Today we have a quick piece, but one that I read at the weekend, you know, the 9/11 20th anniversary one. The article served as a quick summary of the day for those who either don’t know or don’t remember. After 20 years, there are a lot of people who have come of age in a post-9/11 world that were either not born or too young to recall those before times.
And so this map helped to identify the location of the three sites impacted by the planes: the World Trade Centre in New York, the Pentagon in Washington, and a field in Shanksville, Pennsylvania.
Except look closely at the graphic.
Little is where it belongs. The World Trade Centre marker is on Sandy Hook, New Jersey. The Pentagon is nearer to Fredericksburg than Washington. And Shanksville is in Maryland.
You can leave the dots for Washington and New York, as they are correctly placed. But why not just use some typography to put the World Trade Centre beneath New York and the Pentagon beneath Washington?
What makes it peculiar is that Shanksville is in Maryland, so it’s dot is just wrong. And so here’s a rough fix for that part of the graphic.
It was just an odd graphic for an article about one of those days that will be long-remembered in history.
Credit for the piece goes to the BBC graphics department.
It’s been a little less than a week since our last Covid-19 update for Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. At the time we had just come back from the Labour Day holiday here in the United States and that left us with two big questions. First, what would the data show after we began to process the tests after the extra time off? Second, would the holiday itself cause any increase in the numbers of new cases?
We also need to remember that last week we had seen some positive signs in some states. And we can start with those states today.
In New Jersey and Illinois we had the clearest evidence of this fourth wave peaking and new cases, whilst still climbing, slowing down with the seven-day average beginning to fall. The good news continues to be that both states continue to show signs their fourth waves have peaked. In fact, Illinois appears to be beginning a downward trajectory. New Jersey has flattened the curve, in other words exhibiting steady numbers of new cases each day.
Delaware appeared to have peaked, but after a brief dip following the holiday, the numbers have begun to shoot back up again. The seven-day average as of yesterday hit 457 new cases per day, exceeding that spike just prior to Labour Day. In other words, it appears that the fear of the holiday increasing rates of new cases, just as they appeared to be peaking came true in Delaware.
What about Virginia and Pennsylvania? Well in the former we had some indications prior to Labour Day that Virginia may have been approaching a peak of new cases. And now you can throw that out the window. Over the three-day holiday weekend, Virginia added just under 11,000 new cases. This past weekend, only two days, Old Dominion added just over 9,200. Not surprisingly the seven-day average spiked upward yesterday to 4,700 new cases per day. If the fourth wave continues at that pace, it will soon surpass the rates we saw last winter.
And in Pennsylvania the data is also not great. We had seen perhaps the beginning of a decline after a peak prior to Labour Day. In the week since? Well, the numbers of new cases have started climbing once again. In fact, yesterday the seven-day average climbed to just under 4,100 new cases per day. That is still below the spring peak and well below winter, but surpasses the numbers we saw just before Labour Day.
In other words, the fear of Labour Day creating new cases appears to have come true.
So then what about deaths? We know that deaths from any increase in cases won’t manifest in the data for a few weeks.
Starting with good news, let’s look at Pennsylvania. Two days after Labour Day the Commonwealth’s seven-day average for deaths reached 30.1 deaths per day. In the almost week since that rate has steadily dropped to 24.3 per day. Ideally we would want to see that trend extend beyond five days. Because if the Labour Day surge persists, it wouldn’t be beyond belief to imagine deaths rising again in coming days.
But that’s also about it for good news. True, Delaware went from 0.9 deaths per day to just 1.0. But that’s more of a stable rate than anything. All the other states have seen their death rates continue to climb of late. Although, we would also expect deaths to peak sometime after the peak in new cases, so this trend makes sense.
In New Jersey deaths climbed from 12.4 to 13.1 per day. Not terrible, but again still an increase in deaths. The worst increases were in Illinois and Virginia. In Illinois deaths have continued to climb, rising from 30.7 last time we wrote to 34.7. But Virginia has seen the worst, despite an apparent dip around Labour Day. Instead people are dying at increasing rates, climbing from 16.7 deaths per day to 27.1 as of yesterday.
Unfortunately, until we see new cases truly peak in Virginia those numbers are likely to continue climbing in coming days and possibly weeks.
When the remnants of Hurricane Ida rolled through the Northeast two weeks ago, here in the Philadelphia region we saw catastrophic flooding from deluges west of the city and to the east we had a tornado outbreak in South Jersey. At a simplistic level we can attribute the differences in outcomes to the path of the storm. As Ida was no longer a hurricane she developed what we call warm and cold sectors along the frontal boundary. Long story short, we can see different types of weather in these setups with heavy rain in the cold and severe weather in the warm. And that’s what we saw with Ida: heavy, flood-causing rains in the cold sector north and west of Philadelphia and then severe weather, tornadoes, in the warm sector south and east of the city.
But I want to talk about the tornadoes, and one in particular: an EF3 tornado that struck the South Jersey town of Mullica Hill. EF3 refers to the enhanced Fujita scale that describes the severity of tornadoes. EF1s are minor and EF5s are the worst. The Philadelphia region has in the past rarely seen tornadoes, but even moderate strength ones such as an EF3 are almost unheard of in the area.
This tornado caused significant damage to the area, but was also remarkable because it persisted for 12 miles. Most tornadoes dissipate in a fraction of that length. The Mount Holly office of the National Weather Service (NWS) produced a few graphics detailing tornadoes Ida spawned. You can see from the timeline graphic that the Mullica Hill tornado was particularly long lasting in time as well as distance travelled, surviving for twenty minutes.
To briefly touch on the design of this graphic, I think it generally works well. I’m not certain if the drop shadow adds anything to the graphic and I might have used a lighter colour text label for the times as they fight with the graphical components for visual primacy. Secondly, I’m not certain that each tornado needs to be in a different colour. The horizontal rules keep each storm visually separate. Colour could have instead been used to indicate perhaps peak severity for each tornado or perhaps at specific moments in the tornadoes’ lives. But overall, I like this graphic.
NWS Mt Holly also produced a graphic specifically about the tornado detailing its path.
Here we have a graphic incorporating what looks like Google Earth or Google Maps imagery of the area and an orange line denoting the path. At various points faint text labels indicate the strength of the tornado along its path. A table to the left provides the key points.
From a graphical standpoint, I think this could use a bit more work. The orange line looks too similar to the yellow roads on the map and at a quick glance may be too indistinguishable. Compare this approach to that of the Philadelphia Inquirer in its writeup.
Here we have a map with desaturated colours with a bright red line that clearly sets itself apart from the map. I think a similar approach would have benefitted the NWS graphic. Although the NWS graphic does have a stroke weight that varies depending upon the path of the tornado.
I also have a graphic made by a guy I know who lives in the area. He took that maximum tornado width of 400 yards and used screenshots of Google Maps in combination with his own direct evidence and photos and videos from his neighbours and their social media posts to try and plot the tornado’s path more granularly. Each red mark represents storm damage and a width of 400 yards.
I’m not going to critique this graphic because he made it more for himself to try and understand how close he was to the storm. In other words, it wasn’t meant to be published. But I’m thankful he allowed me to share it with you. But even here you can see he chose a colour that contrasted strongly with the background satellite views.
All of this just goes to show you the path and devastation one tornado caused. And that one tornado was just a fraction of the devastation Ida wrought upon the Northeast let alone the rest of the United States.
Last time we looked at the state of the Covid-19 pandemic in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois we had some encouraging signs. In particular we had evidence of a potential slowdown in New Jersey, Delaware, and Illinois and I wrote that I would not be surprised if we saw a peak in new cases. Virginia was the only state where things were bad and getting worse, though the rate at which they were getting bad had begun to slow. Finally, Pennsylvania had some conflicting data with its new cases and I wanted to see another week’s worth of evidence.
So a week or later, where are we?
First, the obvious caveat that the Labour Day holiday may affect these numbers in both the short term collection of data and the medium term potential for a surge of new cases from gatherings and parties.
If we look to last week’s good news states of New Jersey, Delaware, and Illinois, we do indeed see what we’ve long been waiting for since this summer: potential peaks in this fourth wave. New Jersey and Illinois. We can see the trend most clearly in New Jersey and Illinois where the beginnings of a decline from the peak appear as a slight dip from what we hope was the peak of the fourth wave. In Delaware that peak is still apparent, but the decline is less so.
Additionally for Delaware, over the last week the numbers rose above those of the third wave, i.e. in terms of cases the fourth wave is worse than the third wave of the spring. We had already seen Illinois reached that unfortunate milestone and fortunately for New Jersey the Garden State peaked at a point nearly half of the third wave’s peak.
What about last week’s bad news state? Well, Virginia, which does not report data on weekends, had an additional day of zero earlier this week. You can see that with the peak on Tuesday, which exceeded 10,000 new cases in one day. Of course, that’s really over three days and so we are talking about 3,000 per day. Unfortunately that extra holiday affected the seven-day average and caused a sudden fall. If we look closely at the data we can see that the trend probably points to a situation that continues to worsen. Two weeks ago the seven-day average was generally about 2800; last week it was about 3100; and heading into this week—excluding the Labour Day aberration—it looks to be about 3300. The very rough differences of 300 to 200 could point to a slowing rise in new cases, the necessary precursor to a peak, but we’d need to see how the rest of this week plays out before we can make any determination. But I’d probably say we are beginning to see the first signs of arriving at a peak in the coming weeks, maybe not next but perhaps in two or so.
And then we have Pennsylvania, where we had conflicting data and I wanted another week’s worth before making comment. I think the Commonwealth has indeed peaked, but unlike in New Jersey, Delaware, and Illinois, this peak looks more complicated. Note how we do have the recent spike I alluded to last week, but subsequent to that spike the numbers have been lower. I say complicated because in the aftermath of the holiday weekend we are seeing a slight tick upwards in the number of new cases, but it’s still below that spike. Consequently I’m reasonably confident we’ve just begun to peak here in Pennsylvania, but I’ll clearly want another week’s data before saying that with more authority.
What about deaths? How have those progressed over the last week?
Here too I’d be remiss if I failed to reiterate the caveats above that Labour Day can skew with the seven-day average—as we saw with Virginia—though any clustering of a surge of deaths would likely be weeks away given death’s status as a lagging indicator.
Here, unfortunately, we have not reached peak deaths for the fourth wave, at least not for all five states.
Starting with the bad news, we have two states where the numbers continue to climb. In Pennsylvania and Illinois, the two largest states in the data set, we have deaths continuing to climb. Both states’ averages exceeded 30 deaths per day yesterday. For Pennsylvania that is the first time since early June. We need not go much further back for Illinois, which had last recorded an average of 30 deaths per day in late May.
In fact, Illinois yesterday reached an average of 30.7 deaths per day. The state’s peak during the third wave was not much higher, 31.7. Given that we are seeing higher numbers of new cases in the fourth wave than we saw in the third, I would expect the deaths to continue climbing and exceed the third wave’s death rate in coming days.
Pennsylvania’s seven-day average peaked at 51 deaths per day earlier this summer and the Commonwealth’s average of 30.1 yesterday is still far below that level. Given that this fourth wave appears to be less severe in terms of new cases than the third wave, I doubt we reach the level of 51 per day, but I wouldn’t put a level in the 40s out of reach.
Next we have three states where we probably have some good news. Delaware is probably the easiest to report. Since the state has so few people with which to begin, we can expect to see fewer deaths. Indeed, this fourth wave may have peaked and did so at only 3.4 deaths per day. But yesterday that number fell to 0.9. However, I wonder if that is due to the Labour Day holiday. I would want to see more data before saying with more authority that Delaware may have peaked in terms of deaths.
Virginia and New Jersey both present cases where the seven-day averages are now down from some high numbers about two weeks ago. In late August, New Jersey reached a level of 14.3 deaths per day and Virginia hit 21.4. Yesterday’s averages had each state at 12.4 and 16.6, respectively. That looks good. But we can also see that in the last two days both states reported their highest number of daily increases since the beginning of the fourth wave. Yesterday New Jersey reported an additional 29 deaths. Virginia reported 30 yesterday, not the highest, but for that we need look only to the day before when it reported 48.
In other words, I want to see if these recent high numbers of new deaths are the result of delays from Labour Day or if we are beginning to see an actual reversal in the trend. Both at this point appear plausible. I would suspect, however, that New Jersey, given its peaked number of new cases, is more likely to be on actual downward trend in deaths. That said, given the recentness of that peak, I would still expect deaths to rise. I want another week’s worth of data to better evaluate the Garden State.
Virginia seems pretty clear to me, the most likely cause in the dip in deaths of late relates to the holiday. With the numbers of new cases continuing to climb and a peak appearing to be at least a few weeks away, Virginia probably can expect deaths to resume climbing for a bit more time. And of course this wave is already worse, in terms of deaths, than the third wave. Unfortunately I think that story line will only get stronger.
A brief bit of housekeeping, your author will be taking a brief holiday during which I won’t be posting. But I should return to posting next week.
Last week we looked at some relatively good news in terms of the spread of Covid-19 in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. We had been watching some recent data that suggested some of the five states had begun to see a real and measurable slowdown in the rate of increase of new cases.
Where are we this week? Did those trends continue?
Starting with the bad news, we have Virginia. Old Dominion is now in the throes of a Covid wave worse than what it saw back in the Spring, and fast nearing the heights—maybe depths—of the winter wave. That wave peaked just under 6,200 new cases per day. (And fell to as low as 129 per day in mid-June.) Presently Virginia reports just under 3,200 new cases per day, or nearly half that previous peak. Unfortunately, we do not yet see any significant decline in the rate at which those case numbers have increased. The line in the graphic above is no longer curving upwards, instead you can describe it as more a straight line.
Somewhere below Virginia in that it’s not bad news, but it’s also not great news, we have Delaware and Illinois. In both states the unfortunate news remains that new cases continue to increase. But at present we can also see that new cases have begun to slow. In these states the curves have begun to flatten out, though they still tilt positive.
Contrast that to New Jersey, where we have good news. The Garden State looks similar to Delaware and Illinois, but the difference is the curve in New Jersey may have peaked. The line is now tilted negative. Of course, this is a very recent development and we would want to see this trend continue for a week or so before we begin to speak of New Jersey’s wave cresting.
But between New Jersey, Delaware, and Illinois, I would not be surprised if by the end of next week we begin to see new cases peaking and beginning to decline.
But what about Pennsylvania? Initially I would have placed the Commonwealth with Delaware and Illinois as it clearly had not peaked, but it did exhibit evidence that its curve was beginning to flatten. In recent days, however, as one can clearly see in the chart, the average has begun to shoot back up again. But as I cautioned last week, that’s not uncommon. Consequently, I want to see another week’s worth of data before we begin to talk about what direction Pennsylvania is taking.
In all this though, we do have one wildcard. This weekend we in the United States begin our Labour Day holiday. Will Labour Day gatherings and parties lead to increased spread of the virus? Will we have super-spreader events? Unfortunately we will not know for about a week or week and a half after the holiday.
As all this has been happening, we also have the death rate.
Last week I noted that we had begun to see rising numbers of deaths. This made sense given that deaths lag behind new cases. Early in the pandemic it often—not always—took a few weeks before people needed hospitalisation. Then a few weeks later is often—not always—when people would die. So a few weeks after the fourth wave began to take hold we continue to see rising numbers of deaths in all five states.
In Virginia and Illinois we see two of the most significant increases. In fact in the third wave, Illinois peaked at just under an average of 32 deaths per day. As of Tuesday the seven-day average was at just over 25. And with the current trend pointing towards increasing death, it’s possible we could see the fourth wave be more lethal in Illinois than the third.
Compare that to Virginia. Old Dominion saw a smaller death rate in the third wave, peaking at 18 deaths per day. However, just yesterday the state reached an average of 21 deaths per day. In other words, Virginia’s fourth wave has become more lethal than its third wave. Unfortunately, like in Illinois we continue to see deaths climbing and there is no evidence yet that deaths are slowing down.
In the tri-state area we see some slightly better news by comparison. In Pennsylvania and New Jersey deaths remain below their third wave peak. For example, in the third wave, Pennsylvania peaked at nearly 50 deaths per day. Yesterday the average was just below 20. Despite both states being below their third wave peaks, however, deaths do continue to climb.
Delaware is the exception here. With such a small population, it reached a third wave peak of about two deaths per day. At present it’s just reached three. But I would not say that three is significantly greater than two.
Overall, however, I expect to see deaths continue to climb over the next week or two until these slowing rates of new cases begin to create slowing death rates. And so I am hopeful that in the coming few weeks we will begin to see the new case rates slow, peak, and begin to decline by about mid-September. That’s more likely in places like New Jersey, Delaware, and Illinois, but if we’re lucky those patterns or similar will soon begin to emerge in Pennsylvania and Virginia.
A few weeks back, a good friend of mine sent me this graphic from Statista that detailed the global beer industry. It showed how many of the world’s biggest brands are, in fact, owned by just a few of the biggest companies. This isn’t exactly news to either my friend or me, because we both worked in market research in our past lives, but I wanted to talk about this particular chart.
At first glance we have a tree map, where the area of each “squarified” shape represents, usually, the share of the total. In this case, the share of global beer production in millions of hectolitres. Nothing too crazy there.
Next, colour often will represent another variable, for market share you might often see greens or blues to red that represent the recent historical growth or forecast future growth of that particular brand, company, or market. Here, however, is where the chart begins to breakdown. Colour does not appear to encode any meaningful data. It could have been used to encode data about region of origin for the parent company. Imagine blue represented European companies, red Asian, and yellow American. We would still have a similarly coloured map, sans purple and green,
But we also need to look at the data the chart communicates. We have the production in hectolitres, or the shape of the rectangle. But what about that little rectangle in the lower right corner? Is that supposed to be a different measurement or is it merely a label? Because if it’s a label, we need to compare it to the circles in the upper right. Those are labels, but they change in size whereas the rectangles change only in order to fit the number.
And what about those circles? They represent the share of total beer production. In other words the squares represent the number of hectolitres produced and the circles represent the share of hectolitres produced. Two sides of the same coin. Because we can plot this as a simple scatter plot and see that we’re really just looking at the same data.
We can see that there’s a pretty apparent connection between the volume of beer produced and the share of volume produced—as one would (hopefully) expect. The chart doesn’t really tell us too much other than that there are really three tiers in the Big Six of Breweries. AB Inbev is in own top tier and Heineken is a second separate tier. But Carlsberg and China Resources Snow Breweries are very competitive and then just behind them are Molson Coors and Tsingtao. But those could all be grouped into a third tier.
Another way to look at this would be to disaggregate the scatter plot into two separate bar charts.
You can see the pattern in terms of the shapes of the bars and the resulting three tiers is broadly the same. You can also see how we don’t need colour to differentiate between any of these breweries, nor does the original graphic. We could layer on additional data and information, but the original designers opted not to do that.
But I find that the big glaring miss is that the article makes the point despite the boom in craft beer in recent years, American craft beer is still a very small fraction of global beer production. The text cites a figure that isn’t included in the graphic, probably because they come from two different sources. But if we could do a bit more research we could probably fit American craft breweries into the data set and we’d get a resultant chart like this.
This more clearly makes the point that American craft beer is a fraction of global beer production. But it still isn’t a great chart, because it’s looking at global beer production. Instead, I would want to be able to see the share of craft brewery production in the United States.
How has that changed over the last decade? How dominant are these six big beer companies in the American market? Has that share been falling or rising? Has it been stable?
Well, I went to the original source and pulled down the data table for the Top 40 brewers. I took the Top 15 in beer production, all above 1% share in 2020, and then plotted that against the change in their beer production from 2019 to 2020. I added a benchmark of global beer production—down nearly 5% in the pandemic year—and then coloured the dots by the region of origin. (San Miguel might not seem to fit in Asia by name, but it’s from the Philippines.)
What mine does not do, because I couldn’t find a good (and convenient) source is what top brands belong to which parent companies. That’s probably buried in a report somewhere. But whilst market share data and analysis used to be my job, as I alluded to in the opening, it is no longer and I’ve got to get (virtually) to my day job.
A few weeks ago I wrote about the United Nation’s Intergovernmental Panel on Climate Change (IPCC) latest report on climate change, which synthesised the last several years’ data. If you didn’t see that post, suffice it to say things are bad and getting worse. At the time I said I wanted to return to talk about a few more graphics in the release. Well, here we are.
In this piece we have a map, three technically. In a set of small multiples, the report’s designers show the observed change, i.e. what’s happening today, and the degree of scientific consensus on whether humans are causing it.
What I like about this is that, first, improved data and accuracy allows for sub-continental breakdowns of climate change’s impacts. That breakdown allowed the designers to use a tilemap consisting of hexagons to map those changes.
Since we don’t look at the world in this kind of way, the page also includes a generous note where it defines all these acronyms. Of course even with those, it still doesn’t look super accurate—and that is fine, because that’s the point—so little strokes outside clusters of hexagons are labelled to further help the reader identify the geographic regions. I really like this part.
I also like how little dots represent the degree of confidence. The hexagons give enough space to include dots and labels while still allowing the colours to shine. These are really nice.
But then we get to colour, the one part of this graphic with which I’m not totally thrilled. The first map looks at temperature, specifically heat extremes. Red means increase in heat extremes and blue means decrease. Fair enough. Hatched pattern means there is low consensus and medium grey means there’s little data. I like it.
Moving to the second map we look at heavy precipitation. Green means an increase and yellow a decrease. Hatched and medium grey both mean the same as before. I like this too. Sure, with clear titling you could still use the same colours as the first map, but I’ll buy if you’re selling you want visual distinction from the red–blue map above.
Then we get to the third map and now we’re looking at drought. Hatched and grey mean the same. Good. But now we have green and yellow, the same green and yellow as the second map. Okay…but I thought the second map showed we need a visual distinction from the first? But what makes it really difficult is that in this third map we invert the meaning of green and yellow. Green now means a decrease in drought and yellow an increase.
I can get that a decrease in drought means green fields and an increase in drought means dead and dying fields, yellow or brown. And sure, red and blue relate to hot and cold. But the problem is that we have the exact same colours meaning the opposite things when it comes to precipitation.
Why not use two other colours for precipitation? You wouldn’t want to use blue, because you’re using blue in the first map. But what about purple and orange, like I often do here on Coffeespoons? This is why I don’t think the designers needed to switch up the colours from map to map. Pick a less relational colour palette, say purple and orange, and colour all three maps with purple being an increase and orange being a decrease.
Colour is my big knock on these graphics, which unfortunately could otherwise have been particularly strong. Of course, I can’t blame designers for going with red and blue for hot and cold temperatures. I’ve had the same request in my career. But it doesn’t make reading these charts any easier.
Credit for the piece goes to the IPCC graphics team.
I spent way more time trying to craft that title than I’d like to admit. Headline writing is not easy.
Quick little piece today about Sankey diagrams. I love them. You often see them described as flow diagrams—this piece is in the article we’ll get to shortly—but they are more of a subset within a flow diagram. What sets Sankeys apart is their use of proportional strokes or widths of the directional arrows to indicate share of movement.
The graphic in question comes from an article about Major League Baseball’s (MLB’s) problem with “sticky stuff”. For the unfamiliar, sticky stuff is a broad term for foreign substances pitchers put on their fingers to provide better grip on the baseball. A better grip makes it easier to create movement like sliding and sinking in a pitch there therefore makes it harder for a hitter to hit it. Back when I was a wannabe pitcher, it was spitballs and scuff balls. Now professionals use things like Spider Tack. These are substances that allow you to put the ball in the palm of your hand, then turn your hand over to face the ground and not have the ball fall out of your hand.
So the graphic looks at starting pitchers and how their spin rate, the quantifiable measure impacted by sticky stuff, of their fastballs has changed since MLB instituted a ban on sticky stuff. (It had actually long been in place, see spitballs for example, but had rarely been enforced.)
This graphic explores how 223 pitchers saw their spin rates change in the first two months after the change in policy was announced to the nearly month after that period.
Sankeys use proportional width not just to show movement from category to category but the important element of what share of which category moves to which category. For example, we can see a little less than half of starting pitchers saw their spin rates stay the same after the policy change and another almost equal group saw their spin rates decrease. That’s probably a sign they were using sticky stuff and stopped lest they get caught.
But we can then see of that group, maybe 1/6 then saw their spin rates increase again over the last month. That could be a sign that they have found a way to evade the ban. Though it could also be they’ve found new ways of gripping or throwing the baseball. Spin rate alone does not prove sticky stuff usage.
Similarly, we can see that in the group that maintained their spin rate, a small group has found a way to increase it. Finally, a small fraction of the original 223 saw their spin rates increase and a fraction of that group has seen their spin rates increase even further.
This was just a really nice graphic to see in an article from the Athletic about sticky stuff and its potential return.
Last week I mentioned how there was some initial evidence showing the rapid, near-exponential spread of the virus was beginning to slow down. One week later, where are we?
The good news is that those initial signals do appear to be true, i.e. not noise. You can see it if you look at the very end of the charts for all but Virginia.
We can see the slowing spread rate most easily in Illinois and New Jersey. The shape of the curve now resembles more like the top of a hill rather than the beginning of a roller coaster. To be clear, this doesn’t mean Covid-19 is no longer spread—that is not the case at all. Rather, just the speed at which people are spreading the virus has slowed from that initial rapid acceleration.
In the last week, however, despite the good news for Illinois, we can also see that this fourth wave, driven by the Delta variant, has now exceeded the third wave we saw earlier this spring. Virginia still remains the only other state joining Illinois in that auspicious category, but Delaware is edging ever closer.
However, Delaware as well as Pennsylvania can both join Illinois and New Jersey in seeing slowing rates, though it’s not nearly as evident as in the other two states. Delaware continues to approach its third wave peak.
Virginia is the one state where we might just now be seeing the beginning of a slowdown. Though it’s probably the hardest state in which to see it. Yesterday, after a weekend of no data updates, the state reported over 7,000 new cases. That’s bad. But jumping from 5,900 new cases last Monday to yesterday’s 7,100 is comparatively good. Compare Monday to Monday, four weeks ago the increase was 91%. Three weeks ago it was 88.8%. Three weeks ago it fell to 30.9%. And then two weeks ago it was 26.8%. Yesterday’s increase was only 20%. Again, not great, but that’s a slowdown.
The hope in all five states is that we can begin to actually peak perhaps in early- to mid-September before the seven-day average begins to decline. The question then will be what do things look like as begin to head into flu season, which is when last winter’s deadly surge began in earnest.
What about deaths though?
Last week I discussed how we were seeing death rates hold fairly steady with only small increases to the seven-day averages. Unfortunately this past week we saw more significant increases. Nothing too dramatic, to be clear, but increases all the same.
Take Pennsylvania, for example. Two weeks ago we went from an average of 7 deaths per day due to Covid-19 to just 9 last week. But yesterday that number jumped up to 16. Illinois, which had went from 12 to 13, climbed from 13 to 20 as of yesterday.
This is also not terribly surprising. As we are now several weeks into this wave, we would expect to see hospitalisations increase, which we had been seeing, before, sadly, deaths followed suit. We may now be entering that phase where deaths, again, a lagging indicator, begin to rise.
I do not think, however, that we will see numbers near to the extremes we saw this past winter. Even to reach levels we saw this past spring will be difficult. However, I’d be remiss if I didn’t point out that Illinois has reached nearly half its third wave peak number of deaths.
We will need to continue observing these death rates over the coming week to see if they continue to increase. I suspect they will before, like new cases, they begin to slow down before then peaking.
After a rainy weekend in Philadelphia thanks to Hurricane Henri, we are bracing for another heat wave during the middle of this week. Of course when you swelter in the summer, you seek out shade. But as a recent article in the Philadelphia Inquirer pointed out, not all neighbourhoods have the same levels of tree cover, or canopy.
From a graphics standpoint, the article includes a really nice scatter plot that explores the relationship between coverage and median household income. It shows that income correlates best with lack of shade rather than race. But I want to focus on a screenshot of another set of graphics earlier on in the article.
I enjoyed this graphic in particular. It starts with a “simple” map of tree coverage in Philadelphia and then overlays city zip codes atop that. Two zip codes in particular receive highlights with bolder and larger type.
Those two zip codes, presumably the minimum and maximum or otherwise broadly representative, then receive call outs directly below. Each includes an enlarged map and then the data points for tree cover, median income, and then Black/Latino percentage of the population.
I don’t think the median income needs to be in bar chart form here, especially given the bars do not line up so that you can easily compare the zip codes. The numbers would work well enough as factettes or perhaps a small dot plot with the zip codes highlighted could work instead.
Additionally, the data labels would be particularly redundant if a small scale were used instead. That would work especially well if the median income were moved to the lowest place in the table and the share charts were consolidated in one graphic. Conceptually, though, I enjoy the deep dive into those two zip codes.
Then I wanted to highlight some great design work on the maps. Note how in particular for Chestnut Hill, 19118, the outline of the zip code is largely in a thicker, black stroke than the rest of the map. At the upper right, however, you have two important roads that define the area and the black stroke breaks at those points so the roads can be clearly and well labelled. The other map does the same thing for two roads, but their breaks are shorter as the roads run perpendicular to the border.
Overall this was just a great piece to read and I thoroughly enjoyed the graphics.