This afternoon I am off on a flight to Austin, Texas for a friend’s wedding in nearby Kyle, Texas. Two years in a row I’ve been to Texas in October. And so that felt like a good enough reason to update my counties visited map that, according to my files, I haven’t updated since 2015.
In those four years, before I moved from Chicago to Philadelphia, I explored Wisconsin for genealogy purposes. Then after said move, I have visited Las Vegas for bachelor party—now the furthest west in the United States I have ever visited. And work trips have sent me to St. Louis and Dallas, the former of which allowed me a nice train ride from St. Louis to Chicago across central Illinois. I have also done some genealogy research up in western New York bookending a bachelor party to the Finger Lakes.
I’m clearly an East Coast guy with a decent representation of the Midwest.
At a state level that makes 23 states visited plus two through which I’ve travelled (Connecticut and Rhode Island). Plus I’ve visited DC. Almost halfway there to visiting half the United States.
With the wedding Saturday, I am on holiday Friday. Plus, Monday is a bank holiday and so I will be posting again from Tuesday.
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.
As many of you are aware, one of my personal interests is in genealogy and my family history. And sometimes, data visualisation can help make sense of my research. This past weekend, I was looking through some of my notes on my great-great-great-great-grandfather, a man named Stephen Remington.
One of the outstanding questions is who was his wife, a woman named Eliza Ann. Her surname might be either Garretson or Caustin. So I used a timeline of Stephen’s residences to see if any his residences overlapped with similar surnames. It sort of did, but not until after the year he married her. So still more work is needed.
But then I decided with a few tweaks I could actually plot out where he lived, because he lived all over. His earliest years are a bit of a mystery, because his parents are both unknown and they both died during Stephen’s youth.
Ridgefield was home to a small cluster of Remingtons. Were they related?
In his earlier years he was what was called a circuit rider. Before there were large, dense settlements of people, the rural and frontier people relied upon essentially travelling ministers. The ministers had a responsibility for a small (sometimes large) area. And early in Stephen’s life his circuit riding kept pushing him north up the Hudson River with occasional postings back to New York City.
Rhinebeck is the town demanding my closer attention for Eliza’s sake.
Eventually, however, he ended up preaching in Massachusetts, where he separately earned his medical doctorate from Harvard University. He practiced medicine on the side for years. Then in 1846 he converted from the Methodist church to the Baptist church. He wrote about it in a notable book/pamphlet: Reasons for Becoming a Baptist.
From then he became an itinerant pastor, never staying at a single congregation for more than five years or so. He travelled from New York to Philadelphia to Louisville for several months then back to New York.
Evidently his time in Louisville was short, possibly because of anti-slavery views.
He preached as a Baptist for twenty-plus more years before finally settling in Brooklyn, where he died at the age of 66. He lived all over the mid-Atlantic, especially the Hudson River Valley. And while he returned to places over the years, notably New York City, he appears to have never stayed in one place longer than maybe five years.
That was a lot of places for Stephen to hang his hat.
As for Eliza, she died in 1850. But I wonder if she may be related to a cluster of Garretsons that lived in Rhinebeck, which included the famous Reverend Freeborn Garretson, a circuit riding Methodist minister.
The daughter born in Hartford is my direct ancestor. She eventually married a man in New York City with the surname Miller. Then, after having a son (my next direct ancestor), she upped and moved to Wisconsin and married another man with the surname Miller, who was not related to the first. There is talk of a divorce, but no record of it. Could she have been a bigamist? That’s a story for another day.
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.
My neighbourhood’s not bad, but thankfully I live next to a park.
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.
I lived near the lake for eight years, and that does a great deal for mitigating temperature extremes.
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.
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.
The Northeast is where it’s at with its dense cities designed for a pre-automobile era
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.
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.
Ugh.
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.
I mean at least it could be worse?
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.
Wicked hot
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.
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.
Note the sharpie weirdly extending the cone (in black, not the usual white) into Florida and onward into Alabama.
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.
No Alabama in this forecast, the OG, if you will (and if I’m using that term correctly).
And then Trump tweeted on 1 September. So let’s take the 02.00 Eastern time 1 September forecast from NOAA.
By 30 August the forecast was already tracking northward, not westward. So by 1 September the idea that the hurricane would hit Alabama was just nonsense.
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.
No real clear pattern here
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?
Five years ago, I covered the Russian invasion of Ukraine a littletinybit. Five years on and Russia has formally annexed Crimea and Russian “patriotic volunteers” continue to destabilise the Donbass. About two weeks ago, this article from the BBC caught my eye as it recounted the story of Ukraine’s deadliest day in the conflict. Initially I read it simply because I have long been fascinated by that undeclared war.
Since at least high school, but probably most definitely earlier, I have long been interested in military history. And I distinctly recall being awestruck by maps depicting the bombing of Pearl Harbour, or the Roman defeat at Cannae, or the Battle of Waterloo.
So I loved scrolling through the article and finding this graphic.
A long and bloody road
It’s a fairly simple map, showing the alignment of forces. It’s not quite a tactical map showing unit size/formations, but it does show the Ukrainian forces essentially surrounded. And how their retreat brought them through essentially a shooting gallery of Russian artillery.
Credit for the piece goes to the BBC graphics department.
