What Is Infrastructure?

This morning I read a piece in Politico Playbook that broke down President Biden’s $2.25 trillion proposal for infrastructure spending. A thing generally regarded as the United States sorely needs. $2.25 trillion is a lot of money and it’s a fair question to ask whether all that money is really money for infrastructure.

Because, it turns out, it’s not.

Please, sir, may I have more train money?

That isn’t to say money spent on job retraining or home care services wouldn’t be money well spent. Rather, it’s just not infrastructure.

But politics and the English language is a topic for another day. Oh wait, somebody already did write about that.

Credit for the piece is mine.

Biden’s Cabinet

Note: I wanted this to go up on Inauguration Day, but I had some server issues last week. And while I got everything back for Friday and Monday, I didn’t want to wait too long to post this. You’ll note at the end that I have questions about General Austin and whether he could be confirmed as Defence Secretary. Spoiler: He was.

Today is Inauguration Day and at noon, President Trump returns to being a citizen and Joe Biden assumes the office of the presidency. He comes to office with arguably the most diverse cabinet in American history supporting him and his agenda.

CNN took a look at that diversity with this piece, which uses an interactive, animated stacked bar chart.

The proposed cabinet vs. the US ethnic breakdown

I took a screenshot at the ethnic/racial diversity. At the top, each bar represents one member of cabinet who you can reveal after mousing over the bar. Below is a stacked bar chart showing the racial makeup of the United States. You can see how it does resemble, and in some cases exceeds, the diversity of the broader United States.

One thing to note, however, is that we see 26 members of Cabinet. Some of those are the heads of the big executive departments like Treasury and Defence. But I’m not certain everyone is technically a cabinet-level position, e.g. Celia Rouse, Chair of the Council of Economic Advisors. It could be that the position is being elevated to cabinet level like John Kerry’s role as climate envoy. And if I just missed the press announcement, that’s on me. But that could affect the overall numbers.

Regardless, the nominated cabinet is more diverse than the previous two administrations as the CNN piece also shows.

The proposed cabinet vs. the preceding inaugural cabinets

I should point out that usually an incoming administration usually has a few of its national security positions already confirmed or confirmed on the first day, e.g. Defence and State. However, the Republican Senate, obsessed with the lie of a fraudulent election, has only just begun to start the confirmation process. In fact, as of late last night, only Avril Haines has been confirmed by the Senate (84–10) for Director of National Intelligence.

Furthermore, almost every administration has one or two nominations that fail to pass the Senate. George W Bush had Linda Chavez, Barack Obama had Tom Daschle, and Donald Trump had Andrew Puzder, just to give one from each of the last three administrations.

With a 50–50 Senate, I would expect there to be a few nominees who fail to make it over the line. Austin could be one, there appears to be some bipartisan agreement that we ought not nominate recent military officials as civilian heads of said military. Another to keep an eye out for is Neera Tanden. She riles conservatives and angers Bernie Sanders supporters, so whether the Senate will confirm her as Director of the Office of Management and Budget remains an open question in my mind.

Credit for the piece goes to Priya Krishnakumar, Catherine E. Shoichet, Janie Boschma and Kenneth Uzquiano.

Can Texas Go Blue on Tuesday?

One story I’m following on Tuesday night is Texas. The state’s early voting—still with Monday to go—has surpassed the state’s total 2016 vote. Polling suggests that early votes lean Biden due to President Trump’s insistence that his supporters vote in person on Election Day as he lies about the integrity of early and mail-in voting.

The Texas Tribune looked at what we know about that turnout and what it may portend for Tuesday’s results. And, to be honest, we don’t—and won’t—really know until the votes are counted. They put together a great piece that divided Texas counties into four groups (their terminology): big blue cities, fast-changing counties, solidly red territory, and border counties. They then looked at the growth in registered voters in those counties from 2016, and looked at how they voted in the 2016 presidential election (Hillary Clinton vs. Donald Trump) and the 2018 US Senate election (Beto O’Rourke vs. Ted Cruz).

The piece uses the above stacked bar chart to show that Texas’ 1.8 million new registered voters’ largest share belongs to the big blue cities. The second largest group is the competitive suburbs in the fast-changing counties. The third largest, though quite close to second, was the solidly red territory. The border counties, still important for the margins, ranks a distant fourth.

I’m not normally a fan of stacked bar charts, because they do not allow for great comparisons of the constituent elements. For example, try comparing any of of those solidly red territory counties to one another. But here, the value is more in the stacked set as a group rather than the decomposition of the set, because you can see how the big blue cities have, as a group, a greater number of those 1.8 million new voters.

Those fast-changing counties include a lot of the suburbs for Texas’ largest cities. And those are areas where, across the country, Republicans are losing voters by the tens of thousands to the Democrats. As battlegrounds, these presented a challenge, because as swing counties, they split their votes between Clinton and Cruz and Trump and Beto. And so the designers chose purple to represent them in the stacked design. I think it’s a solid choice and works really well here.

But in terms of the story, I’ll add that in 2016, Trump won Texas by 807,000 votes. Texas added 1,800,000 new voters since then. And turnout before Election Day is already greater than it was in 2016.

It’s still a state likely to go for Trump on Tuesday. But, if Biden has a good night, it’s not inconceivable that Texas flips. FiveThirtyEight’s polling average has Trump with only a 1.2 point lead.

Credit for the piece goes to Mandi Cai, Darla Cameron and Anna Novak.

Which of These Countries Does Not Belong

For those of you reading from the States, I hope you all enjoyed your holiday. And for my UK readers, I hope you all enjoyed your summer bank holiday last weekend. So now to the good and uplifting kind of news.

Something is clearly not right here.
Something is clearly not right here.

Indeed, a chart about deaths from firearms from the Economist. From a graphical standpoint, we all know how much I loathe stacked bar charts and this shows why. It is difficult for the user to isolate and compare the profiles of certain types of firearm violence against each other. Clearly there are countries where suicide by gun is more prevalent than murder, but most on this list are more murder happy.

And then the line chart that is cleverly spaced within the overall graphic, well, it falls apart. There are too many lines highlighted. Instead, I would have separated these out into a separate chart, made larger, so that the reader can more easily discern which series belongs to which country. Or I would have gone with a set of small multiples isolating those nine countries.

I am also unclear on why certain countries were highlighted in the line chart. Did they all need to be highlighted? Why, for example, is Trinidad & Tobago. It is not mentioned in the article, nor is it in the stacked bar chart.

But the biggest problem I have is with the data itself. But, every one of the countries on that list is among the developing countries or the least developed countries. Except one. And that, of course, is the United States.

Credit for the piece goes to the Economist Data Team.

The Economic Impact of Hurricanes

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.

I just want a common baseline…
I just want a common baseline…

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.

Credit for the piece goes to Andrew Van Dam.

O’Reilly’s Out

Of all the things I expected to cover this week, this was not one of them.

This is Fox New’s firing of Bill O’Reilly, their lead personality and heaviest hitter for the last 21 years, for accusations of sexual harassment both externally and internally. But up until yesterday afternoon, just how important was O’Reilly to Fox News? Well, as you might guess somebody covered just that question. The New York Times addressed the question in this online piece and uses a nice graphic to buttress their argument.

What goes up must eventually fall down
What goes up must eventually fall down

I like the use of the longer time horizon across the top of the graphic. But most important in it is the inclusion of the trend line. It helps the reader find the story amid the noise in the weekly numbers. The big decline towards the end of December looks important until one realises that it probably owes the drop to the Christmas holidays.

Then the bottom piece does something intriguing; it shows both the actuals and percentages side-by-side. Typically people love stacked bar charts—by this point you probably all know my personal reluctance to use them—and that would be that. But here the designer also opts to show the share as a separate data point beside the stacked bar charts.

I think the only thing missing from the piece is a bit more context. Is O’Reilly still the heaviest weight in the lineup? The top chart could have perhaps used some additional context of other shows over the last few months. For example, how does O’Reilly compare to Hannity?

Regardless, this piece does a fantastic job of showing the until-yesterday increasing importance of O’Reilly to Fox News and then Fox News’ importance to 21st Century Fox.

Credit for the piece goes to Karl Russell.

Wilders Wilts in the Netherlands

It’s a tulip joke, get it? Because the Netherlands.

The point of today’s piece is that Geert Wilders, the anti-EU, anti-Muslim, populist leader of the Dutch Freedom Party did not upset Prime Minister Rutte’s People’s Party for Freedom and Democracy (VVD), a centre-right party. Wilders had threatened to upset the status quo in the Netherlands earlier in the electoral season, but had come under pressure in recent weeks and days. He did, however, manage to come in second. Although its radical platform makes it highly unlikely to enter into any coalition government.

And speaking of coalition government, that is the Dutch way. With over a dozen parties competing for 150 seats, Rutte’s VVD looks to have won 33 seats—final results are expected in a few days’ time. Consequently, he will need the support of other parties to govern. And that gets us to today’s piece from the Guardian, a look at a few potential coalition scenarios. (As you probably know, I’m a huge fan of coalition governments.)

Which collection of colours will cross the finish line?
Which collection of colours will cross the finish line?

As you know I’m not a huge fan of stacked bar charts, but in this case the form works well. After all the point in this graphic is not to compare the number of seats held by each party—if it were, this fails—but to show the order needed to cross the 75 seat line. The table of who’s who above also is a great help to those not so familiar with Dutch politics who are trying to ascertain which coalition partnerships are more likely. After all, it’s highly unlikely a rightwing and leftwing party would come together to govern.

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

Deportation of Immigrants

Donald Trump announced how he wants to deport 2–3 million undocumented immigrants that have criminal convictions or that belong to gangs. I read up on the issue at FiveThirtyEight and came across the following graphic from the US Immigration and Customs Enforcement (ICE).

The government's chart on deportations
The government’s chart on deportations

However, when I review the graphic, I found it difficult to understand the FiveThirtyEight article’s point that President Obama has lessened the focus on deportation, but those deported are those convicted of serious criminal offences. So I expanded the size of the y-axis and broke apart the stacked bar chart to show the convicted criminals vs. the non-criminal immigration violators. This graphic more clearly shows the dramatic falloff in deportations, and the emphasis on those with criminal convictions.

A general decline in deportations has also seen a focus on convicted criminals over non-criminal immigration violators
A general decline in deportations has also seen a focus on convicted criminals over non-criminal immigration violators

Credit for the original goes to the graphics department of the US Immigration and Customs Enforcement. The other one is mine.