On Friday, Pennsylvania reported its first death from the vaping disease spreading across the country. So I decided I would take a moment to update the map I made a month ago charting the outbreak. Then, the CDC had tallied 450 cases. Now we are at 1080. And whereas last time New England, parts of the deep South, and the Southwest were untouched, now the disease is everywhere but New Hampshire and Alaska.
But we are starting to see a pattern in a clustering of high numbers of cases around Lake Michigan and the Upper Midwest. Though I should point out these bin breakdowns come from the CDC. They did not provide more granular data.
Yesterday President Trump announced that the FDA is seeking to implement a ban on flavoured e-cigarettes. Ostensibly this is to combat teen uptake on the habit, but it comes at the same time as an outbreak of respiratory illnesses seemingly linked to vaping. Though, it should be pointed out that preliminary data points to a link to cannabis-infused vaping liquids, not necessarily cigarettes.
Regardless, the day before yesterday, I want to the CDC website to get the data on the outbreak to see if there was a geographic pattern to the outbreak. And, no, not really.
The closest thing that I could argue is the Eastern Seaboard south of New England. But then the deaths are all from the Midwest and westward. So no, in this graphic, there really is no story. I guess you could also say it’s more widespread than not?
In case you did not hear, earlier this week Alabama banned all abortions. And for once, we do not have to add the usual caveat of “except in cases of rape or incest”. In Alabama, even in cases of rape and incest, women will not have the option of having an abortion.
And in Georgia, legislators are debating a bill that will not only strictly limit women’s rights to have an abortion, but will leave them, among other things, liable for criminal charges for travelling out of state to have an abortion.
Consequently, the New York Times created a piece that explores the different abortion bans on a state-by-state basis. It includes several nice graphics including what we increasingly at work called a box map. The map sits above the article and introduces the subject direct from the header that seven states have introduced significant legislation this year. The map highlights those seven states.
The gem, however, is a timeline of sorts that shows when states ban abortion based on how long since a woman’s last period.
It does a nice job of segmenting the number of weeks into not trimesters and highlighting the first, which traditionally had been the lower limit for conservative states. It also uses a nice yellow overlay to indicate the traditional limits determined by the Roe v. Wade decision. I may have introduced a nice thin rule to even further segment the first trimester into the first six week period.
We also have a nice calendar-like small multiple series showing states that have introduced but not passed, passed but vetoed, passed, and pending legislation with the intention of completely banning abortion and also completely banning it after six weeks.
This does a nice job of using the coloured boxes to show the states have passed legislation. However, the grey coloured boxes seem a bit disingenuous in that they still represent a topically significant number: states that have introduced legislation. It almost seems as if the grey should be all 50 states, like in the box map, and that these states should be in some different colour. Because the eight or 15 in the 2019 column are a small percentage of all 50 states, but they could—and likely will—have an oversized impact on women’s rights in the year to come.
That said, it is a solid graphic overall. And taken together the piece overall does a nice job of showing just how restrictive these new pieces of legislation truly are. And how geographically limited in scope they are. Notably, some states people might not associate with seemingly draconian laws are found in surprising places: Pennsylvania, Illinois, Maryland, and New York. But that last point would be best illustrated by another box map.
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.
Tomorrow is Election Day here in the United States and this morning I wanted to look at a piece I’ve had in mind on doing from City Lab. I held off because it looks at the election and what better time to do it than right before the election.
Specifically, the article looks at the density of the different congressional districts across the United States. Whilst education level appears to be the most predictive attribute of today’s political climate—broadly speaking those with higher levels of formal education support the Democrats and those with lower or without tend to support President Trump—the growing urban–rural divide also works. But what about the in-between? The suburbs? The exurbs? And how do we then classify the congressional districts that include those lands.
For that purpose City Lab created its City Lab Congressional Density Index. Very simplistically it scores districts based on their mixture of low- to medium- to high-density neighbourhoods. But visually, which is where this blog is concerned, we get maps with six bins from pure urban to pure rural and all the mixtures in-between. This cartogram will show you.
Now, there are a couple of things I probably would have done differently in terms of the visualisation. But the more I look at this, one of those things would not be to design the hexagons to all fit together nicely. Instead, you get this giant gap right where the plains states begin west of the Mississippi River stretching through the Rockies over to California. If you think about it, however, that is a fairly accurate description of the population distribution of the United States. With a few exceptions, e.g. Denver, there are not many people living in that space. Four geographically enormous states—North Dakota, South Dakota, Montana, and Wyoming—have only one congressional district. Idaho has two. Nebraska three. And then Iowa and Kansas four. So why shouldn’t a map of the United States display the plains and Rocky Mountain interior as a giant people hole?
Like I said, initially I took umbrage at that design decision, but the more I thought about it, the more it made sense. But there are a few others with which I quibble. The labelling here is a big one. First, we have the district labels. They are small, because they have to be to fit within the five hexagons that define the districts’ shapes. But every label is black. Unfortunately, that makes it difficult to read the labels on the darker colours, most notably the dark purple. I probably would have switched out the black labels in those instances for white ones.
But then the state labels are white with black outlines, which makes it difficult to read on either dark or light backgrounds. The designer made the right decision in making the labels larger than the districts, but they need to be legible. For example, the labels of Alaska and Hawaii need not be white with black outlines. They could just be set in black type to be legible. Conversely, Florida’s, sitting atop darker purple districts, could be made white.
The piece makes use of more standard geographic map divided into congressional districts—the type you will see a lot tomorrow night. And it makes use of bar charts to describe the demographics of the various density types. I like the decision there to use a new colour to fill in the bars. They use a dark green because it can cut across each of the six types.
Wow do we have a lot to talk about this week. Probably bleeding into next week to be honest. But, last night was the special election for the Georgia 6th.
For those of you not following politics, the congressman representing it was Tom Price; he is now the Secretary of Health and Human Services. Consequently, Georgia needed to elect a fill-in for the Atlanta-suburbs district. That election was between 18 candidates last night. The race could have been won outright, but it would have required a vote total over 50%.
That did not happen—and realistically with 18 people running was not likely. But, Democrats hoped they could get their candidate in at 50+%.
This screenshot is from a nice piece by the New York Times. As you all know by now, I am not a huge fan of choropleth maps. They distort geographic area and population. But, I like the arrangement of these small multiples. It does a nice job of comparing the results for the five major candidates. I particularly like the addition of the 2016 presidential election result. With the cratering poll approvals of Donald Trump, could some of the paler red precincts flip in June?
The above screenshot comes from BuzzFeed, whose coverage I followed via live streaming last night. They used a cartogrammic approach, assuming that cartogrammic is actually a word. The colours could use a bit more sophistication—the best example being the Democratic–Republican margin map where the blues are darker than the reds and have a hopefully unintended greater visual weight.
Sorry about last week, everyone. I had some trouble with the database powering the blog here. Great week for things to go down, right? Well, either way, we’re back and it’s not like the news is stopping. This week? Brexit’s back, baby.
I’m never using the word “baby” again on this blog.
I have been saving this piece until the announcement of Article 50 by the UK government. I know the British and Europeans among my audience likely know what that means, but for the rest of you, Article 50 is the formal mechanism by which the United Kingdom starts the two-year process to leave the European Union.
Think of it like signing the divorce papers, except that the divorce isn’t unofficial for two years until after that date. The interim period is figuring out who gets which automobile, the dinnerware, and that ratty-old sofa in the basement. Except that instead of between two people, this divorce is more like a divorce between polygamists with multiples husbands and wives. So yeah, not really like a divorce at all.
This piece from the Guardian attempts to explain what the various parties want from the United Kingdom and from the eventual settlement between the UK and the EU. It leads off with a nice graphic about the importance of a few key issues in a cartogram. And then several voting blocs run down the remainder of the page with their key issues.
I really like this piece as the small multiples for each section refer back to the opening graphic. But then on a narrow window, e.g. your mobile phone, the small multiples drop off, because really, the location of the few countries mentioned on a cartogram is not terribly important to that part of the analysis. It shows some great understanding of content prioritisation within an article. In a super ideal world, the opener graphic would be interactive so the user could tap the various squares and see the priorities immediately.
But overall, a very solid piece from the Guardian.
Credit for the piece goes to the Guardian’s graphics department.
One of the big news stories yesterday centred on the Trump administration’s budget outline that would expand US defence spending by 9%, or $54 billion. That is quite a lot of money. More worrying, however, was the draft’s directive that it be accompanied by equal spending cuts in neither security nor entitlement programmes like Social Security and Medicare. Nor, obviously, the trillions allocated for mandatory spending, e.g. debt repayment.
White House officials—worth noting of the Trump-despised anonymous type that I suppose that only matters if reporting unflattering news—declined to get into specifics, but pointed out foreign aid as an area likely to receive massive cuts.
Problem is, foreign aid is one of the smallest segments of the federal budget. How small? Well, let’s segue into today’s post—see how smooth that was—from the Washington Post. The article dates from October, but was just brought to my attention to one of my mates.
Beyond this graphic that leads the piece, the Post presents numerous cartograms and other graphics that detail spending patterns. Hint, there is a pattern. But those patterns could also make it difficult to slash said spending.
The reason foreign aid spending is important is that it ties nicely into that concept of soft power. No surprise that over 120 retired generals and admirals told Congress that spending on diplomacy and foreign aid is “critical to keeping America safe”.
But for now this remains a budget outline sent to federal agencies to review. The actual budget fight is yet to come. So I’m sure this won’t be the last time we look at this topic here on Coffeespoons.
Credit for the piece goes to Max Bearak and Lazaro Gamio.
It just won’t die. Grandma, that is, in front of the death panels of Obamacare. Remember those? Well, even if you don’t, the Affordable Care Act (the actual name for Obamacare) is still around despite repeated attempts to repeal it. So in this piece from Bloomberg, Obamacare is examined from the perspective of leaving 27 million people uninsured. In 2010, there were 47 million Americans without insurance and so the programme worked for 20 million people. But what about those remaining 27?
I am not usually a fan of tree maps, because it is difficult to compare areas. However, in this piece the designers chose to animate each section of the tree as they move along their story. And because the data set remains consistent, e.g. the element of the 20 million who gained insurance, the graphic becomes a familiar part of the article and serves as a branching off point—see what I did there?—to explore different slices of the data.
So in the end, this becomes one of those cases where I actually think the tree map worked to great effect. Now there is a cartogram in the article, that I am less sure about. It uses squares within squares to represent the number of uninsured and ineligible for assistance as a share of the total uninsured.
Some of the visible patterns come from states that refused to expand Medicaid. It was supposed to cover the poorest, but the Supreme Court ruled it was optional not mandatory and 19 states refused to expand the coverage. But surely that could have been done in a clearer fashion than the map?
Credit for the piece goes to Jeremy Scott Diamond, Zachary Tracer, and Chloe Whiteaker.