Yesterday we looked at the wildfire conditions in California. Today, we look at the Economist’s take, which brings an additional focus on the devastation of the fires themselves. However, it adds a more global perspective and looks at the worldwide decline in forest fires and both where and why that is the case.
The screenshot here focuses on California and combines the heat and precipitation we looked at yesterday into a fuel-aridity index. That index’s actual meaning is simplified in the chart annotations that indicate “warmer and drier years” further along the x-axis. The y-index, by comparison, is a simpler plot of the acres burned in fires.
This piece examines more closely that link between fires and environmental conditions. But the result is the same, a warming and drying climate leaves California more vulnerable to wildfires. However, the focus of the piece, as I noted above, is actually on the global decline of wildfires.
Only 2% of wildfires are actually in North America, the bulk occur in Africa. And the piece uses a nice map to show just where those fires occur. In parallel the text explains how changing economic conditions in those areas are lessening the risk of wildfire and so we are seeing a global decline—even with climate change.
Taken with yesterday’s piece with its hyper-California focus, this provides a more global context of the problem of wildfires. It’s a good one-two read.
Credit for the piece goes to the Economist Data Team.
Wildfires continue to burn across in California. One, the Camp Fire in northern California near Chico, has already claimed 77 lives. But why has this fire been so deadly?
FiveThirtyEight explained some of the causes in an article that features a number of charts and graphics. The screenshot below features a scatter plot looking at the temperature and precipitation recorded from winter through autumn every year since 1895.
The designers did a good job of highlighting the most recent data, separating out 2000 through 2017 with the 2018 data highlighted in a third separate colour. But the really nice part of the chart is the benchmarking done to call out the historic average. Those dotted lines show how over the last nearly two decades, California’s climate has warmed. However, precipitation amounts vary. (Although they have more often tended to be below the long-term average.)
I may have included some annotation in the four quadrants to indicate things like “hotter and drier” or “cooler and wetter”, but I am not convinced they are necessary here. With more esoteric variables on the x- and y-axis they would more likely be helpful than not.
The rest of the piece makes use of a standard fare line chart and then a few maps. Overall, a solid piece to start the week.
Credit for the piece goes to Christie Aschwanden, Anna Maria Barry-Jester, Maggie Koerth-Baker and Ella Koeze.
Well this week Amazon finally chose not just one city for its HQ2, but two—New York and Washington. Of course Philadelphia had been angling for the site. Alas, it was not to be. So let’s work with that for this Friday post.
Your author is back after a few days out sick and then the Armistice Day holiday. But guess what? The elections are not yet all over. Instead, there are a handful of races to call. Below is a screenshot from a FiveThirtyEight article tracking those races still too close to call.
Why are there races? Because often time mail-in ballots need only be postmarked by Election Day. Therefore they can still be arriving in the days after the election and their total must be added to the race. (Plus uncounted/missed ballots et cetera.) For example, the late count and mail-in ballots are what tipped the Arizona senate seat. When we went to bed on Tuesday night—for me Wednesday morning—Arizona was a Republican hold, albeit narrowly. Now that the late count ballots have been counted, it’s a Democratic pickup.
The graphic above does a nice job showing how these races and their late calls are impacting seat changes. Their version for the House is not as interesting because the y-axis scale is so much greater, but here, the user can see a significant shift. The odds were always good that the Republicans would pick up seats—the question was how many. And with Arizona flipping, that leaves two seats on the table. Mississippi’s special election will almost certainly be a Republican hold. The question is what about Florida? The last I saw the race is separated by 0.15% of the vote. That’s pretty tiny.
Credit for the piece goes to the FiveThirtyEight graphics department.
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).
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 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.
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.
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.
I was reading the paper this morning and stumbled across this graphic in a New York Times article that focused on the increasing importance of debt payments.
The story is incredibly important and goes to show why the tax cuts passed by the administration are fiscally reckless. But the graphic is really smart too. After all, it is designed to work in a single colour.
Credit for the piece goes to the New York Times graphics department.
Another day, another allegation of sexual misconduct against Brett Kavanaugh. We are presently at two and are expecting a third tomorrow. But the question is, will these allegations sink his nomination? Probably not. But could that confirmation hurt Republicans in the mid-terms? Possibly.
The New York Times posted an article about how Kavanaugh’s support in battleground congressional districts is slipping. To be fair, the chart is simple, but it does its job. And usually that’s all we want a chart to do.
Me the person interested in politics, however, will take this a bit further. If Kavanaugh’s support continues to fade—this survey was taken before these new allegations were public—will Republicans supporting the nomination face a backlash from their constituents?
Sweden went to the polls this past weekend and the results are mostly in, with overseas ballots left to be counted. But the results are clear, a stark rise for the nationalist Sweden Democrats, though not as high as some had feared late last week.
Not surprisingly we had the standard parliamentary seat chart, seen below by the BBC. The nice twist this time is the annotations stating the seat change. (More on that later.)
It does a good job of showing the parties and how they are laid out, though I am sometimes more partial to a straight-up bar chart like below at Reuters.
However, both do not do a great job in showing what would traditionally be a kingmaker result for Sweden Democrats. When stacked at each end, neither the centre-left bloc, led by the Social Democrats, nor the centre-right, led by the Moderates, are in control of a majority of seats in the Riksdag. Imagine that neutral colour straddling a 50% benchmark line or sitting in the middle of the seats. It makes it far clearer just how pivotal the Sweden Democrats would usually be. Because, usually, Sweden Democrats or parties like it—in the sense of it won a large number of seats—that help the main coalition cross that 50% threshold would have an enormous sway in the next governing coalition. But here, the Sweden Democrats are an anti-immigrant, nationalist party that both the centre-left and centre-right have said with whom they will not enter talks.
But graphically, the thing I always find lacking in charts like those above are just how dramatic the rise of the Sweden Democrats has been. And so for that, we have this little piece of mine that complements the two. Because not all members of the coalitions experienced the declines of their major parties, the Social Democrats and the Moderates. In fact, with the exception of the Green Party, all others rose or, in the case of the Liberals, stayed flat. A more thorough defeat would have probably seen the whole of the coalition falling in the number of seats. Unfortunately for Sweden, in this case, the nationalists took the lion share of the seats lost by the top two parties.
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.
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.
This past weekend I cited this article from the Economist that looked at the rise of online dating as a way of couples meeting. There was some debate about which channels of interaction/attraction still worked or were prevalent. And it turns out that, in general, the online world is the world today.
My problem with the graphic is that it is a bit too spaghettified for my liking. Too many lines, too many colours, and they are all overlapping. I probably would have tried a few different tricks. One, small multiples. The drawback to that method is that while it allows you to clearly analyse one particular series, you lose the overlap that might be of some interest to readers.
Second, maybe don’t highlight every single channel? Again, you could lose some audience interest, but it would allow the reader to more clearly see the online trend, especially in the heterosexual couple section of the data. You could accomplish this by either greying out uninteresting lines or removing them entirely, like that primary/secondary school series.
Third, I would try a bit more consistent labelling. Maybe increase the overall height of the graphic to give some more vertical space to try and label each series to the right or left of the graphic. You might need a line here or there to connect the series to its label, but that is already happening in this chart.
However, I do like how the designers kept the y-axis scale the same for both charts. It allows you to clearly see how much of an impact the online dating world has been for homosexual couples. My back-of-the-envelope calculations would say that is more than three times as successful than it is for heterosexual couples. But that insight would be lost if both charts were plotted on separate axis scales.
But lastly, note how the dataset only goes as far as 2010. I can only imagine how these charts would look if the data continued through 2018.
Credit for the piece goes to the Economist Data Team.