Asteroids on the Moon

I hope everybody enjoyed their holiday. But, before we dive back into the meatier topics of the news, I wanted to share this serpentine graphic from the Guardian I discovered last week. Functionally it is a timeline charting the size of 96 known large asteroid impact craters on the Moon, between 80ºS and 80ºN.

Impacts on the Moon
Impacts on the Moon

The biggest question I have is whether the wrapping layout is necessary. I would prefer a more simplistic and straightforward, well, straight timeline, but I can imagine space constraints forcing the graphic into this box—either for the digital version and/or the likely print version.

The transparencies help to give a sense of density to the strikes, especially in the later years. And the orange ones highlight important or well-known craters like Tycho.

I do wonder, however, if the designer could have added a line at the 290 million years point. Since the graphic’s title calls that year out in particular, it might help the audience more quickly grasp the graphic’s…impact. In theory, the reader can more or less figure it out from the highlighting of the Ohm impact crater that is listed as 291 million years old. But a small grey line like those for the 250 million year increments could have been a nice little touch.

Overall, however, it’s nice to see a compact and helpful space graphic.

Credit for the piece goes to the Guardian graphics team.

Border Arrests

We move from one manufactured crisis to another today as we look at a piece by the Economist on the number of illegal immigrants arrested at the US southern border. Lately, here in the United States we have been hearing of an invasion on our southern border. Illegal immigrants streaming across the border. Except, that is not true. In fact, illegal immigration is at or near its lowest rate in recent years.

Note how few there have been in recent years…
Note how few there have been in recent years…

The graphic does one thing really well and that is its unorthodox placement of the map. Instead of the usual orientation, here the designers chose to “tilt” the map so that the border segments roughly align with the sets of charts below them. I might have desaturated the map a little bit and cut off the gradient so Mexico does not bleed through underneath the bars, but the concept overall is really nice.

On the other hand, we have the bar charts arranged like funnels. This does allow the reader to see the slopes trending towards zero, however, it makes it incredibly difficult to see changes in smaller numbers. And without a scale on the axis, the reader has to take the bars and mentally transpose them on top of the grey bars in the bottom right corner. I wonder if a more traditional set of bar charts in small multiples could have worked better beneath the map.

Overall, however, I really do like this piece because of the way the map and the bar charts interact in their positioning.

Credit for the piece goes to the Economist Data Team.

The Brexit Deal Vote

Today’s (one of) the day(s). For those of you who haven’t followed Brexit, the British Parliament will vote this evening on whether to accept the deal Prime Minister Theresa May negotiated with the European Union…or not. And if not, well, the government now only has three—instead of the original 21—days to figure out a Plan B.

Of course this vote is only happening today because the government punted back in December when it was clear they were going to suffer a substantial loss. And back then, the BBC prepared this article about Brexit, where it was and where it was going. Funny thing is, after a month, not much has changed.

The screenshot below is of the process. As I noted above, the most critical change is that the government no longer has 21 business days to figure out what’s next. So instead of, to use the American football phrase, running out the clock, May will have to come up with something and present it to Parliament before 29 March, the day the UK leaves by statute.

How neat and orderly it must all seem…
How neat and orderly it must all seem…

I think the thing missing from the graphic is the chaos that happens if the deal is rejected. And while that may have been far from clearly the most obvious result two and a half years ago, it is now. And Parliament is scheduled to start voting around 19.00 GMT, or 14.00 EST for those of us on the East Coast or 13.00 CST for those of you in the Midwest.

Credit for the piece goes to the BBC graphics department.

The World Grows On and On

I mentioned this this time last year, but I used to make a lot of datagraphics about GDP growth. The format here has not changed and so there is nothing new to look at there. But, the content is still interesting. And the accompanying Economist article makes the point that high growth rates are not always what they seem. After all, Syria’s high growth rate is because its base is so small.

The 2019 GDP growth forecasts
The 2019 GDP growth forecasts

Credit for the piece goes to the Economist Data Team.

PECO Outages Five Years Ago

Christmas time is a time when people receive gifts. Well this year was no different and I received a few. One, however, was in a box stuffed with old newspaper pages. And it turns out one of said pages had a graphic on it. So let us spend today looking at this little blast from the past.

The piece looks at PECO outages, PECO being the Philadelphia region’s main electricity supplier. The article is full page and is both headed and footed with photography, the graphic in which we are interested sits centre stage in the middle of the page.

Full page design.
Full page design.

Overall the graphic is fairly compact and works well at showing the distribution of the outages, which the bar chart below the choropleth shows was historically significant. (Despite my years in Chicago, I was somehow in the area for all but the storm written about and can confirm that they were, in fact, disruptive.)

Ice storms suck.
Ice storms suck.

The choropleth works, but I question the colour scheme. The bins diverge at about 50%, which to my knowledge marks no special boundary other than “half”. If that yellow bin represented, say, the average number of outages per storm or the acceptable number of outages per storm, sure, I could buy it. Otherwise, this is really just degrees of severity along one particular axis. I would have either kept the bins all red or all blue and proceeded from a light of either to a dark of either.

I probably would have also dropped Philadelphia entirely from the map, but I can understand how it may be important to geographically anchor readers in the most populous county to orientate themselves to a story about suburbia.

Lastly, I have one data question. With power lines down during an ice storm, I would be curious to see less of the important roadways as the map depicts and other variables. What about things like average temperature during the storm? Was the more urban and built-up Delaware County less susceptible because of an urban heat bubble preventing water from freezing? Or what about trees? Does the impact in the more rural areas have anything to do with increasing numbers of trees as one heads away from the city?

Those last data questions were definitely out of scope for the graphic, but I nevertheless remain curious. But then again, this piece is almost five years old. Just a look at how some graphical forms remain in use because of their solid ability to communicate data. Long live the bar chart. Long live the choropleth.

Credit for the piece goes to the Philadelphia Inquirer graphics department.

More on California’s Dry Heat

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.

California isn't looking too…hot. Too soon?
California isn’t looking too…hot. Too soon?

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.

Dry Heat Is Only Part of California’s Problem

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 evolving California climate
The evolving California climate

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.

The Midterms Are Not Over

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.

The Republican gain might not be as big as they had hoped
The Republican gain might not be as big as they had hoped

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.

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.