Last week I wrote about the deaths in Gaza and Israel, where a ceasefire is holding at the time of writing. But I also included a graphic about the size of Hamas’ rocket arsenal. In a social media post I commented about how it appeared Hamas had also changed its tactics given Israel’s Iron Dome missile defence system.
Specifically, in the past Hamas launched rockets at a fairly even pace. However, with Iron Dome, Israel could—and did—defend about 90% of incoming fire. Consequently, Hamas tried to swarm Israel’s defences and some fire did leak through, killing over a dozen Israelis. I was looking for data on that, but couldn’t find what I wanted.
Clearly I didn’t look hard enough. This graphic appeared in the Wall Street Journal last week. It shows the cumulative number of rockets launched at Israel during this most recent surge in violence compared to the 2014 war between Israel and Hamas.
A very different profile for Hamas’ attack
In 2014, you can see even, incremental steps up in the total count of rockets. But from earlier this month, you can see much steeper increases on a daily basis with more time between those swarms.
From a design standpoint, it’s a really nice graphic. I will often say that good graphics don’t need to be crazy or flashy. This is neither. It relies on solid fundamentals and executes well. All the axis lines are labelled and the data series fall within the bounds of the x- and y-axis. The colours chosen contrast nicely.
Credit for the piece goes to the Wall Street Journal graphics department.
Last week I wrote about how we were seeing new cases continuing to rapidly decline. This week we can say cases are still declining, but perhaps a bit less rapidly than earlier.
New case curves for PA, NJ, DE, VA, & IL.
The charts above show that slowdown in the tail at the right of the chart. First some points to note, Delaware reported that several hundred cases had not been entered into their database, and so we saw a one-time spike midweek. But note that after the spike, the numbers continue to trend down. In other words, the rapid decline was probably a bit less rapid than we saw, but it was still a decline.
Pennsylvania’s chart has a problem of your author’s own design. Now that I’m fully vaccinated I was able to leave the flat this weekend and the Pennsylvania data wasn’t ready by the time I left on Saturday. But by the Sunday data, it was and so the 2500 new cases is probably split somehow between those days—accounted for by the seven-day average. This points to a broader question for which I do not yet have an answer: as life increasingly returns to normal, how much longer will I continue to update these charts?
I started these graphics as a way for myself to track the spread of the virus in my home state and the state where I still have a large number of friends. At the time, there were few if any visualisations out there doing this. Now most media outlets have them and my work at home led to a similar project at work. The reason I continued to make these was you, my readers here and in other places where I post this work. Your comments, messages, texts, and emails made it clear you valued the work. First, I know there are still many people left to be fully vaccinated, nearly half the population, and due to bias, some of the people most likely to follow these posts are those most likely to get vaccinated as early as possible. But please let me know, readers, if you’re still getting value out of these graphics.
But back to the data, in two of the remaining three states, Virginia and Illinois, we saw numbers continue to decline. New Jersey, however, shows a tail with a slight uptick in the seven-day average of new cases. This will be something I follow closely this coming week.
Deaths finally appear to be dropping.
Death curves for PA, NJ, DE, VA, & IL.
Not by large numbers, no, but in Virginia and Illinois we saw declines of 5 deaths per day. Pennsylvania was even greater with a decline of 7. We are still above rates we saw last summer, but it does appear that finally we have hit the inflection point we have been waiting for the last several weeks.
Finally we have vaccinations. These charts look at the cumulative number of people fully vaccinated.
Fully vaccinated curves for PA, VA, & IL.
And in that the number keeps going up, and that’s good. But they can also only keep going up. But if you look closely at the right tail of the curve, you begin to see it flattening out as the rate of daily vaccinations begins to drop. Unfortunately we’re well below levels we think we’d need for herd immunity. But, to try and look at the positive, we’re almost halfway there and that is certainly playing a role as we can see with the rapid decline in numbers of new cases. But we need to keep trying to get more people vaccinated.
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.
New case curves for PA, NJ, DE, VA, & IL.
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.
Death curves for PA, NJ, DE, VA, & IL.
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.
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.
New cases curves for PA, NJ, DE, VA, & IL.
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.
Deaths curves for PA, NJ, DE, VA, & IL.
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.
Full vaccination curves for PA, NJ, DE, VA, & IL.
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.
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.
Need to see more downward trending lines.
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.
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.
Some not so great looking numbers across the globe.
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.
New case curves for PA, NJ, DE, VA, & IL.
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.
Death curves for PA, NJ, DE, VA, & IL.
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.
Total vaccination curves for PA, NJ, DE, VA, & IL.
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.
I don’t know if this is a trend, but I’ve now seen a few graphics appearing using arrows to show the direction or trend of the data. This graphic in an article by Bloomberg prompted me to talk about this piece.
I should add, after rereading my draft, that I’m not clear who made this graphic. I assume that it was the Bloomberg graphics team, because it appears in Bloomberg and all the data is presented to recreate the chart. But, it could also be a chart made by someone at Goldman Sachs that credits Bloomberg as a source and then someone at Bloomberg got hold of a copy. And a graphic made for a news/media outlet will typically be of a different quality or level of polish than one made perhaps by and for analysts. (Not that I think there should be said differences, as it does a disservice to internal users, but I digress from a digression.)
All the things going on in this chart.
The arrow here appears above the peak quarter, i.e. the second of 2021, for both the Goldman Sachs Economics forecast and the consensus forecast. But what does it really add? First, it adds “ink”, in this case pixels. Here, every pixel consumes our attention and there is a finite number of available pixels within the space of this graphic.
When I work with authors or subject matter experts, I often find myself asking them “what’s the most important thing to communicate?” or something along those lines. If the person answers with a long laundry list, I remind them that if everything is important, nothing is important. If everything is set in bold, all caps text, what will look most important is the rare bit of text set in regular, lower-case letters.
In the above graphic, there are so many things screaming for my attention, it’s difficult to say which is the most important. First, I’m fairly certain that “US QoQ annualised GDP growth” could move to the graphic subhead or data definition. Allow the graphic’s data container to contain, well, data. Second, the data series labels can be moved outside the data container. The labels here have an inherent problem is that the Goldman Sachs Economics numbers are in blue, and that blue text has less visual weight than the black text of the Consensus label. Consequently, the Goldman Sachs Economics label recedes into the background and becomes lost, not what you want from your legend.
Third, I don’t believe the data labels here add anything to the chart. They function as sparkly distractions from the visual trend, which should be the most important aspect of a visual chart.
Finally, we get to the arrow, the impetus for this post. First, I should note that it is not clear what growth it shows. The fact the line is black makes me think it reflects the Consensus forecast whereas a blue line would represent the Goldman Sachs forecast. But it could also be the average of the two or even a more general “here’s the general shape”. The problem is that the shape matters. If you look at the slope of the actual forecasts, you see a sharp increase to the peak followed by a slower, more gradual taper. The arrow in the original graphic shows a decelerating curve that is shallower in the lead up to the peak and that is not what is forecast to happen.
Now we get to the issue I mentioned at the top, the extraneous labelling and data ink wasted. If we look at the chart as is, but remove the arrow, we see this.
Immediately to the right of the peak, we have have some blue data labels and then just a bit to the right of that, but sitting vertically above the label we have the bold blue text labelling the data series. But further to the upper right we have a dark and bold block of text that draws the eye away from the peak and into the corner. It draws the eye away from the very element of the shape the peak needs to be a peak, the trough in the wave. Consequently, it makes sense with the eye being drawn up and to the right that the designers threw an arrow in above the peak to show how, no, actually your eye needs to go down and to the right.
But what happens if we then strip out the data series labelling? Do we still need the arrow? Let’s take a look.
I would argue that no, we do not. And so let’s strip the arrow out of the picture and take a look.
Here the shape of the curve is clear, a sharp rise and then a gradual taper to the right. No arrow needed to show the contour. In other words, the additional labelling wastes our attention, which then forces us to add an arrow to see what we needed to see in the first place, but then further wasting our attention.
There are a number of other things I take issue with in this chart: the black outlines of the blue rectangles, the tick marks on the x-axis, the solid border of the container, the lack of axis lines. But the arrow points to this graphic’s central problem, a poorly thought out labelling structure.
So because the chart provides all the data, I took a quick stab at how I would chart it using my own styles. I gave myself a 3:2 ratio, less space than the original graphic had. This is where I landed. I would prefer the legend below the chart labelling, but it felt cramped in the space. And with so few data points along the x-axis, the chart doesn’t need a ton of horizontal space and so I repurposed some of it to create a vertical legend space.
I mixed typefaces only because my default does not have a proper small capitals and I wanted to use small capitals to reduce and balance out the weight of the exhibit label in the graphic title.
I could still tweak the spacing between the bars and perhaps the treatment of the years below the quarters could use some additional work, but the main point here is that the shape of the curve is clear. I need no arrow to tell the user that there is a peak and that after the peak the line goes down. The white space around the bars and the line does that for me.
Credit for the piece goes to either the Bloomberg graphics department or the Goldman Sachs graphics department. Not sure.
Yesterday I wrote my usual weekly piece about the progress of the Covid-19 pandemic in the five states I cover. At the end I discussed the progress of vaccinations and how Pennsylvania, Virginia, and Illinois all sit around 25% fully vaccinated. Of course, I leave my write-up at that. But not everyone does.
This past weekend, the New York Times published an article looking at the correlation between Biden–Trump support and rates of vaccination. Perhaps I should not be surprised this kind of piece exists, let alone the premise.
From a design standpoint, the piece makes use of a number of different formats: bars, lines, choropleth maps, and scatter plots. I want to talk about the latter in this piece. The article begins with two side by side scatter plots, this being the first.
Hesitancy rates compared to the election results
The header ends in an ellipsis, but that makes sense because the next graphic, which I’ll get to shortly, continues the sentence. But let’s look at the rest of the plot.
Starting with the x-axis, we have a fairly simple plot here: votes for the candidates. But note that there is no scale. The header provides the necessary definition of being a share of the vote, but the lack of minimum and maximum makes an accurate assessment a bit tricky. We can’t even be certain that the scales are consistent. If you recall our choropleth maps from the other day, the scale of the orange was inconsistent with the scale of the blue-greys. Though, given this is produced by the Times, I would give them the benefit of the doubt.
Furthermore, we have five different colours. I presume that the darkest blues and reds represent the greatest share. But without a scale let alone a legend, it’s difficult to say for certain. The grey is presumably in the mixed/nearly even bin, again similar to what I described in the first post about choropleths from my recent string.
Finally, if we look at the y-axis, we see a few interesting decisions. The first? The placement of the axis labels. Typically we would see the labelling on the outside of the plot, but here, it’s all aligned on the inside of the plot. Intriguingly, the designers took care for the placement—or have their paragraph/character styles well set—as the text interrupts the axis and grid lines, i.e. the text does not interfere with the grey lines.
The second? Wyoming. I don’t always think that every single chart needs to have all the outliers within the bounds of the plot. I’ve definitely taken the same approach and so I won’t criticise it, but I wonder what the chart would have looked like if the maximum had been 35% and the grid lines were set at intervals of 5%. The tradeoff is likely increased difficulty in labelling the dots. And that too is a decision I’ve made.
Third, the lack of a zero. I feel fairly comfortable assuming the bottom of the y-axis is zero. But I would have gone ahead and labelled it all the same, especially because of how the minimum value for the axis is handled in the next graphic.
Speaking of, moving on to the second graphic we can see the ellipsis completes the sentence.
Vaccination rates compared to the election results
We otherwise run into similar issues. Again, there is a lack of labelling on the x-axis. This makes it difficult to assess whether we are looking at the same scale. I am fairly certain we are, because when I overlap the graphics I can see that the two extremes, Wyoming and Vermont, look to exist on the same places on the axis.
We also still see the same issues for the y-axis. This time the axis represents vaccination rates. I wish this graphic made a little clearer the distinction between partial and full vaccination rates. Partial is good, but full vaccination is what really matters. And while this chart shows Pennsylvania, for example, at over 40% vaccinated, that’s misleading. Full vaccination is 15 points lower, at about 25%. And that’s the number that needs to be up in the 75% range for herd immunity.
But back to the labelling, here the minimum value, 20%, is labelled. I can’t really understand the rationale for labelling the one chart but not the other. It’s clearly not a spacing issue.
I have some concerns about the numbers chosen for the minimum and maximum values of the y-axis. However, towards the middle of the article, this basic construct is used to build a small multiples matrix looking at all 50 states and their rates of vaccination. More on that in a moment.
My last point about this graphic is on the super picky side. Look at the letter g in “of residents given”. It gets clipped. You can still largely read it as a g, but I noticed it. Not sure why it’s happening, though.
So that small multiples graphic I mentioned, well, see below.
All 50 states compared
Note how these use an expanded version of the larger chart. The y-minimum appears to be 0%, but again, it would be very helpful if that were labelled.
Also for the x-axis in all the charts, I’m not sure every one needs the Biden–Trump label. After all, not every chart has the 0–60% range labelled, but the beginning of each row makes that clear.
In the super picky, I wish that final row were aligned with the four above it. I find it super distracting, but that’s probably just me.
Overall, this is a strong piece that makes good use of a number of the standard data visualisation forms. But I wish the graphics were a bit tighter to make the graphics just a little clearer.
Credit for the piece goes to Danielle Ivory, Lauren Leatherby and Robert Gebeloff.
