More on Those Million Covid-19 Deaths

Yesterday I focused on the big graphic from the New York Times that crossed the full spread of the front/back page. But the graphic was merely the lead graphic for a larger piece. I linked to the online version of the article, but for this post I’m going to stick with the print edition. The article consists of a full-page open then an entire interior spread, all in limited colour. The remainder of the extensive coverage consists of photo essays and interviews that understandably attempt to humanise the data points, after all, each dot from yesterday represented one individual, solitary, human being. That is an important element of a story like this and other national and international tragedies, but we also need to focus on the data and not let the emotion of the story overwhelm our rational and logical analysis.

Sometimes it’s hard to realise we’re in the third year of this pandemic.

From a data visualisation standpoint the first page begins simply enough with a long timeline of the Covid-19 pandemic charting the number of absolute deaths each day. As we looked at yesterday, the absolute deaths tell part of the story. But if we were to have looked at the number of absolute cases in conjunction with the deaths, we could also see how the virus has thus far evolved to be more transmissible but less lethal. Here the number of daily deaths from Omicron surpassed Delta, but fell short of the winter peak in early 2021. But the number of cases exploded with Omicron, making its mortality rate lower. In other words, far more people were getting sick, but as far fewer were dying.

An interesting note is that if you take a look at the online version, there the designers chose a more stylised approach to presenting the data.

All the dots

Here they kept the dot approach and simply stacked and reordered the dots. However, I presume for aesthetic reasons, they kept the stacking loose dots and dropped all the axis lines because it does make for a nice transition from the map to this chart. But they also dropped all headings and descriptors that tell the reader just what they are looking at. These decisions make the chart far less useful as a tool to tell the data-driven element of the story.

There are three annotations that label the number of deaths in New York, the Northeast, and the rest of the United States. But what does the chart say? When are the endpoints for those annotations? And then you can compare the scale of the y-axis of this chart and compare it to the printed version above. A more dramatic scale leads to a more dramatic narrative.

This sort of visual style of flash and fancy transitions over the clear communication of the data is why I find the print piece more compelling and more trustworthy. I find the online version, still useful, but far more lacking and wanting in terms of information design.

The interior spread is where this article shines.

Just a fantastic spread.

From an editorial design standpoint, the symmetry works very well here. It’s a clear presentation and the white space around the graphic blocks lets that content shine as it should in this type of story. Collectively these pieces do a great job telling the story of the pandemic thus far across the nation. The graphics do not need a lot of colour and make do with sparse flash. Annotations call the reader’s attention to salient points and outliers.

Very nice work here.

From a content standpoint, I would be particularly curious if we have robust data for deaths by education level. Earlier this year I recall reading news about a study that said education best correlated to Covid cases, and I would be curious to see if that held true for deaths. Of course these charts do a great job of showing just how effective the vaccines were and remain. They are the best preventative measure we have available to us.

More really nice graphics

Here I disagree with the design decision of how to break down the states into regions. The Census Bureau breaks down the United States into four regions using the same names as in the graphic above. However, if you look closely at the inset map, you will see that Delaware, Maryland, and West Virginia in particular are included as part of the Northeast. (I cannot tell if the District of Columbia is included as part of the Northeast or South.)

Now compare that to the Census Bureau’s definition:

How the government defines US geography

If you ask me to include Delaware and Maryland as part of the Northeast, well, if you’re selling it, I’ll buy it. After all, just because the Census Bureau defines the United States this way does not mean the New York Times has to. Both are connected to the Northeast Corridor via Amtrak and I-95 and are plugged into the Megalopolis economy. Maybe the Potomac should be the demarcation between Northeast and South. But I struggle to understand West Virginia. Before you go and connect it to the Northeast, I would argue that West Virginia has far more in common with the Midwest geographically, economically, and culturally.

More critically, given this issue, it strikes me as a serious problem when the online version of the chart—with the aforementioned issues—does not even include the little inset to highlight this at best unusual regional definition.

Where would you place West Virginia?

And so while I have reservations about the data—how would the data have looked if the states were realigned?—the design of the line charts overall is good.

Again, I am talking about the print version, not that online graphic. I would argue that the above screenshot is barely even a chart and more “data art” or an illustration of data. Consider here, for example, that for the South we have that muted slate blue for the dots, but the spacing and density of the dots leads to areas of lighter slate and darker slate. But a lighter slate means more space between stacked dots and darker slate means a more compact design. A lighter colour therefore pushes the “edge” of the line further up the y-axis and artificially inflates its value, not that we can understand what that value is as the “chart” lacks any sort of y-axis.

Finally the print piece has a set of small multiples breaking down deaths by income in the three largest American cities: New York, Los Angeles, and Chicago. These are just great little charts showing the correlation between income and death from Covid, organised by Zip code.

But this also serves as a stark reminder of just how much better the print piece is over the online version. Because if we take a look at a screenshot from the online article, we have a graphic that addresses all the issues I pointed out earlier.

Why couldn’t the online article kept to this style?

I am left to wonder why the reader of the online version does not have access to this clearer and more accurate representation of the data throughout the piece?

To me this article is a great example of when the print piece far exceeds that of the online version. Content-wise this is a great story that needed to be told this weekend, but design wise we see a significant gap in quality from print to online. Suffice it to say that on Sunday I was very glad I received the print version.

Credit for the piece goes to Sarah Almukhtar, Amy Harmon, Danielle Ivory, Lauren Leatherby, Albert Sun, and Jeremy White.

All the Colours, All the Space

Everyone knows inflation is a thing. If not, when was the last time you went shopping? Last week the Boston Globe looked specifically at children’s shoes. I don’t have kids, but I can imagine how a rapidly growing miniature human requires numerous pairs of shoes and frequently. The article explores some of the factors going into the high price of shoes and uses, not very surprisingly, some line charts to show prices for components and the final product over time. But the piece also contains a few bar charts and that’s what I’d like to briefly discuss today, starting with the screenshot below.

What is going on here?

What we see here are a list of countries and the share of production for select inputs—leather, rubber, and textiles—in 2020. At the top we have a button that allows the user to toggle between the two and a little movement of the bars provides the transition. The length of the bar encodes the country in question’s market share for the selected material.

We also have all this colour, but what is it doing? What data point does the colour encode? Initially I thought perhaps geographic regions, but then you have the US and Mexico, or Italy and Russia, or Argentina and Brazil, all pairs of countries in the same geographic regions and yet all coloured differently. Colour encodes nothing and thus becomes a visual distraction that adds confusion.

Then we have the white spaces between the bars. The gap between bars is there because the country labels attach to the top of the bars. But, especially for the top of the chart, the labels are small and the gap is at just the right height such that the white spaces become white bars competing with the coloured bars for visual attention.

The spaces and the colours muddy the picture of what the data is trying to show. How do we know this? Because later in the article we get this chart.

Ahh, much better. Much clearer.

This works much better. The focus is on the bars, the labelling is clear, almost nothing else competes visually with the data. I have a few quibbles with this design as well, but it’s certainly an improvement over the earlier screenshot we discussed. (I should note that this graphic, as it does here, also comes after the earlier graphic.)

My biggest issue is that when I first look at the piece, I want to see it sorted, say greatest to least. In other words, Furniture and bedding sits at the top with its 15.8% increase, year-on-year, and then Alcoholic beverages last at 3.7%. The issue here, however, is that we are not necessarily looking at goods at the same hierarchical level.

The top of the list is pretty easy to consider: food, new vehicles, alcoholic beverages, shelter, furniture and bedding, and appliances. We can look at all those together. But then we have All apparel. And then immediately after that we have Men’s, Women’s, Boys’ , Girls’, and Infants’ and toddlers’ apparel. In other words, we are now looking at a subset of All apparel. All apparel is at the same level of Food or Shelter, but Men’s apparel is not.

At that point we would need to differentiate between the two, whilst also grouping them together, because the range of values for those different sub-apparel groups comprise the aggregate value for All apparel. And showing them all next to Food is not an apples-to-apples comparison.

If I were to sort these, I would sort by from greatest to least by the parent group and then immediately beneath the parent I would display the children. To differentiate between parent-level and children-level, I would probably make the bars shorter in the vertical and then address the different levels typographically with the labels, maybe with smaller type or by putting the children in italic.

Finally, again, whilst this is a massive improvement over the earlier graphic, I’d make one more addition, an addition that would also help the first graphic. As we are talking about inflation year-on-year, we can see how much greater costs are from Furniture and bedding to Alcoholic beverages and that very much is part of the story. But what is the inflation rate overall?

According to the Bureau of Labour Statistics, inflation over that period was 8.5%. In other words, a number of the categories above actually saw price increases less than the average inflation rate—that’s good—even though they were probably higher than increases had been prior to the pandemic—that’s bad. But, more importantly for this story, with the addition of a benchmark line running vertically at 8.5%, we could see how almost all apparel and footwear child-level line items were below the inflation rate. But the children and infant level items far exceeded that benchmark line, hence the point of the article. I made a quick edit to the screenshot to show how that could work in theory.

To the right, not so good.

Overall, an interesting article worth reading, but it contained one graphic in need of some additional work and then a second that, with a few improvements, would have been a better fit for the article’s story.

Credit for the piece goes to Daigo Fujiwara.

America’s Crime Problem

During the pandemic, media reports of the rise of crime have inundated American households. Violent crimes, we are told, are at record highs. One wonders if society is on the verge of collapse.

But last night a few friends asked me to take a look at the data during the pandemic (2020–2021) and see what is actually going on out on the streets in a few big cities. Naturally I agreed and that’s why we have this post today. The first thing to understand, however, is that we do not have a federal-level database where we can cross compare crimes in cities using standardised definitions. The FBI used to produce such a thing, but in 2020 retired it in favour of a new system that, for reasons, local and state agencies have yet to fully embrace. Consequently, just when we need some real data, we have a notable lack of it.

At the very least we have national-level reporting on violent crimes and homicides, the latter of which is a subset of violent crimes. Though these reports are also dependent on local and state agencies self-reporting to the FBI. I also wanted to look at not just whether crime is up of late, but is crime up over the last several years. I chose to go back 30 years, or a generation.

We can see one important trend here, that at a national level violent crimes are largely stable at rate of 400 per 100,000 people. Homicides, however, have climbed by nearly a third. Violent crimes are not rising, but murders are.

My initial charge was to look at cities and violent crime. However, knowing that nationally violent crimes are largely stable, the issue of concern would be how the rise in murders is playing out on American city streets. With the caveat that we do not have a single database to review, I pulled data directly from the five cities of interest: Philadelphia, Chicago, New York, Washington, and Detroit.

I also considered that large cities will have more murders simply by dint of their larger populations. And so when I collected the data, I also tried to find the Census Bureau’s population estimates of the cities during the same time frame. Unfortunately the 2021 estimates are not yet available so I had to use the 2020 population estimates for my 2021 calculations.

First we can see that not all cities report data for the same time period. And for Detroit in particular that makes comparisons tricky. In fact only New York had data back to the beginning of the century. Regardless of the data set’s less than full robustness we can see that in all five cities homicides rose in 2020 and 2021.

Second, however, if squint through that lack of full data, we see a trend at the city level that aligns with the national level. Homicides, tragically, are indeed up. However, in New York and Washington homicides are still below the data from near 2000 and at that time homicides already appear on a downward trajectory. I would bet that homicides were even higher during the 1990s and that the 2000s represented a long-run decline. In other words, whilst homicides are up, they are still below their peaks. A worrying trend, but far from the sky is falling.

That cannot quite be said for other cities. Let’s start with Detroit. Sadly we have too few years of data to draw any conclusion other than that homicides rose compared to the years preceding the pandemic.

That leaves us with Philadelphia and Chicago. Philadelphia has less data available and it’s harder to make a determination of what is happening. But we can say that since 2007, homicides have not been higher. If you look closely though, you can see how there does appear to be a downward trend at the beginning of the line. We do not have enough data like we do with New York and Washington, but I would bet homicides are up in Philadelphia, but still far short of what they were in the 1990s.

Chicago is the oddball. Yes, it saw a peak in homicides during the pandemic. But in 2016 the city didn’t miss the pandemic peak by much. In other words, homicides were staggeringly high in Chicago before the pandemic. If anything, we see a failure to combat high crime rates. But even before that spike in 2016, we see more of a valley floor in homicides. True, at the beginning of the century homicides appear to have trended down. But unlike the other cities here, homicides bottomed out at around 450 per 100,000 people. I’m not so certain we had a persistent, long-run decline in Chicago with which to start.

And like I said above, larger populations we would expect to have more murders because more potential criminals and victims. When we equalise for population we see the same trends as we expect—the city populations have been relatively stable over the last 20 years. Instead what we see is that relative to each other murders are more common in some cities and less so in others.

New York is a great example with nearly 500 murders last year, a number on par with Philadelphia. But New York has over 8 million inhabitants. Philadelphia has just 1.6. Consequently New York’s homicide rate is a surprisingly low 5.9 per 100,000 people. Philadelphia’s on the other hand? 35.6.

Philadelphia is near the top of that list, with Washington and Chicago having similar, albeit lower, rates at 31.7 and 30.1, respectively. But sadly Detroit surpasses them all and is in league of its own: 47.5 in 2021.

Credit for the pieces is mine.

Philadelphia’s Wild Winters

Winter is coming? Winter is here. At least meteorologically speaking, because winter in that definition lasts from December through February. But winters in Philadelphia can be a bit scattershot in terms of their weather. Yesterday the temperature hit 19ºC before a cold front passed through and knocked the overnight low down to 2ºC. A warm autumn or spring day to just above freezing in the span of a few hours.

But when we look more broadly, we can see that winters range just that much as well. And look the Philadelphia Inquirer did. Their article this morning looked at historical temperatures and snowfall and whilst I won’t share all the graphics, it used a number of dot plots to highlight the temperature ranges both in winter and yearly.

Yep, I still prefer winter to summer.

The screenshot above focuses attention on the range in January and July and you can see how the range between the minimum and maximum is greater in the winter than in the summer. Philadelphia may have days with summer temperatures in the winter, but we don’t have winter temperatures in summer. And I say that’s unfair. But c’est la vie.

Design wise there are a couple of things going on here that we should mention. The most obvious is the blue background. I don’t love it. Presently the blue dots that represent colder temperatures begin to recede into and blend into the background, especially around that 50ºF mark. If the background were white or even a light grey, we would be able to clearly see the full range of the temperatures without the optical illusion of a separation that occurs in those January temperature observations.

Less visible here is the snowfall. If you look just above the red dots representing the range of July temperatures, you can see a little white dot near the top of the screenshot. The article has a snowfall effect with little white dots “falling” down the page. I understand how the snowfall fits with the story about winter in Philadelphia. Whilst the snowfall is light enough to not be too distracting, I personally feel it’s a bit too cute for a piece that is data-driven.

The snowfall is also an odd choice because, as the article points out, Philadelphia winters do feature snowfall, but that on days when precipitation falls, snow accounts for less than 1/3 of those days with rain and wintry mixes accounting for the vast majority.

Overall, I really like the piece as it dives into the meteorological data and tries to accurately paint a portrait of winters in Philadelphia.

And of course the article points out that the trend is pointing to even warmer winters due to climate change.

Credit for the piece goes to Aseem Shukla and Sam Morris.

The Terrible No Good Chart About Gas Prices

Saw this graphic on the Twitter the other day from the Democratic Congressional Campaign Committee (DCCC), or the D Triple C or D Trip C. The context was that earlier in the day Matt Yglesias posted a clearly tongue-in-cheek chart about how after signing the infrastructure bill, President Biden had single-handedly fixed inflation and gas prices were heading down.

Oh, the power to misuse FRED.

Of course, anyone with a brain knows this isn’t true. The President of the United States cannot control the price of petrol. Because, you know, market economy. The underlying problem of high demand and low supply was, of course, not solved by the infrastructure bill. But lots of people complain on the telly or the internets about Biden not doing more about inflation, but, you know, not really within the wheelhouse.

Anyway, this chart in particular does not bother me. Because Yglesias knows—and most of his audience knows—it is not meant to be taken seriously. It is really just a joke.

But emphasis most of his audience.

Because the DCCC later posted this graphic with the accompanying text “Thanks, Joe Biden”.

Oh boy.

Oh boy.

Clearly they didn’t get the memo about the original being a joke.

The entire scale of the chart is 4¢. I cannot even recall the last time I had to use the glyph ¢ we’re talking so small a scale. The change in the the three week period amounts to a decline of 2¢.

And now you get the joke of the post. Ask me my 2¢ about the chart…

Now look closely at that y-axis. You’ll also note that we are carrying it all the way out to the third decimal point. Now, it’s true that some petrol stations will have a wee little nine trailing just after the two digits to the right of the decimal. Sometimes you might see a 9/10. As was explained to me in school that’s because people will buy something if it looks even a fraction of a cent cheaper. Thing 99¢—getting the use out of this glyph today—versus $1. Makes all the difference. So back when petrol was cheap (inflation stories come round and round), 0.899 looked better than 0.90. But now that it’s routinely well over a few dollars, that 9/10 is a laughable percentage of the total price.

So, yes, we do present petrol prices to three decimals in the environmental design space. But think to yourself, when have you ever aloud repeated a price to the third decimal point? You probably haven’t. And so this chart probably shouldn’t be using that granular a level of specificity.

The other underlying problem, jokes aside, is that the chart spends all that horizontal space looking at three data points. Three. If the data were showing the daily price, not the weekly average, we’d have 21 days worth of data, and that—scale notwithstanding—would be worthy of charting. My basic rule is that if it’s five or six data points, you can use a table unless there is a contextual or design reason for doing so. Say, for example, you’re doing a series of small multiples for a time series of objects in a category. For all but a few categories you have dozens of data points, but just a few have really spotty observations. In those cases, plot the three or four numbers. But in this case, just don’t.

Instead this kind of graphic is best presented as a factette, a big old number, preferably in a narrow or condensed width. Because a 2¢ decline over a three-week period is also not terribly newsworthy. (Unless your story is how prices haven’t changed much over the last three weeks.)

This also points to how the original chart misses the context of time. Granted, a lot can happen in three weeks, but a 2¢ shift is not massive. Give those three weeks their proper place in time, however, and you can see just how little movement that truly is. Cue my own quickly whipped up charts.

That’s more like it.

In the first chart you can begin to see how the change, during the course of the last nearly two years, is not significant. And in the second you can see that things really are not that bad compared to where they were back during the lead up to the Great Recession and then in the recovery that followed. (Aww, look at back in the early oughts when prices averaged just over a $1/gallon. I can still remember filling up my minivan for prices like 99¢.)

If the designer wants to make a point that perhaps we’re reaching the peak prices during this time period, sure. Because a two-week decline in prices could well be the beginning of that. But, to show that you also need to show the context of the time before that.

But once again, the President of the United States cannot much affect the price of petrol short of releasing the strategic reserve, which as its name implies, is meant for strategic purposes in case of national emergency. And high consumer prices are not a strategic national emergency on the scale of, say, a crippling storm impacting the refineries in the Gulf or an earthquake destroying pipelines in Alaska or an invasion or stifling blockade of overseas imports.

At the end of the day, this was just a terrible, terrible chart. And I think it speaks to a degree of chart illiteracy that I see creeping up in society at large. Not that it wasn’t there in the past—get off my lawn, kids—but seems more ever present these days. I don’t know if that’s because of the amplification effect of things like the Twitter or just a decline in education and critical thinking. But those are topics for another day.

This chart fails on so many levels. The concept is bad, i.e. neither Biden nor Trump nor their predecessors nor their successors—unless we adopt a planned economy, am I right, comrades?—can directly affect petrol prices. Prices are governed by larger market forces that boil down to supply and demand.

But also, the sheer design is bad. Don’t use a chart of three data points. Don’t stretch out the x-axis. Don’t use decimal points to a point where they’re unrecognisable.

In the meantime, charts like this? Don’t do them, kids.

Credit for the first original goes to FRED, whose chart Matt Yglesias used.

Credit for the second goes to the DCCC graphics department.

Oh, and because I used Federal Reserve data for the charts, and because I work there, I should add the views and opinions are my own and don’t represent those of my employer.

Speaking Truth to Employees

First, today is Friday and so congrats to us all for reaching the weekend. But before the weekend begins, I want to do a little housekeeping. I am taking my first real holiday for the first time in two years—thanks, Covid. So don’t expect any posts for the next two weeks. But I’ll be back on the 25th.

Thus it seems like a good time to remind everyone to take your holiday time. Or vacation time. Or paid time off. Or whatever you call those days that your employer pays you, but you don’t have to do a damn thing. Thankfully, Jessica Hagy over at Indexed has a graphic that can explain it better than I can. She titled the piece, “Use Your Vacation Days”.

Yep.

So yeah, use your holiday time. I am. See you all in two weeks.

Credit for the piece goes to Jessica Hagy.

Covid Update: 29 September

Last week when I wrote my update on Covid-19, we had seen a few signs for optimism, but in other states the news was hard to interpret or, in the case of Pennsylvania, not going the right way at all. So where are we this week? In some ways, not a lot has changed over the last seven days.

New case curves for PA, NJ, DE, VA, & IL.

Last week, we had positive developments in both New Jersey and Illinois. There cases had begun to noticeably and consistently fall with clear peaks in this fourth wave of infections. Their seven-day averages were decidedly below their recent peaks. That trend continued last week. In fact, in Illinois the seven-day average is now also below the peak from not just this fourth wave, but also the third wave. That’s good.

New Jersey’s fourth wave was nowhere near as impactful as its first three. It helps to have one of the highest vaccination rates in the United States. But the Garden State’s seven-day average is also falling, though not as quickly as in Illinois. You could even make the argument that over the last week cases have really remained flat, though the last few days I would contend are evidence of a slow slowdown.

Delaware had been a tricky state to judge given some recent volatility in its average. But as we can see over the last week the new case curve clearly has flattened. The flat line, however, remains just that, a flat line. This is more of a plateau shape than a descending hill shape. That means that cases are continuing to spread, but at a steady rate of about 450 new cases per day. This isn’t uncommon, but hopefully it precedes a fall in new cases rather than serving as a respite on an ever upward climb.

In Virginia I had mentioned some early indications of a potential flattening, the first step towards a decline in the average. That flattening appears to be taking hold. In the chart above you can clearly see a sharp decline beginning to take root in Old Dominion. The curve here most closely resembles Illinois in what, at least for now, is a fairly symmetrical increase and decrease.

Finally we have Pennsylvania. I was pretty short in my analysis last week, the state was headed in the wrong direction. The latest data shows that the Commonwealth may just be beginning to turn the corner and flatten the curve. However, after the pre-Labour Day slowdown that then erupted into a full-blown outbreak, I’m wary of saying anything definitive about Pennsylvania. All we can do is hope that these early trends hold true.

So what about deaths? Are we seeing any progress on that front? Last week I noted that it was almost all bad news. In all but Illinois we had death rates continuing to climb.

Death curves for PA, NJ, DE, VA, & IL.

That story, sadly, remains largely the same. Illinois, unfortunately has actually seen its seven-day average resume ticking upwards, although not by a significant degree. It’s enough that I think it fair to say deaths have largely plateaued and not necessarily begun to climb. And as I keep saying, that would track for a state where we have seen new cases falling for the last few weeks now.

Unfortunately, that’s about it. Deaths in New Jersey have remained fairly stable, though the average has moved from 19.3 to 17.4 as of yesterday. Perhaps that could be an indication of a falling death rate. But just a few days ago it was still nearer 19 than 18. I would want to see more data showing a consistent and persistent decline before saying New Jersey is headed the right way.

And in Pennsylvania, Delaware, and Virginia, deaths are headed the wrong way, plain and simple. At the beginning of the sample set, Delaware reported 14 deaths in one day, the most in a month. Consequently the average has jumped from 2.6 last week to 3.4 today. In Virginia we had seen deaths jump from 20 to 34. Well this week they jumped again, though by half the amount, to 41 deaths per day. Pennsylvania performed the worst, however. Deaths here climbed from 43 to 57 per day.

While we have seen new cases plateau in Delaware and begin to fall in Virginia, which should mean declining death rates in a few weeks, in Pennsylvania the numbers of new cases may only be beginning to flatten. Consequently, unless we begin to see a sharp decline in new cases, we will likely continue to see rising deaths in the Commonwealth. At least for a little while longer.

Credit for the piece is mine.

Covid Vaccination and Political Polarisation

I will try to get to my weekly Covid-19 post tomorrow, but today I want to take a brief look at a graphic from the New York Times that sat above the fold outside my door yesterday morning. And those who have been following the blog know that I love print graphics above the fold.

On my proverbial stoop this morning.

Of the six-column layout, you can see that this graphic gets three, in other words half-a-page width, and the accompany column of text for the article brings this to nearly 2/3 the front page.

When we look more closely at the graphic, you can see it consists of two separate parts, a scatter plot and a line chart. And that’s where it begins to fall apart for me.

Pennsylvania is thankfully on the more vaccinated side of things

The scatter plot uses colour to indicate the vote share that went to Trump. My issue with this is that the colour isn’t necessary. If you look at the top for the x-axis labelling, you will see that the axis represents that same data. If, however, the designer chose to use colour to show the range of the state vote, well that’s what the axis labelling should be for…except there is none.

If the scatter plot used proper x-axis labels, you could easily read the range on either side of the political spectrum, and colour would no longer be necessary. I don’t entirely understand the lack of labelling here, because on the y-axis the scatter plot does use labelling.

On a side note, I would probably have added a US unvaccination rate for a benchmark, to see which states are above and below the US average.

Now if we look at the second part of the graphic, the line chart, we do see labelling for the axis here. But what I’m not fond of here is that the line for counties with large Trump shares, the line significantly exceeds the the maximum range of the chart. And then for the 0.5 deaths per 100,000 line, the dots mysteriously end short of the end of the chart. It’s not as if the line would have overlapped with the data series. And even if it did, that’s the point of an axis line, so the user can know when the data has exceeded an interval.

I really wanted to like this piece, because it is a graphic above the fold. But the more I looked at it in detail, the more issues I found with the graphic. A couple of tweaks, however, would quickly bring it up to speed.

Credit for the piece goes to Ashley Wu.

Covid Update: 22 September

It’s been a little over a week now since my last update on Covid-19 in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. So where do we stand now, especially since last week we had seen a split with some good news and some not so good news?

Well let’s start with where we had good news last week: Illinois and New Jersey. In those two states we had the clearest evidence of the fourth wave peaking and beginning a slow descent.

New case curves for PA, NJ, DE, VA, & IL.

This week we can see that in Illinois the peak really does appear to have been reached as the seven-day average for new cases has been heading down slowly over the last week or so. In New Jersey we saw a sort of false peak, because new cases began to rise again not long after I posted. And with it the seven-day average did as well. However, in the last few days, the seven-day average has flattened ever so slightly, though it is still increasing.

Delaware is a bit harder to judge. When I last posted the seven-day average sat at 457 new cases per day. Yesterday? 454 new cases per day. If you look at the chart, you can see there was a brief spike that I had noted as a potential indicator of a peak for Delaware. After that brief decline however, you can see how the curve shot back up again, exceeding the earlier peak with an average of 470 new cases per day before cooling off slightly. New cases have been increasing for the last four days, but they are still below that 470 new cases number.

Virginia’s fourth wave long looked the worst. You can see some aberrant declines and spikes due to the extra day holiday in reporting—recall Virginia does not publish its weekend data. Since then however, there are some initial indications that Old Dominion may have peaked. Consider that when I last posted, the seven-day average sat at 4700 new cases per day. But over the last nine days, the average dropped to the 3600s for six days, then the 3500s for two days, and yesterday the average fell into the 3400s. That is the kind of flattening we want to see if there is a real peak.

Finally we have Pennsylvania. Right before Labour Day we had evidence of a slowing outbreak. But then after the holiday, new cases began to climb sharply. There was then a quick slowdown, but ever since we’ve continued to see rising numbers of new cases in the Commonwealth. At the time of my last post we had an average of 4100 new cases per day. Yesterday that was at 4700.

Pennsylvania looks like the only state we cover here that is clearly moving in the wrong direction.

But what about deaths?

Death curves for PA, NJ, DE, VA, & IL.

Well, here it’s almost all bad news. Before we can reasonably expect deaths to begin to slowdown, we need to see the spread of new cases slowdown. Remember that deaths are a lagging indicator as it can take weeks from infection to hospitalisation to death. And if most of our states have not yet clearly peaked, we shouldn’t really expect deaths to have peaked yet.

Here the only good news is Illinois where deaths peaked at 41 per day, but have since fallen to 31. Compare that to the shape of the curve in the new cases chart. We can clearly see the peak in new cases being followed by sometime by the peak in deaths.

In all the other states, however, we continue to see climbing numbers of deaths. In Pennsylvania over the last nine days we’ve seen the average climb from 24 deaths per day to 43. New Jersey increased a bit more slowly, from 13 to 19. And Delaware, again due to its small size, climbed, but only from 1.1 to 2.6. And in Virginia, we’ve seen the average number of deaths climb from 20 to 34.

If we are nearing peaks in New Jersey and Virginia, we should begin to see deaths cool down in the near future. The same holds true for Delaware, but there we have less evidence of a peaking outbreak.

Credit for the piece is mine.

Covid Update: 13 September

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.

New case curves for PA, NJ, DE, VA, & IL

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.

Death curves for PA, NJ, DE, VA, & IL.

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.