Last week I wrote about how new cases in the five states we cover (Pennsylvania, New Jersey, Delaware, Virginia, and Illinois) were falling and falling rapidly. And this week that pattern continues to hold.
If we look at the Sunday-to-Sunday numbers, daily new cases were down in all five states. If we look at the seven-day averages, cases are down in all states. Pennsylvania and Illinois are now down below 2000 new cases per day, Virginia is just over 500 per day, New Jersey is below 400, and Delaware is over 100. These are all levels we last saw last autumn. In other words, we’re not quite back to summer levels of low transmission, but this time next month, I wouldn’t be surprised if we were.
Deaths remain stubbornly resistant to falling.
In fact, if we compare the Sunday-to-Sunday numbers we see that the numbers yesterday were largely the same as last Sunday, except in Pennsylvania where they were up significantly. The seven-day average?
Here’s where it gets interesting, because deaths are up slightly. Not by much, for example, Illinois was at 29.1 deaths per day last Sunday, this Sunday? 30.9. Illinois isn’t alone. Pennsylvania, Delaware, and Virginia all have reported slight upticks in their death rates.
But the biggest concern is the continuing slowdown in vaccinations. We’re perhaps halfway to the point of herd immunity in the three states we track. All three are between 37% and 38%. The thing to track this coming week will be if the rate continues to slow.
Last week, Ken Rosenthal of the Athletic wrote an article examining the recent spate of injuries in Major League Baseball. For those interested in the sport, the article is well worth the read. For the unfamiliar, baseball played only about 1/3 of the number of games as usual last year due to Covid-19. This year, pitcher after pitcher seems to be falling prey to arm troubles. Position players are straining hamstrings, quads, and other muscles I’ve never heard of let alone used over the last year. And joking aside, therein is thought to be the problem.
And the evidence, in part, shows that we are seeing an increase in the numbers of injuries. But 2020 may not be as much of a problem as youngsters throwing baseballs near 100 mph. But I digress. The article contained a table detailing the numbers of injuries for certain body parts in the first month (April) of the season in both 2021 and 2019, the last comparable season due to Covid-19.
To be fair, the table was nice, but in the exhaustion of post-second dose shot last weekend, I sketched out some things and decided to turn it into a proper post.
Every ten years the United States conducts a census of the entire population living within the United States. My genealogy self uses the federal census as the backbone of my research. But that’s not what it’s really there for. No, it exists to count the people to apportion representation at the federal level (among other reasons).
The founding fathers did not intend for the United States to be a true democracy. They feared the tyranny of mob rule as majority populations are capable of doing and so each level of the government served as a check on the other. The census-counted people elected their representatives for the House, but their senators were chosen by their respective state legislatures. But I digress, because this post is about a piece in the New York Times examining the new census apportionment results.
I received my copy of the Times two Tuesdays ago, so these are photos of the print piece instead of the digital, online editions. The paper landed at my front door with a nice cartogram above the fold.
Each state consists of squares, each representing one congressional district. This is the first place where I have an issue with the graphic, admittedly a minor one. First we need to look at the graphic’s header, “States That Will Gain or Los Seats in the Next Congress” and then look at the graphic. It’s unclear to me if the squares therefore represent the states today with their numbers of districts, or if we are looking at a reapportioned map. Up in Montana, I know that we are moving from one at-large seat to two seat, and so I can resolve that this is the new apportionment. But I am left wondering if a quick phrase or sentence that declares these represent the 2022 election apportionment and not those of this past decade would be clearer?
Or if you want a graphic treatment, you could have kept all the states grey, but used an unfilled square in those states, like Pennsylvania and Illinois, losing seats, and then a filled square in the states adding seats.
Inside the paper, the article continued and we had a few more graphics. The above graphic served as the foundation for a second graphic that charted the changing number of seats since 1910, when the number of seats was fixed.
I really like this graphic. My issue here is more with my mobile that took the picture. Some of these states appear quite light, and they are on the printed page. However, they are not quite as light as these photos make them out to be. That said, could they be darker? Probably. Even in print, the dark grey “no change” instances jump out instead of perhaps falling to the background.
The remaining few graphics are far more straightforward, one isn’t even a graphic technically.
First we have two maps.
Nothing particularly remarkable here. The colours make a lot of sense, with red representing Republicans and blue Democrats. Yellow represents independent commissions and grey is only one state, Pennsylvania, where the legislature is controlled by Republicans and the governorship by Democrats.
Finally we have a table with the raw numbers.
Tables are great for organising information. Do you have a state you’re most curious about, Illinois for example? If so, you can quickly scan down the state column to find the row and then over to the column of interest. What tables don’t allow you to do is quickly identify any visual patterns. Here the designers chose to shade the cells based on positive/negative changes, but that’s not highlighting a pattern.
Overall, this was a really strong piece from the Times. With just a few language tweaks on the front page, this would be superb.
Credit for the piece goes to Weyi Cai and the New York Times graphics department.
Last Friday, the government released the labour statistics from April and they showed a weaker rebound in employment than many had forecasted. When I opened the door Saturday morning, I got to see the numbers above the fold on the front page of the New York Times.
What I enjoyed about this layout, was that the graphic occupied half the above the fold space. But, because the designers laid the page out using a six-column grid, we can see just how they did it. Because this graphic is itself laid out in the column widths of the page itself. That allows the leftmost column of the page to run an unrelated story whilst the jobs numbers occupy 5/6 of the page’s columns.
If we look at the graphic in more detail, the designers made a few interesting decisions here.
First, last week I discussed a piece from the Times wherein they did not use axis labels to ground the dataset for the reader. Here we have axis labels back, and the reader can judge where intervening data points fall between the two. For attention to detail, note that under Retail, Education and health, and Business and professional services, the “illion” in -2 Million was removed so as not to interfere with legibility of the graphic, because of bars being otherwise in the way.
My issue with the axis labels? I have mentioned in the past that I don’t think a designer always needs to put the maximum axis line in place, especially when the data point darts just above or below the line. We see this often here, for example Construction and Manufacturing both handle it this way for their minimums. This works for me.
But for the column above Construction, i.e. State and local government and Education and health, we enter the space where I think the graphic needs those axis lines. For Education and health, it’s pretty simple, the red losses column looks much closer to a -3 million value than a -2 million value. But how close? We cannot tell with an axis line.
And then under State and local government we have the trickier issue. But I think that’s also precisely why this could use some axis lines. First, almost all the columns fall below the -1 million line. This isn’t the case of just one or two columns, it’s all but two of them. Second, these columns are all fairly well down below the -1 million axis line. These aren’t just a bit over, most are somewhere between half to two-thirds beyond. But they are also not quite nearly as far to -2 million as the ones we had in the Education and health growth were near to -3 million.
So why would I opt to have an axis line for State and local governments? The designers chose this group to add the legend “Gain in April”. That could neatly tuck into the space between the columns and the axis line.
Overall it’s a solid piece, but it needs a few tweaks to improve its legibility and take it over the line.
Credit for the piece goes to Ella Koeze and Bill Marsh.
Last week I wrote about how, for new cases, we had seen a few consecutive days of increasing cases. Were we witnessing an aberration, a one-off “well, that was weird”? Or was this the beginning of a trend towards increasing new cases?
A week later and we have our answer. Just a one-off.
If we focus on just the seven-day average, in just one week the numbers in New Jersey have fallen by half. In Pennsylvania, Virginia, it’s by one quarter. Illinois is a little less than that, as is Delaware. Across the board, numbers are falling and falling quickly.
When we move to deaths, we’re beginning to see an improvement. As the lagging indicator, we would expect these to begin to drop a few weeks after new cases begin to drop. We have begun to see what might be the peaks of deaths in a few states.
Over this coming week, I’ll be closely watching these numbers to see if we can finally begin to say authoritatively that deaths are in decline.
Vaccinations drive all of this. And we continue to see the total number of fully vaccinated people climbing in Pennsylvania, Virginia, and Illinois. But, that rate is slowing down. Most likely we are entering a phase where those eager for their shots have largely received them. Now begins the challenge of vaccinating those who might lack easy access or have reservations.
But to be clear, we need those people to become fully vaccinated before we can truly begin to return to normal. Whatever normal is. It’s hard to remember anymore.
If all goes according to plan, I should be receiving my second dose of Pfizer later this afternoon. Then it’s two more weeks until I’m fully vaccinated and ready to rejoin the world. But what kind of world will be rejoining? The allergy plagued one looking at the calendar. And that’s why this post from Indexed by Jessica Hagy made me laugh.
Two Fridays ago, I opened the door and found my copy of the New York Times with a nice graphic above the fold. This followed the announcement from the White House of aggressive targets to reduce greenhouse gas emissions
In general, I love seeing charts and graphics above the fold. As an added bonus, this set looked at climate data.
But there are a few things worth pointing out.
First from a data side, this chart is a little misleading. Without a doubt, carbon dioxide represents the greatest share of greenhouse gasses, according to the US Environmental Protection Agency (EPA) it was 76% in 2010. Methane contributes the next largest share at 16%. But the labelling should be a little clearer here. Or, perhaps lead with a small chart showing CO2’s share of greenhouse gasses and from there, take a look at the largest CO2 emitters per person.
Second, where are the axis labels?
I will probably have more on this at a later date, but neither the bar chart nor the line charts have axis labels. Now the designers did choose to label the beginning value for the lines and the bars, but this does not account for the minimums or maximums. (It also assumes that the bottom of the lines is zero.)
For example, we can see that China began 1990 with emissions at 3.4 billon metric tons. The annotation makes clear that China’s aggregate emissions surpassed those of the US in 2004. But where do they peak? What about developing countries?
If I pull out a ruler and draw some lines I can roughly make some height comparisons. But, an easier way would be simply to throw some dotted lines across the width of the page, or each line chart.
This piece takes a big swing at presenting the challenge of reducing emissions, but it fails to provide the reader with the proper—and I think necessary—context.
Credit for the piece goes to Nadja Popovich and Bill Marsh.
It’s no secret that Americans—and likely at least Western communities more broadly—live in bubbles, one of which being our political bubbles. And so I want to thank one of my mates for sending me the link to this opinion piece about political bubbles from the New York Times.
The piece is fairly short, but begins with an interactive piece that allows you to plot your address and examine whether or not you live in a political bubble. Using my flat in Philadelphia, the map shows lots of little blue dots, representing Democratic voters, near the marker for my address and comparatively few red dots for Republicans.
If you then look a bit more broadly, you can see that by summing up the dots, my geographic bubble is largely a political bubble, as only 13% of my neighbours are Republicans. Not terribly surprising for a Democratic city.
And while the piece does then zoom back out a wee bit, it tries to show me that I don’t live too far from a politically integrated bubble. Except in this case, it’s across a decent sized river and getting there isn’t the easiest thing in the world. I’m not headed to Gloucester anytime soon.
These interactives serve the purpose of drawing the user into the article, which continues explaining some of the causes of this political segregation, by both policy, redlining, and personal choice, lifestyle. The approach works, because it gives us the most relatable story in a large dataset, ourselves. We’re now emotionally or intellectually invested in the idea, in this case political bubbles, and want to learn all about it. Because the more you know…
The piece uses the same type of map to showcase the bubbles more broadly from the Bay Area to the plains of Wyoming. (No surprises in the nature of those political bubbles.) It wraps up by showing how politicians can use the geography of our political bubbles to create political geographies via gerrymandering that shore up their political careers by creating safe districts. The authors use a gerrymandered northeastern Ohio district that encompasses two cities, Cleveland and Akron, to make that point.
That’s in part why I’m in favour of apolitical, independent boundary commissions to create more competitive congressional districts. Personally, I would have been fascinated to see how Pennsylvania’s congressional districts, redrawn in 2018 by the Pennsylvania Supreme Court, after the court found the gerrymandered districts of 2011 unconstitutional, created political competition between parties instead of within parties. But I digress.
And then for kicks, I looked at how my flat in Chicago compared.
Not surprisingly, my neighbourhood in Lakeview was another political bubble, though this one even more Democratic than my current one.
But if I had wanted to move to an integrated political bubble, instead of Philadelphia, I could have moved to…Jefferson Park.
Credit for the piece goes to Gus Wezerek, Ryan D. Enos and Jacob Brown.
I’ve been trying to limit the amount of Covid-19 visualisations I’ve been covering. But on Sunday this image landed at my front door, above the fold on page 1 of the New York Times. And it dovetails nicely with our story about the pandemic’s impact on Pennsylvania, New Jersey, Delaware, Virginia, and Illinois.
For most of 2020, the United States was one of the worst hit countries as the pandemic raged out of control. Since January 2021, however, the United States has slowly been coming to grips with the virus and the pandemic. Its rate is now solidly middle of the pack—no longer is America first.
And if you compare the chart at the bottom to those that I’ve been producing, you can clearly see how our five states have really gotten this most recent wave under control to the point of declining rates of new cases.
However, you’ve probably heard the horror stories from India and Brazil where things are not so great. It’s countries like those that account for the continual increase in new cases at a global level.
Credit for the piece goes to Lazaro Gamio, Bill Marsh, and Alexandria Symonds.
I didn’t write a post last Monday, but this Monday I am. A few things may have changed in the Covid situation. The most important is that we may have finally seen the peak of this current wave’s surge of new cases.
For the last few weeks we’ve seen cases rising in the five states. Only New Jersey of late had shown a return to declining cases. About the middle of the week before last, we began to see those numbers decline. And so in this past week we did begin to see cases decline in all five states.
The thing to watch this week will be that at the very end of last week, new cases ticked up slightly for two or three days in a number of states. It could be an aberrant one-off, but with full vaccinations still well below herd immunity and cases still at high levels, it isn’t difficult to imagine a scenario where the virus begins to surge once again.
Deaths on the other hand, they continue to climb. We aren’t seeing massive increases, instead these are largely marginal. But they are increasing all the same.
Encouragingly, if cases can continue to decline, deaths will begin to fall. As a lagging indicator, they will be the last metric we see decline. Consequently, it’s a question of when, not if, deaths begin to decline. On Saturday, we did see a small decline in deaths, but one day before the weekend is insufficient to determine whether or not we’ve seen the inflection point, after which deaths would fall.
Vaccinations remain a broad set of positive news. All three states are now reporting just over 30% of their populations as fully vaccinated. However, the rate of vaccination has begun to slow.
And that worries me and the professionals, because we are still far from herd immunity. Until we reach that level, the virus can easily spread among unvaccinated populations. The charts above don’t show the decline, as they look only at the total, cumulative effect. But the charts that I see make it quite clear the decline over the last week or two.
Moral of that story is, if you haven’t been vaccinated yet, please register to do so or visit a location that allows walk-up vaccinations.