Next week I am heading west. And by west I mean Austin, Texas. I mean you could argue that Austin is more south than west, but if you throw a “×” in there you get South × Southwest. Anyway, the allure of the western remains strong and that reminded me of an old xkcd piece reflecting on the relative length of the western period vs. the “west” in American culture.
It’s kind of like how M*A*S*H lasted far longer than the actual Korean War.
Credit for the piece goes to Randall Munroe.
Yesterday was the first day of 32º+C (90º+F) in Philadelphia in October in 78 years. Gross. But it made me remember this piece last month from NPR that looked at the correlation between extreme urban heat islands and areas of urban poverty. In addition to the narrative—well worth the read—the piece makes use of choropleths for various US cities to explore said relationship.
As graphics go, these are effective. I don’t love the pure gradient from minimum to maximum, however, my bigger point is about the use of the choropleth compared to perhaps a scatter plot. In these graphics that are trying to show a correlation between impoverished districts and extreme heat, I wonder if a more technical scatterplot showing correlation would be effective.
Another approach could be to map the actual strength of the correlation. What if the designers had created a metric or value to capture the average relationship between income and heat. In that case, each neighbourhood could be mapped as how far above or below that value they are. Because here, the user is forced to mentally transpose the one map atop the other, which is not easy.
For those of you from Chicago, that city is rated as weak or no correlation to the moderately correlated Philadelphia.
Granted, that kind of scatterplot probably requires more explanation, and the user cannot quickly find their local neighbourhood, but the graphics could show the correlation more clearly that way.
Finally, it goes almost without saying that I do not love the red/green colour palette. I would have preferred a more colour-blind friendly red/blue or green/purple. Ultimately though, a clearer top label would obviate the need for any colour differentiation at all. The same colour could be used for each metric since they never directly interact.
Overall this is a strong piece and speaks to an important topic. But the graphics could be a wee bit more effective with just a few tweaks.
Credit for the piece goes to Meg Anderson and Sean McMinn.
This week is the Conservative Party Conference in Manchester where the Tories unveil their government programmes and platforms. Naturally it has been overshadowed by allegations that Boris Johnson groped one (maybe two) reporters at a dinner in 1999. Just prior to that scandal, however, there was another. In this, Johnson was allegedly having an affair with an American businesswoman for whom he then arranged lucrative business deals whilst in office as the Mayor of London. Johnson has been referred to a police unit for further investigation in that matter. Sounds like some, you might say, golden parallels to…someone or something.
But today’s big news about the government’s plans is that they might have one regarding Brexit. And that plan is to essentially create a hard border between Northern Ireland and Ireland, violating certainly the spirit if not the letter of the Good Friday Agreement that brought the end to the Troubles.
Why is this a big deal? Well, one, remember all the debates during the Brexit Referendum campaign about the Irish border, how the different groups had different solutions to this fundamental problem?
Oh wait, yeah, nobody ever brought this up. Sorry.
So back to my trilemma graphic. I’ve updated it to show which two sides of the triangle Boris Johnson seems to be choosing. To be fair, as I’ve said many times, the UK cannot have all three points of the triangle. They need to pick two. And so, unlike Theresa May, Johnson is at least picking two. My problem is that this was never discussed during the Brexit debates and it seems a rather drastic decision to not have it be confirmed by the people since they never explicitly voted on it.
Credit for the graphic is mine.
Last week was the climate summit in New York, and the science continues to get worse. Any real substantive progress in fighting climate change will require sacrifices and changes to the way our societies function and are organised, including spatially. Because one area that needs to be addressed is the use of personal automobiles that consume oil and emit, among other things, carbon dioxide. But living without cars is not easy in a society largely designed where they are a necessity.
But over at CityLab, Richard Florida and Charlotta Mellander created an index trying to capture the ability to live without a car. The overall piece is worth a read, but as usual I want to focus on the graphic.
It’s nothing crazy, but it really does shine as a good example of when to use a map. First, I enjoy seeing metro maps of the United States used as choropleths, which is why I’ve made them as part of job at the Philly Fed. CityLab’s map does a good job showing there is a geographic pattern to the location of cities best situated for those trying to live a car-free life. Perhaps not surprisingly, one of the big clusters is the Northeast Corridor, including Philadelphia, which ranks as the 17th best (out of 398) and the 7th best of large metro areas (defined as more than one million people), beating out Chicago, ranked 23rd and 8th, respectively.
Design wise I have two small issues. First, I might quibble with the colour scheme. I’m not sure there is enough differentiation between the pink and light orange. A very light orange could have perhaps been a better choice. Though I am sympathetic to the need to keep that lowest bin separate from the grey.
Secondly, with the legend, because the index is a construct, I might have included some secondary labelling to help the reader understand what the numbers mean. Perhaps an arrow and some text saying something like “Easier car-free living”. Once you have read the text, it makes sense. However, viewing the graphic in isolation might not be as clear as it could be with that labelling.
Credit for the piece goes to David Montgomery.
The week of the climate summit in New York and the revelation of the whistleblower complaint against the president seems to make it an ideal Friday to share this graphic from Wednesday by Jessica Hagy at Indexed.
So it’s less of a graphic and more an if then statement. But if the average designer codes the occasional if then statement, then the graphic is alright for Coffeespoons.
Credit for the piece goes to Jessica Hagy.
The British Supreme Court ruled today that Boris Johnson unlawfully advised the Queen to prorogue Parliament. And because the advice was unlawful, the act was therefore unlawful. And because the act was unlawful, the effects of said act were unlawful. And because the effects were unlawful, said effects are null and void. So, you know, prorogation never happened.
So the Prime Minister has misled the Queen. He has failed to pass all but one bill in Parliament (it was a bill for the restoration of the Palace of Westminster totally unrelated to Brexit). He lost three seats, one via a by-election and two by defecting MPs. And then he purged 21 MPs from his party to completely obliterate his working majority. In any other year, this would be cause for the immediate resignation of the Prime Minister. Instead he is sticking around in New York to give a speech about, what else, Brexit, before flying back to London tonight (Eastern US time).
So what’s next? Who really knows. This has never before happened in the history of the United Kingdom. But one possible option is that the opposition parties may hold a no confidence vote. But there will be significant pressure against that, because, as my graphic shows, any election that would likely result, would mean Brexit happening with Parliament dissolved. And that would, ahem, defeat the entire purpose of preventing a No Deal Brexit. Consequently, a no confidence vote or general election is unlikely. (Unless, the opposition and Tory rebels can agree to a non-Jeremy Corbyn caretake prime minister, e.g. Ken Clarke or Margaret Beckett.)
Regardless, get ready for a crazy day of Parliamentary procedure tomorrow.
The UN climate summit begins in New York today. So let’s take a look at another data visualisation piece exploring climate change data. This one comes from a Washington Post article that, while largely driven by a textual narrative, does make use of some nice maps.
There is nothing too crazy going on with the actual map itself. I like the subtle use here of a stepped gradient for the legend. This allows for a clearer differentiation between adjacent regions and just how, well, bad things have become.
But where the piece shines is about halfway through. It takes this same map and essentially filters it. It starts with those regions with temperature changes over 2ºC. Then it progressively adds slightly less hotter regions to the map.
It’s a nice use of scrolling and filtering to highlight the areas worst impacted and then move down the horrible impact scale. And because this happens in the middle of the piece, giving it the full column width (online) allows the reader to really focus on the impacts.
Credit for the piece goes to Chris Mooney and John Muyskens.
Though the temperatures might not always feel it, at least in Philadelphia, summer is ending and autumn beginning. Consequently I wanted to share this neat little work that explores urban heat islands. Specifically, this post’s author looks at Massachusetts and starts with a screenshot of the Boston area.
The author points out that the Boston Common and Public Garden are two areas of cool in an otherwise hot Boston. He also points out the Charles River and the divide between Boston and Brookline. I would like to add to it and point out the Fens and the Emerald Necklace.
I wonder if a scale of sorts would help, though the shift from warm yellows and reds to cooler greens and blues certainly helps differentiate between the cooler and warmer areas.
Credit for the piece goes to Krishna Karra.
I mean come on, guys, did you really expect me to not touch this one?
Well we made it to Friday, and naturally in the not so serious we have to cover the sharpie map. Because, if the data does not agree with your opinions, clearly the correct response is to just make shit up.
By now you have probably all heard the story about how President Trump tweeted an incorrect forecast about the path of Hurricane Dorian, warning how Alabama could be “hit (much) harder than anticipated”. Except that the forecast at the time was that Alabama wasn’t going to be hit. Cue this map, days later. As in days. As in this news story continued for days.
So to be fair, I went to the NOAA website and pulled down from their archive the cone maps from the date of the graphic Trump edited, and the one from the day when he tweeted about Alabama being hit by the hurricane.
Important to note that this forecast dates from 29 August. This press conference was on 4 September. He tweeted on 1 September. So in other words, two days after he used the wrong forecast, he had printed a big version of a contemporaneously two-day old forecast to show that if he drew a sharpie line on it, it would be correct.
Here is the original, from the National Hurricane Centre, for 29 August. Note, no Alabama.
And then Trump tweeted on 1 September. So let’s take the 02.00 Eastern time 1 September forecast from NOAA.
Definitely no Alabama in that forecast.
This could have all gone away if he had just admitted he looked at the wrong forecast and tweeted an incorrect warning. Instead, we had the White House pressuring NOAA to “fix” their tweet.
Now we can all chalk this up as funny. But it does have some serious consequences. Instead of people in the actual path of Dorian preparing, because of the falsely wide range of impacts the president suggested, people in Alabama needlessly prepared for a nonevent.
But more widely, as someone who works with data on a daily basis, we need to agree that data is real. We cannot simply change the data because it does not fit the story we want to tell. If I could take a screenshot of every forecast and string them together in an animated clip, you would see there was never any forecast like the sharpie forecast. We cannot simply create our own realities and choose to live within them, because that means we have no common basis on which to disagree policy decisions that will have real world impacts.
Credit for the photo goes to Evan Vucci of the AP.
Credit for the National Hurricane Centre maps goes to its graphic team.
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?
Credit for this piece goes to me.
