bar chart – Page 6 – Coffee Spoons

The May Jobs Report

Last Friday, the government released the labour statistics from April and they showed a weaker rebound in employment than many had forecasted. When I opened the door Saturday morning, I got to see the numbers above the fold on the front page of the New York Times.

What I enjoyed about this layout, was that the graphic occupied half the above the fold space. But, because the designers laid the page out using a six-column grid, we can see just how they did it. Because this graphic is itself laid out in the column widths of the page itself. That allows the leftmost column of the page to run an unrelated story whilst the jobs numbers occupy 5/6 of the page’s columns.

If we look at the graphic in more detail, the designers made a few interesting decisions here.

First, last week I discussed a piece from the Times wherein they did not use axis labels to ground the dataset for the reader. Here we have axis labels back, and the reader can judge where intervening data points fall between the two. For attention to detail, note that under Retail, Education and health, and Business and professional services, the “illion” in -2 Million was removed so as not to interfere with legibility of the graphic, because of bars being otherwise in the way.

My issue with the axis labels? I have mentioned in the past that I don’t think a designer always needs to put the maximum axis line in place, especially when the data point darts just above or below the line. We see this often here, for example Construction and Manufacturing both handle it this way for their minimums. This works for me.

But for the column above Construction, i.e. State and local government and Education and health, we enter the space where I think the graphic needs those axis lines. For Education and health, it’s pretty simple, the red losses column looks much closer to a -3 million value than a -2 million value. But how close? We cannot tell with an axis line.

And then under State and local government we have the trickier issue. But I think that’s also precisely why this could use some axis lines. First, almost all the columns fall below the -1 million line. This isn’t the case of just one or two columns, it’s all but two of them. Second, these columns are all fairly well down below the -1 million axis line. These aren’t just a bit over, most are somewhere between half to two-thirds beyond. But they are also not quite nearly as far to -2 million as the ones we had in the Education and health growth were near to -3 million.

So why would I opt to have an axis line for State and local governments? The designers chose this group to add the legend “Gain in April”. That could neatly tuck into the space between the columns and the axis line.

Overall it’s a solid piece, but it needs a few tweaks to improve its legibility and take it over the line.

Credit for the piece goes to Ella Koeze and Bill Marsh.

Covid Update: 9 May

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.

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.

Credit for the piece is mine.

Off the Axis

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.

Covid-19: A Global Update

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.

Covid Update: 2 May

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.

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.

Arrowheads

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.)

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.

Politicising Vaccinations

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.

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.

Covid Update: 18 April

Last week I wrote about how we may have been beginning to see divergent patterns in new cases, i.e. how New Jersey in particular had seen its new cases numbers falling whilst other states continued with increasing case counts.

One week later, that may still broadly hold true.

Emphasis on may.

If we look at the new charts, we can see that broadly, New Jersey did continue its downward trend as Pennsylvania and Delaware experienced significant rises in new cases. Virginia remained fairly stable, but with a slight trend towards increasing numbers of new cases.

But New Jersey and now Illinois present some interesting trends to watch this coming week. Illinois reminds me of New Jersey in that despite rising numbers most of last week, the last few days (and of course the weekend) saw numbers lower than preceding days. You can see from the slightest of dips at the tail of the line the trend has flipped direction. Will the direction hold, however, once we start receiving weekday reporting figures starting Tuesday?

Back to New Jersey, though. The downward trend continued most of the week. But, the last several days could portend a reversal of sorts. For most of the last week, the state saw daily new case numbers increasing day after day. But the trend line, as it should, remained heading downwards. Until just a few days ago. If you look at the tail of the line there, you will see a slight uptick. This too will be something to watch in the coming week.

Deaths also need careful attention this week.

Last I asked the question, will deaths follow rising cases? After a week of data, the answer is unmistakably yes. However, unlike new cases, the increases are largely of a marginal number. Look closely at the ends of the lines for Pennsylvania, New Jersey, Delaware, and Illinois and you will see last week’s shallow rise continued.

Virginia bucked the trend with decreasing numbers of deaths. And of course marginal increases could easily give way to marginal decreases. Now I try not to mention too many daily numbers in these posts because I take the weekly view, but I will be closely following Pennsylvania this week. For the last several weeks, the Commonwealth regularly reported deaths on Sunday and Monday in the single digits. Yesterday Harrisburg reported 40. Is this a one-day surge of reports? Is the state resuming reporting more deaths at the weekend? Or does it portend something worse, a mores significant rise in the number of deaths?

Vaccinations continue apace. Although, I would expect to see some slowdown as the Johnson & Johnson vaccine pause ripples out across the vaccination programme.

Fully vaccinated curves for PA, NJ, DE, VA, & IL.

For now though we continue to see increasing numbers. Indeed, the three states I track have now all reached or should reach today 25% of their population as fully vaccinated.

One, that is good news.

But, two, this is just the beginning.

Last week in some tense questioning about when we can expect resumption of “normal”, Dr. Fauci provided a figure of 10,000 new cases per day across the US. (Currently we are about at 60,000 or so.) Vaccines will impede the transmission as they become ever more widely administered and fully implemented—remember that a first dose of a two-dose regimen does not mean you should be heading out and socialising.

At present, we have Pennsylvania averaging 5,000 new cases per day. In other words, Pennsylvania alone represents half of Dr. Fauci’s target. We are clearly far from that reopening level.

What I will be curious about in the coming weeks though is that interplay between new cases and vaccinations. If Illinois does begin to see a downward trend in new cases this week, how much of it is due to the state being 25% fully vaccinated?

That’s a complex question to answer, but at some point, increasing vaccinations will force new cases to reach an inflection point. First they will begin to bend downward, increasing more slowly instead of exponentially. Then with even more vaccinations a second point will be reached at which this new surge begins to finally turn and new cases drop.

The question is when.

Credit for the piece is mine.

Covid Update: 11 April

This time last week I wrote about how we should not be surprised at rising levels of coronavirus in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. After all, our elected officials reopened economies despite data saying they should do otherwise. On top of that, people have been engaging in reckless behaviour and seemingly abandoning the very behaviours that had been leading to declining rates. With those two failures, our last hope is that vaccines will come quickly and be widely taken by the public.

A week hence.

Well, we are beginning to see some divergent patterns, especially with new cases.

Last week there was some evidence that New Jersey might be bucking the trend and headed downwards after weeks of rising new cases. And now that appears to be a more sustained trend as the line for the Garden State’s seven-day average clearly began headed the right direction this past week.

That’s the good news. The bad news is that we continue to see rising numbers of new cases in Pennsylvania, Delaware, and Illinois. Although if we want to try and find the positives in the bad, we can see that Delaware’s upward trend remains fairly shallow. Illinois, while steeper, is rising from a lower base as the Land of Lincoln managed to reach low, summer levels of new case spread earlier this year. And in Pennsylvania, there is a bend in the curve, an inflection point, that could indicate growth in the number of new cases is slowing. We still need to see it turn negative, but slowing growth is better than increasing growth.

Virginia splits the difference between those sets. It remains at an elevated level of new case transmission, but the upward tick we saw—unlike the other states—was not followed by a general surge in new cases. The little rise we did see, in fact seems to have perhaps shifted back downward.

One of the big questions in this current wave of new cases is will deaths rise? We are seeing increasing numbers of new cases and hospitalisations, but will deaths follow? The hope is that we have vaccinated enough of the most vulnerable populations to prevent them from suffering the most serious of results.

So far so good. While death rates remain slightly elevated over summer levels, we do not yet see any signs of rising numbers of deaths. The only possible exception is Virginia, where cases bottomed out after the state added delayed death certificates from the holidays, but have risen in recent days.

Finally we have vaccinations. Here is the best news at which we can look. We can now say that at least 20% of the populations of Pennsylvania, Virginia, and Illinois are fully vaccinated. To be clear, that is still a long way from herd immunity levels, but that’s 20 percentage points more than we had four months ago.

Total full vaccination curves for PA, VA, & IL.

One big outstanding question is how much, if at all, can vaccinated people spread coronavirus? This is why we need to continue to wear masks and socially distance even those who have been vaccinated. But at some point—I don’t know when—these increasing levels of full vaccination should begin to flatten the new case curves. Could that be what’s flattening the curves in New Jersey, Virginia, and Pennsylvania? It’s too early to say, but one can hope.

Credit for the piece is mine.

What Is Infrastructure?

This morning I read a piece in Politico Playbook that broke down President Biden’s $2.25 trillion proposal for infrastructure spending. A thing generally regarded as the United States sorely needs. $2.25 trillion is a lot of money and it’s a fair question to ask whether all that money is really money for infrastructure.

Because, it turns out, it’s not.

Please, sir, may I have more train money?

That isn’t to say money spent on job retraining or home care services wouldn’t be money well spent. Rather, it’s just not infrastructure.

But politics and the English language is a topic for another day. Oh wait, somebody already did write about that.

Credit for the piece is mine.