No two rivers are the same, though they certainly can be similar. Rivers have their own ecosystems and when I was at school, I learned of the different classifications of rivers by the colour of their water: black, white, and clear. Broadly speaking, that just means the amount of sediment dissolved in the river’s water. Black colours appear when slow moving water has absorbed lots from its environment, think swamps. White waters resemble tea or coffee with added milk or cream. This happens when sediments enter and dissolved into the water. Clear water is that, relatively clear and free of sediment.
But a team of scientists at University of North Carolina at Chapel Hill (UNC Chapel Hill) recently released some work where they used shifts in blue to yellow and green to help classify rivers. Their classification differs, but broadly can point to a change from healthy (blue) to unhealthy (yellow and green). The novelty in their work, however, focuses on using satellite imagery to capture the colour of rivers and their evolution since the mid 1980s.
A look at the broader lower-48 of the United States
They published their findings as an interactive application driven primarily by a clickable map. Clearly not all rivers are available, but a large number are, and you can see some obvious patterns at a national scale—their work excludes Alaska and Hawaii. If blue represents healthy rivers, we see healthy rivers in New England and the Pacific Northwest with a host of green rivers in the Mid-Atlantic and Upper Midwest with yellow in the Mississippi basin and southeast.
I wanted to look at Pennsylvania a bit more specifically given my familiarity with the Commonwealth and zoomed in a bit on the map.
The colour of Pennsylvania’s rivers
You can see that using that above scale, Pennsylvania’s rivers are in okay, not great state. Some of the upper stretches of the Delaware and Susquehanna Rivers are coloured blue, but we mostly see a lot of green.
To the right of the map, the designers placed three smaller charts driven by the user’s selection of river. Let’s take a look at the Juniata River as an example—my grandfather grew up living alongside a tributary that emptied into the Frankstown Branch just a short walk from his house.
A look at the Schuylkill River south of the Fairmount Water Works
We can see that the chart on the upper right shows the colour shift over the decades for that observed section of the river. The legend provides the information that the section of the river has shifting blue—gotten healthier—and then below it looks for any seasonal changes. Here the chart is grey, indicating the system lacks enough data for a clear trend. This examines the short changes we might see in a river based on seasonal effects like rainy season, dry season, and human-driven effects—perhaps we pollute more in the spring and then use rivers recreationally in the summer.
Finally a distribution of the river section’s colour, all in wavelengths of light.
My biggest critique here would be the wavelengths. Users likely will not the colour spectrum by wavelength, and adding some labels like blue, yellow, and green could go a long ways to help users understand at what they are looking.
Overall, though, this is a really fascinating project.
Today’s post is not about data visualisation per se, but rather an element of it: colour. Two weeks ago, the Times reported on the creation of a new artificially made pigment of the colour blue.
This screenshot from the article doesn’t do it justice. Click through to see the large photo.
You can read the article for the full details, but the new pigment contains yttrium, indium, and manganese. Combine the symbols for those elements, Y, In, and Mn, and you have YInMn Blue. In particular, the colour exhibits permanence and thus does not fade, say when mixed with water.
And it’s non-toxic, because for those who don’t know, some of the most popular paint colours in history have turned out to be toxic. White paint? Made with lead. Some of your bright, rich reds? Turns out cadmium can kill. And with blues we often see cobalt or chromium as part of the mixture and, guess what, they’re both toxic. But not YInMn.
Last summer, the Environmental Protection Agency (EPA) approved the pigment for commercial use. And so we can begin to use it in oils and watercolour paints. (The EPA had approved its use for industrial purposes back in 2017. Check out this article for an image of the blue used to make an electric guitar.)
For data visualisation and design purposes, for web stuff, colours work differently. The blue in the screenshot above from the Times article, that is made by photons emitted by your computer or mobile phone. Whereas, when you view that pile of pigment in person, or a guitar body, or a painting—all in person—what you are seeing is the absorption and reflection of light waves striking the objects. What you see is the portion of the light wave that is reflected, i.e. not absorbed, by the object.
So it’s possible that we could see YInMn Blue as the basis for a paint used in printing and therefore tints of it used to make a choropleth map of freshwater availability. But if your work is strictly digital/web based, this probably won’t make too much of an immediate impact.
I missed last week’s posting on an update to Covid-19. Two weeks on from the last post, things in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois continue to improve, albeit with a few fits and starts. But the downward trend nonetheless can be seen in the new cases charts.
Consider that in the charts from two weeks ago, we saw downward slopes, but a look at the charts in the two weeks hence shows some blips.
Another thing to keep in mind is that a major snowstorm disrupted testing and vaccinating operations in the northeastern states of Pennsylvania, New Jersey, and Delaware. The storm, which also hit northern Illinois and Virginia, also likely impacted those states but to lesser degrees.
New cases curves for PA, NJ, DE, VA, & IL.
That means the downward trends in new cases could be slightly exaggerated in those states. Consequently, rebounds next week should be taken with a grain of salt. Indeed, Sunday’s data releases from the tri-state area were greater than we might normally see with weekend data.
When we at deaths, however, we see a more muddled picture.
Death curves in PA, NJ, DE, VA, & IL.
In states like Delaware and Virginia, the average death rate is now higher than it was two weeks ago. In New Jersey, the rate is down slightly, but after two weeks of it being largely up and so all in all, largely a wash. Instead, it’s only in Pennsylvania and Illinois where we any real improvements in the average death rate. Both states are down and look to continue heading down.
Finally, we look at vaccinations and the percent of state populations that have been fully vaccinated.
The fully vaccinated percentage of the populations of PA, VA, & IL.
Two weeks ago, Pennsylvania and Illinois had just reached 1%. Neither New Jersey nor Delaware is reporting similar data, so both those states remain outside our consideration set. But, all three remaining states—Pennsylvania, Virginia, and Illinois—are now over 2%. Pennsylvania reports at least 2.5%—the city of Philadelphia reports separately from the statewide Department of Health, but does not update its figures at the weekend and so is likely higher. Both Virginia and Illinois have reached 2.3% full vaccination.
Last Friday I shared an xkcd post about the relative smoothness of the Earth. This week he posted an illustration but a slightly different scale. You can see more of Earth’s jagged edges.
Gotta love the Star Trek reference. I’m betting he used the length of the Kelvin timeline Enterprise, which I personally dislike, as it’s significantly larger than the prime timeline Enterprise of Shatner and Nimoy.
With Covid-19, one of the big challenges we face is the rapid mutations in the viral genetic code that have produced several beneficial—from the virus’ standpoint—adaptations. Several days ago the New York Times published a nice, illustrated piece that showed just what these mutations look like.
Of course, these were not just nice illustrations of protein molecules, but the screenshot below is of the code itself and you can see how just a few alterations can produce subtle, but impactful, effects.
In a biological sense, these mutations are nothing new. In fact, humanity wouldn’t be humanity but for mutations. Rather we are seeing evolution play out in front of our eyes—albeit eyes locked in the same household for nearly a year now—as the virus evolves adaptations better suited to spreading and surviving in a host population.
The piece includes several illustrations, but begins with an overall, simplified diagram of the virus and where its genetic code lies. And then breaks that code down similar to a stacked bar chart.
Designers identify where in the code the different mutations occur and the type of mutation. Later on in the piece we see a map of where this particular variant can be found.
I might come back to that map later, so I won’t comment too much on it here.
But I think this piece does a great job of showcasing just what we mean when we talk about virus mutations. It’s really just a beneficial slip up in the genetic alphabet.
Credit for the piece goes to Jonathan Corum and Carl Zimmer.
Note: I wanted this to go up on Inauguration Day, but I had some server issues last week. And while I got everything back for Friday and Monday, I didn’t want to wait too long to post this. You’ll note at the end that I have questions about General Austin and whether he could be confirmed as Defence Secretary. Spoiler: He was.
Today is Inauguration Day and at noon, President Trump returns to being a citizen and Joe Biden assumes the office of the presidency. He comes to office with arguably the most diverse cabinet in American history supporting him and his agenda.
CNN took a look at that diversity with this piece, which uses an interactive, animated stacked bar chart.
The proposed cabinet vs. the US ethnic breakdown
I took a screenshot at the ethnic/racial diversity. At the top, each bar represents one member of cabinet who you can reveal after mousing over the bar. Below is a stacked bar chart showing the racial makeup of the United States. You can see how it does resemble, and in some cases exceeds, the diversity of the broader United States.
One thing to note, however, is that we see 26 members of Cabinet. Some of those are the heads of the big executive departments like Treasury and Defence. But I’m not certain everyone is technically a cabinet-level position, e.g. Celia Rouse, Chair of the Council of Economic Advisors. It could be that the position is being elevated to cabinet level like John Kerry’s role as climate envoy. And if I just missed the press announcement, that’s on me. But that could affect the overall numbers.
Regardless, the nominated cabinet is more diverse than the previous two administrations as the CNN piece also shows.
The proposed cabinet vs. the preceding inaugural cabinets
I should point out that usually an incoming administration usually has a few of its national security positions already confirmed or confirmed on the first day, e.g. Defence and State. However, the Republican Senate, obsessed with the lie of a fraudulent election, has only just begun to start the confirmation process. In fact, as of late last night, only Avril Haines has been confirmed by the Senate (84–10) for Director of National Intelligence.
Furthermore, almost every administration has one or two nominations that fail to pass the Senate. George W Bush had Linda Chavez, Barack Obama had Tom Daschle, and Donald Trump had Andrew Puzder, just to give one from each of the last three administrations.
With a 50–50 Senate, I would expect there to be a few nominees who fail to make it over the line. Austin could be one, there appears to be some bipartisan agreement that we ought not nominate recent military officials as civilian heads of said military. Another to keep an eye out for is Neera Tanden. She riles conservatives and angers Bernie Sanders supporters, so whether the Senate will confirm her as Director of the Office of Management and Budget remains an open question in my mind.
Credit for the piece goes to Priya Krishnakumar, Catherine E. Shoichet, Janie Boschma and Kenneth Uzquiano.
Last week we saw some indications that the recent surge was beginning to ebb in Pennsylvania, Delaware, and Illinois with the same in New Jersey, but to a slight degree less so. Only Virginia presented us with data that showed its surge continuing unabated.
So this week we have some generally good news to look at.
New case curves for PA, NJ, DE, VA, & IL.
The drop in Pennsylvania, New Jersey, and Illinois appears real and sustained. Even in Virginia, we are beginning to see some signs of a decline in new cases—albeit it after a week of record reports of new cases.
Of course we should also mention that even though we are seeing declines in new cases, in no state are we close to approach low levels of community spread. Things are still bad out there, but they have gone from catastrophic spread to merely a disaster. Illinois is probably the closest to reaching summer-like levels of viral spread.
Deaths, however, because they lag behind new cases, are just now beginning to show signs of ebbing.
Death curves in PA, NJ, DE, VA & IL.
If last week’s pattern with new cases was that we were seeing positive trends in four states, we can say this week we are seeing positive trends in deaths for the same four states. Virginia is, again, the outlier.
Though I would be remiss if I noted that the declines in deaths is not nearly as pronounced as in new cases. In Pennsylvania, the seven-day trend for new deaths has appeared to have crested. But in New Jersey, recent days have suggested the decline may not be as steady. Only in Illinois are we really seeing a sustained downward trend in deaths.
And Virginia just Saturday saw its seven-day trend reach another new record, over 50 deaths per day.
But what about vaccinations?
Firstly, we still only have data for the three states of Pennsylvania, Virginia, and Illinois. Secondly, keep in mind that I am looking only at people reported fully vaccinated, i.e. they have had both their shots—both Pfizer’s and Moderna’s vaccines require two shots.
Vaccination curves in PA, VA, & IL.
There’s not a lot to report on yet, other than that both Pennsylvania and Illinois reached the 1% threshold. I think that for most people, however, that you can begin to see their respective lines easing off the 0% baseline. Virginia lags behind those two states, however, with just 0.5% of its population reported as fully vaccinated.
I’m curious to see if I cannot find some additional/alternative data sources for New Jersey and Delaware next weekend. I don’t love the idea of mixing data sources, but after a few weeks, we haven’t really seen any improvements to the data sharing from those states.
That said, I should also note that the new US administration has identified data transparency as an issue—or the lack thereof—in the current vaccination programme and is working to develop national and state-level dashboards to inform the public.
At scale. Not quite as smooth as a billiards ball, as is often claimed. But still, with the majority of the Earth’s surface covered by water, the highest mountains of Everest and K2 make for mere fractions of differences in height relative to the Earth’s size.
But that did not stop xkcd from making a scale model of Earth.
Last week we saw that in the weeks after Christmas, new cases and deaths rebounded in the five states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. The question was how bad would things continue to get? Would these rebounds sustain themselves?
A week later we can see a glimmer of good news in that with new cases, these rebounds appear to have crested and are now ebbing back down. At least in four states.
In Virginia, unfortunately, we see that new cases continue to climb with a new record of nearly 10,000 cases reported late last week. More broadly, this is the dilemma that confronts the United States. We have states like Pennsylvania, Delaware, and Illinois where we are bringing the virus back to heel. But in other states like Virginia, things continue to get worse.
New Jersey is somewhere in the middle. It appears to just possibly be cresting with its average actually ticking higher the last few days despite falling daily new cases. We will need to see how the Garden State plays out over the course of this week.
When we look at deaths, we continue to see the grim numbers pile up.
Deaths, of course, lag new cases by 2–4 weeks, sometimes as many as six or longer. In most of our five states, the average rate of deaths appears to be cresting or peaking. In Pennsylvania the curve may have peaked. In Delaware, we have seen a plateau and in Illinois we see the best news of a resumed decline.
In both New Jersey and Virginia, however, we see deaths continuing to climb, and in some cases by significant amounts.
If cases really have peaked in some of these states over the last week, we may expect deaths to continue to rise over the course of this week before beginning to fall again.
I also want to add two new graphics today. I have been trying to figure out how to cover the vaccination programme of the five states. Unfortunately, they do not all report the same data in the same way.
The graphic that perhaps makes the most sense is the one that looks the emptiest at the moment.
In order to resume “normal” lives, we need to achieve herd immunity. When we reach that level, the virus starves of new hosts and dies out. Broadly speaking, we have two ways of achieving herd immunity.
Option 1, let the virus run rampant and takes its course through the population. The benefit is that society remains open and people can return to cafes, pubs, shops, and museums. The cost is that millions get sick and hundreds of thousands die. Sadly, this is the route taken by Sweden and, unofficially, the United States.
Option 2, vaccinate the population. The benefit here is that millions do not get sick and hundreds of thousands do not die. The cost is that in order to wait for a vaccine and vaccination we would need to close cafes, pubs, shops, and museums.
The reality is that we chose something between the two. In the initial months, after we (belatedly) recognised the threat of the virus, we shut down our economies and stayed home. We chose option 2. You can see in the state charts above how that quickly helped us curb the spread of new infections.
Unfortunately, then the Trump administration chose to follow option 1 and encouraged states to “reopen” their economies. And because we never got the virus fully under control, we sowed the seeds for the explosive growth this autumn and winter.
But the vaccines are now here and the best bet is to vaccinate the population. How many people do we need to vaccinate? The exact number depends upon the infectiousness of the virus. Measles, one of the most infectious viruses out there, requires near 100% vaccination rates to achieve herd immunity. Thankfully, this coronavirus is not as infectious as measles. Early estimates placed the range at 60–70%. But lately, some epidemiologists have indicated the true number may be higher. Dr. Fauci of the National Institutes of Health (NIH) has said the true number is likely 70–85%.
This is why the new strains of the coronavirus we have identified in South Africa and the United Kingdom worry folks. Both appear to be more transmissible than earlier strains. Neither strain appears to be more lethal in its own right—although more cases means more people will die—but this increased infectiousness could mean we need an ever higher level of herd immunity, which means more vaccinations. And we’re already seeing the anti-vaccination support rising to somewhere in the range of 15-20%, just the threshold we could perhaps tolerate with the higher herd immunity range.
So what about the chart?
As we begin vaccinations, some states are reporting the numbers of people in their state that have been fully vaccinated against the coronavirus. I plot those numbers here. Pennsylvania, Virginia, and Illinois do so. Unfortunately, neither New Jersey nor Delaware does. I only have one data point recorded for Virginia and Illinois, and so they are not plotted yet, but both fall below the level of Pennsylvania, which has reported 0.50% of its population fully vaccinated. I have added a bar to show the range of estimated herd immunity we need.
And that gets us to the second new chart, the number of total doses administered per day.
Functionally this resembles the usual two charts. We track the number of doses administered daily and then plot their seven-day average to smooth out any day-to-day blips. Of course this means almost the opposite of those two charts as we are tracking the progress of people who will be immune from the virus.
The catch is that with the current vaccines we need two shots for a full course of treatment and not all states break the data down with that level of granularity. Again, we are looking at Delaware and New Jersey as they provide only the total number of doses administered. Now that’s still helpful, but it doesn’t give us the most accurate picture of what is happening with vaccinations.
But in order to make things comparable across five states, I have decided to use that broader, total doses administered metric for Pennsylvania, Virginia, and Illinois. (Virginia and Illinois provide another headache in that it reports the daily number of people fully vaccinated, but does not break down the number of full vaccination doses.)
So what is this second chart showing us?
Well, we are seeing a slow, nearly steady growth in the number of vaccines administered. The problem is that we need to see steep, nearly exponential line charts here if we want to have any hope of returning to “normal” anytime soon. Reporting tells us that the federal government’s approach to the logistics of vaccine distribution has been…not great. (Although at this point, perhaps that should not surprise us.)
Until we see these second charts begin to show more exponential growth, the first charts of the number of people fully vaccinated will be far below that herd immunity threshold we need to see.
Covering the vaccines in addition to the virus is a bit more work, but I’m going to try and cover them both over the next several months as I have with the outbreak itself.
Earlier, I saw these two graphics floating around the Twitter. They each come from a major financial institution and attempt to place the voting (and non-voting members) of the Federal Open Market Committee (FOMC) on a spectrum of doves to hawks or slightly less dovish. The FOMC, part of the Federal Reserve system, sets interest rates for the US economy. Now, I’m being super simplistic here, but it’s broadly true. I should add, full disclosure, I presently work for the Federal Reserve Bank of Philadelphia.
The first graphic is from JPMorgan and plots in one-colour all the voting and non-voting members on a single axis from very dovish to somewhat less dovish. Thin black lines point to evenly spaced points on the axis and people are listed at each interval.
It’s a fairly simple approach, but effective. Nothing revolutionary here. What I find a bit odd is the line underneath the centre tick. What prompts that group to have what I’ll call a summary bar? Is it because Jay Powell, the chair of the Federal Reserve, is placed within that group? It’s a bit unclear.
Now keep in mind the classifications here, very dovish and somewhat less dovish, as we compare JPMorgan’s graphic to that of Bank of America.
The first thing that strikes me is the use of colour. Here we have a fairly straightforward divergent spectrum of red to blue. Along with other design elements, like typographic scale and contrast for the header, subhead, and labels, this piece strikes me as better designed and more polished.
But I still have questions.
Here we have dovish to hawkish. At the hawkish extreme, we have Esther George of Kansas City and Robert Kaplan of Dallas. In JPMorgan’s chart, both are grouped together as somewhat less dovish. But with Bank of America, they are decidedly hawkish. (Although with nine intervals, the Bank of America graphic has a bit more granularity than JPMorgan’s.)
So the biggest question, unfortunately left unanswered by each graphic, is what defines hawkish and somewhat less dovish? Just by words, they sound not at all alike. But both companies clearly place both individuals at the same end of the spectrum.
Part of the issue stems from the divergence point between red and blue. For most spectra of this type, that would be the demarcation between a committee member who is a dove or a hawk. But we have no similar separation for JPMorgan.
There is, however, one design element for Bank of America’s piece that I really like. My explanation of the FOMC at the top was a bit simplistic. Not every regional Federal Reserve president gets to vote every year. They rotate each year except for New York. These presidents get to vote alongside those on the Board of Governors.
In the graphic, note that everybody above the axis label is a member of the Board, i.e. they get to vote every year until their term expires. Below the axis we have the rotation schedule. Each line represents a bank president who can vote in a particular year. For example, the Philadelphia president, Patrick Harker, was a voting member on the committee in 2020, but falls off in 2021 and will not return to 2023. The Bank of America graphic captures this for each president very well.
I am a bit confused as to why some members, i.e. Kaplan and John Williams of New York, appear to sit between lines. I am unaware of any reasons why they would be between years.
Overall, I prefer the Bank of America piece. It more clearly presents the rotation element of the voting members of the FOMC. Yes, it has colours, but I’m confused as to why the demarcation between doves and hawks happens where it does. And why JPMorgan doesn’t describe anyone as a hawk. So while I prefer it, I think it could still use some additional information or context to make it clearer to readers.
Credit for the JPMorgan piece goes to a designer at JPMorgan.
Credit for the Bank of America piece goes to a Bank of American Global Research designer.
