Like many Americans I closely followed the outcome of yesterday’s historic vote by the House of Representatives to impeach President Trump for his incitement of an insurrection at the US Capitol in a failed coup attempt to overturn the 2020 election.
Words I still never thought I’d write describing an American election.
So at the end of the vote, I created this first graphic to capture the bipartisan nature of the impeachment. Ten Republicans broke ranks and voted with the Democrats. Keep in mind that in 2020, zero Republicans did the same. Justin Amash had by then resigned from the Republican Party and sat as an independent.
But I was also interested in how “courageous” these votes could be seen. Trump remains immensely popular with his base despite his attempt to overthrow the US government and keep himself in power. Did the Republicans who supported impeachment sit in districts won by Biden?
The answer? Not really. Two did: congressmen from New York and California. But a look at the other eight reveals they represent Trump-supporting districts.
To be fair, there are probably three tiers of seats in that group. Liz Cheney, the No. 3 Republican in the House, is in her own Trump-supporting seat as Wyoming’s at large representative. But four other Republicans have seats where Trump won by more than 10 points.
Three more Republicans are in seats I’d label competitive, but lean Republican.
Clearly the argument can be made that for most of these Republicans, it was not a politically safe choice to vote for impeachment. House seats will be redistricted this year for the 2022 midterms, but I’ll be curious to see how these Republicans fare in those redistricting proceedings and then in the ultimate elections thereafter.
I meant to publish this yesterday, but this piece also offers a reminder that the hardest part of a data-driven story is usually finding the data. I was unable to find a single source of data for all the numbers I needed by the time I switched on for work. And so this had to wait until last night when I found what I needed.
And of course upon waking up this morning I found a few new articles with the data and more recent figures.
Since 2016, Trump has made building a great, big, beautiful wall on the US-Mexican border his signature policy. Of course, most illegal immigrants cross the border legally at checkpoints and normal ports of entry. A significant number are people who overstay the limits on their visas. So the efficacy of a great, big, beautiful wall is really not that great.
He also claimed that he would make Mexico pay for it.
So as he prepares to leave office, Trump this week is going on something of a victory tour and touting up his administration’s successes. The first stop? Alamo, Texas to highlight his wall.
Let’s look at that wall and how much the administration has accomplished.
For context, the US border with Mexico is nearly 2000 miles long. As of 18 December, the administration had built 452 miles, less than a quarter of the border’s total length.
Crucially, most of that construction merely replaced sections of existing wall and fence scheduled for replacement. The total amount of new wall built, as of 18 December, totals about 40 miles.
The cost of that 452 miles? More than $15 billion.
The last time we checked in on Covid-19 in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois, things were peaking across the five states. As I said then:
If you look at the very tippy tip top of the curves in the other four states, we might just be seeing an inflection point.
And in the month since, my highly scientific term of “tippy point” appears to have been correct. New cases did begin to drop and by the start of the Christmas holiday we began to see real improvement. I should point out that deaths continued to rise, however, but we should expect that because deaths lag new cases by sometimes as many as four to six weeks.
So how are things now, a month hence?
Well as you can see with new cases, not great and getting worse. Pennsylvania, New Jersey, Delaware, and Illinois all bottomed out prior to the holidays, and since then have been rising. It speaks to a surge in new cases likely caused by gatherings centred on the holidays.
The good news—if you can call it that—is that in Pennsylvania and Illinois, whilst cases rebound, they have not yet reached their mid-December peak in Pennsylvania and mid-November peak in Illinois. It’s worth pointing out that Chicago and separately Illinois instituted lockdowns earlier than the other four states prior to the holidays. That may account for the more dramatic reduction in those states.
The bad news is that in New Jersey and Delaware, the rebounds have now surpassed the peaks we saw in mid-December and cases continue to climb with new daily records pointing towards escalation of new cases in those states.
But the really bad news is in Virginia, where the inflection point was there—note the little mini “W” at the top of the chart—but that new cases declined neither significantly long nor in significant numbers such that there was no real holiday decline. Instead, at best we could describe it as numbers paused for two weeks before resuming their upward trends.
How about deaths?
Again, fairly grim news here. A month ago we were talking about rising rates of deaths in all but Illinois. And in fact, Illinois is the only state where the death rate is significantly lower than what it was in mid-December.
In New Jersey and Virginia, we see two states where the rising death rate perhaps slowed, but it never really entered into decline. Pennsylvania and Delaware offer perhaps static death rates. In fact, Pennsylvania just yesterday surpassed its mid-December peak level.
But keep in mind that deaths lag new cases by somewhere between two to four weeks, sometimes longer. What this means is that with new cases now rebounding and in fact surpassing their peaks from a month ago, we can expect that the end of January and beginning of February could be particularly deadly.
The situation is dire in the United States and things are going to get worse before they get better.
Yesterday was maybe the last election day for the 2020 US General Election. (There are still a few US House seats yet to be called, most notably a contested race in upstate New York.) These were a pair of runoff elections in Georgia for the state’s two US Senate seats (one for a full, six-year term, the other to finish out the final two years of a retiring senator).
I spent most of the night eating pizza and tracking results. One thing that I keep tabs on (in the sense of open tabs in the browser) is the New York Times needle forecast. It has its problems, but I wanted to highlight something I think was new last night. Or, if it wasn’t, I didn’t notice it back in November.
Below the needle was a simple table of results.
In the past, the needle was a bit opaque and it consumed data and spat out forecasts without users having a sense of what was driving those forecasts. Back in November, there were a few instances where states published incorrect data—that they later fixed—and when the needle consumed it, the needle forecast incorrect results.
But now we have a clear record of what data the forecast consumed in the table below the needles. It’s fairly straightforward as tables go. But tables don’t have to be sexy to be clear and effective.
The table lists the time when the data was added, the number of votes added, the type of vote added, and then the actual data vs. what was expected. And ultimately how that changed the needle. This goes a long way towards data transparency.
Simple colour use, bright blues and reds, show when the result/data favoured the Republican or Democrat. Thin, light strokes instead of heavy black lines for rows and columns place the visual emphasis on the data. And smaller type for the timestamp places the less important data at a lower level of importance.
It’s just very well done.
Credit for the piece goes to Michael Andre, Aliza Aufrichtig, Matthew Bloch, Andrew Chavez, Nate Cohn, Matthew Conlen, Annie Daniel, Asmaa Elkeurti, Andrew Fischer, Will Houp, Josh Katz, Aaron Krolik, Jasmine C. Lee, Rebecca Lieberman, Jaymin Patel, Charlie Smart, Ben Smithgall, Umi Syam, Miles Watkins and Isaac White.
The holiday break is over as your author has burned up all his remaining time for 2020 and so now we’re back to work. And that means attempting to return to a more frequent and regular posting schedule for Coffeespoons.
I wanted to start with the death of Diego Maradona, a legendary Argentinian footballer. He died in December of a heart attack and left behind a complicated inheritance situation. To help explain the situation, the BBC created what in genealogy we call a descendancy chart. You typically use a descendancy chart to show the children, and sometimes grandchildren, of a person. (You can also attach people above the person of interest and show the person’s ancestral families.)
This is an example of a descendancy chart from my research into an unrelated family.
You can see Samuel Miller married Sabra Clark and had at least nine children with her. And I followed one of them, another Samuel, who married Elizabeth Woodruff and they had four children. In this version, you can also see Samuel the elder’s parents and siblings.
But Diego presents a complicated situation. He was married and had two children, then divorced. That’s not terribly uncommon. But he then went on to have potentially eight children with potentially five different women. (I say potentially because some of the claims are still working their way through the courts via paternity tests.)
The above type of chart works well with one couple. In my own family, I have at least one ancestor who had potentially two husbands (the second marriage has not yet been confirmed, but she definitely had children with two different men). And when we use this chart type to look at my ancestor’s descendants, you can see it becomes tricky.
Her children’s fathers can be placed to either side and then the children flow out from that. But whereas in the first chart we could see all nine children in one glance, Mary Remington had four and we only see two in this same view.
So how do you deal with one person who has six total relationships that have offspring?
The BBC opted for a vertical chart that uses colour to link the couples. Diego and his ex-wife receive a red line, and that link moves vertically down from Diego with the two daughters shown as descendants on the right.
Each subsequent relationship with offspring receives its own colour and continues to move vertically down the page, linking the mother on the left to the children on the right.
What I find interesting is the inconsistency within the chart, however. At the end, with the unidentified women, we have two instances of multiple children. Santiago Lara and Magali Gil, for example, descend from one stem. But note at the top how Diego’s two daughters Gianinna and Dalma each receive their own stem. Is there a reason for combining the two children from one unidentified mother into one branch?
And why the vertical format? You can see in my two examples, we are looking at a horizontal format. It works well when I am working on my desktop. The format is less useful on a mobile. I wonder if the BBC knows from their analytics that most people access their content like this via mobile phone and created a graphic that best uses that tall but narrow proportion. Because the proportions do not work well when the article is viewed on a desktop.
The vertical descendancy chart here is an intriguing solution to show descendants from multiple partners in a single mobile screen display. I am not sure how useful it would be as a new form, because I am not certain of how many times we would run into issues of children from six partners, but it could be worth exploring.
Credit for the images from my examples goes to the designers at Ancestry.com.
Credit for the BBC graphic goes to the graphics department of the BBC.
So as begin to head into winter, where are we at with the spread of Covid-19 in the five states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois?
Nowhere good. Let’s take a look.
If you recall where we were at last week, also not great but better, cases had resumed rising post-Thanksgiving across the board. The data from yesterday indicates that cases have continued to rise everywhere but Illinois, which initiated a lockdown earlier than the other states we cover.
But Philadelphia did eventually institute a lockdown and eventually the rest of the Commonwealth followed, and similar measures—none of course as significant as those from the spring—were enacted in other states.
If you look at the very tippy tip top of the curves in the other four states, we might just be seeing an inflection point. That is, the curve of new cases could be slowing from their near exponential rates of increase. The numbers released today we should expect to be lower than average. Consequently we will want to see the numbers beginning Tuesday through the end of the week to see whether this slowdown is real or a blip.
Regardless of whether or not new cases numbers are slowing down, we have to contend with rising numbers of deaths. Deaths of course lag new cases by weeks, sometimes as many as 4–8. So if we hypothetically hit peak new cases today, we would expect the number of deaths to continue rising and then peak perhaps sometime in mid- to late-January.
So where are we with deaths today? Also nowhere good. Let’s take a look.
In all five states with the potential exception of Illinois, new deaths continue to rise. Pennsylvania, worryingly, will likely surpass the peak death rate it saw in the spring if current trends continue. I would expect that sometime likely this week.
Illinois remains the one state where we might be seeing some good news. As I just mentioned above, deaths lag new cases by several weeks. And several weeks ago we appear to have peaked there in terms of new cases. It’s possible that we are beginning to or have already seen peak deaths in Illinois and that the next several weeks could be a gradual decline as the state gets its outbreak under control.
In the other four states, if we were to hypothetically peak with new cases this week, again, we would likely see these orange lines continue heading upwards for several weeks to come. And in that case, we’d almost certainly pass the peak death rates of the spring in Pennsylvania, Delaware, and Virginia. New Jersey might be the exception to that, however. And that would be largely due to the fact that so many deaths there happened so early in the pandemic before we had identified the best ways to save lives.
I suspect that the data coming out this week will be important to inform us whether or not we have crested or begun to crest this latest wave of infections.
It’s time for another Friday just for fun posting. I once worked with a guy who could draw a map of the United States or the world on a whiteboard incredibly accurately. He then left it in the break room for the office to try and label correctly.
This is kind of that, but in reverse, from xkcd. Good luck.
With the rollout of the first vaccination programme in the United Kingdom, the BBC had a helpful comparison table stating the differences between the four primary options. It’s a small piece, but as I often say, we don’t necessarily need large and complex graphics.
Since there are only four vaccines to compare and only a handful of metrics, a table makes a lot of sense.
But I wanted to take it a step further and so I threw together a quick piece that showed some of the key differences. In particular I wanted to focus on the effectiveness, storage temperatures (key to distribution in the developing world), and cost.
You can pretty quickly see why the United Kingdom’s vaccine developed by Oxford University and produced by AstraZeneca is so crucial to global efforts. The cost is a mere fraction of those of the other players and then for storage temperature, along with Russia’s Sputnik vaccine, it can be stored at common refrigerator temperatures. Both Pfizer’s and Moderna’s need to be kept chilled at temperatures beyond your common freezer.
And in terms of effectiveness, which is what we all really care about, they’re fairly similar, except for the Oxford University version. Oxford’s has an overall effectiveness of 70%. (In)famously, it exhibited a wide range of effectiveness during trials of between just over 60% and 90%.
The 60-odd% effectiveness was achieved when using the recommended dosage. However, in one small group of trial participants, they erroneously were given a half-dosage. And in that case, the dosage was found to be far more effective, approximately 90%. And this is why we would normally have longer, wider-ranging trials, to dial in things like doses. But, you know, pandemic and we’re trying to return to some sense of normalcy in a hurry.
All that said, Oxford’s will be crucial to the developing world, where incomes and government expenditures are lower and cold-storage infrastructure much less, well, developed. And we need to get this coronavirus under control globally, because if we don’t, the virus could persist in reservoirs, mutating for years until the right mutation comes along and the next pandemic sweeps across the globe.
I know we’re presently all fighting about wearing masks, but when we get to having vaccines available to the public, let’s really try to not make that a political issue.
I remember hearing and reading stories as a child about the Thames in London freezing over and hosting winter festivals. Of course most of that happened during what we call the Little Ice Age, a period of below average temperatures during the 15th through the early 19th century.
But those days are over.
The UK’s Meteorological Office, or the Met for short, released some analysis of the impacts of climate change to winter temperatures in the United Kingdom. And if, like me, you’re more partial to winter than summer, the news is…not great.
Broadly speaking, winters will become warmer and wetter, i.e. less snowy and more rainy. Meanwhile summers will become hotter and drier. Farewell, frost festivals.
But let’s talk about the graphic. Broadly, it works. We see two maps with a unidirectional stepped gradient of six bins. And most importantly those bins are consistent between the maps, allowing for the user to compare regions for the same temperatures: like for like.
But there are a couple of things I would probably do a bit differently. Let’s start with colour. And for once we’re not dealing with the colour of the BBC weather map. Instead, we have shades of blue for the data, but all sitting atop an even lighter blue that represents the waters around the UK and Ireland. I don’t think that blue is really necessary. A white background would allow for the warmest shade of blue, +4ºC, to be even lighter. That would allow greater contrast throughout the spectrum.
Secondly, note the use of think black lines to delineate the sub-national regions of the UK whilst the border of the Republic of Ireland is done in a light grey. What if that were reversed? If the political border between the UK and Ireland were black and the sub-national region borders were light grey—or white—we would see a greater contrast with less visual disruption. The use of lines lighter in intensity would allow the eye to better focus on the colours of the map.
Then we reach an interesting discussion about how to display the data. If the purpose of the map is to show “coldness”, this map does it just fine. For my American audience unfamiliar with Celsius, 4ºC is about 39ºF, many of you would definitely say that’s cold. (I wouldn’t, because like many of my readers, I spent eight winters in Chicago.)
The article touches upon the loss of snowy winters. And by and large, winters require temperatures below the freezing point, 0ºC. So what if the map used a bidirectional, divergent stepped gradient? Say temperatures above freezing were represented in shades of a different colour like red whilst below freezing remained in blue, what would happen? You could easily see which regions of the UK would have their lowest temperatures fail to fall below freezing.
Or another way of considering looking at the data is through the lens of absolute vs. change. This graphic compares the lowest annual temperature. But what if we instead had only one map? What if it coloured the UK by the change in temperature? Then you could see which regions are being the most (or least) impacted.
If the data were isolated to specific and discrete geographic units, you could take it a step further and then compare temperature change to the baseline temperatures and create a simple scatterplot for the various regions. You could create a plot showing cold areas getting warmer, and those remaining stable.
That said, this is still a really nice piece. Just a couple little tweaks could really improve it.