if breaking the whole thing out by component would work. As it is, it generally works well, I am just curious because there is the one issue of the United States where our well-being line falls beneath that of the overall index. But then again, the story is the overall index.

Credit for the piece goes to the Economist Data Team.

Europe’s Far-right Parties

Yesterday we looked at the rise of the far-right in Sweden based on their electoral gains in this past weekend’s election. Today, the Economist has a piece detailing their strength throughout Europe and they claim that this type of nationalist party may have peaked.

The tile map, though
The tile map, though

The graphic fascinates me because it appears to be a twist on the box or tile map, which is often used to eliminate or reduce the discrepancies in geographic size so that countries, states, or whatevers, can be examined more easily and more equitably.

I am guessing that the ultimate sizes, which appear to be one to four units, are determined by population size. The biggest hitters of Germany, the UK, France, and Spain are all four squares or boxes whereas the smaller states like Malta are just one. (But again, hey, we can all see Malta this time.)

I think this kind of abstraction will grow on me over time. It is a clever solution to the age-old problem of how do we show important data in both Germany and Malta on a map when Malta is so geographically small it probably renders as only a few pixels.

On the other hand, I am not loving the line chart to the right. I understand what it is doing and why. And even conceptually it works well to show the peaks of the parties. However, there are just a few too many lines and we get into the spaghettification of the chart. I might have labelled a far fewer number and let most sit at some neutral grey. Or, space permitting, a series of small multiples could have been used.

Credit for the piece goes to the Economist Data Team.

The Toll of the Trolls

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.

They're all just ugly trolls. Nobody loves them.
They’re all just ugly trolls. Nobody loves them.

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.

Credit for the piece goes to Oliver Roeder.

Ohio 12th Results

Last week parts of Ohio voted for a special election in the 12th Congressional District. Historically it has been a solidly Republican district by margins in the double digits. However, last week Republicans barely managed to hold the seat by, at the latest count I saw, less than one percentage point. Why? Well, it turns out that Republican support is bleeding away from one of the traditional strongholds: suburban counties.

I saw this data set late last week on Politico and I knew instinctively that it needed to be presented in another form than a table. Consequently I sketched out how it could work as small multiples of area charts to highlight just how Republican the district is. This is the digitisation of that take. Unfortunately my original sketch also featured a map of the district to show how this falls to the north and east of the city of Columbus. But I did not have time for that. Instead, I sketched up something else, but I need time to work on that. So for now, this concept will have to suffice.

That flip to the Democrats in Franklin County could be  a problem come November
That flip to the Democrats in Franklin County could be a problem come November

Credit for the piece is mine.

My New Toast

I am a millennial. That broadly means I am destroying and/or ruining everything. It also means I am obsessed with things like avocado toast. It also means I am not buying a house. Thankfully the Economist is on top of my next fad: indoor houseplants.

Plant things
Plant things

Your author will admit to having a few: a hanging plant, an Easter lily, an aloe plant and its children, and a dwarf conifer. Just don’t ask me how they’re doing. (Hint: not well.) Turns out I am not a plant person.

In terms of the graphic, though, what we have is a straight up set of small multiples of line charts. The seasonality mentioned in the article text appears quite clearly in a number of plants.

But is Swiss Cheese really a plant?

Credit for the piece goes to the Economist Data Team.