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.