I am a graphic designer who focuses on information design. My day job? I am the data visualisation manager for the Federal Reserve Bank of Philadelphia. (This blog is my something I do on my own time and does not represent the views of the Fed, blah blah blah legal stuff.) And with my main interest in information design—be it in the shape of clear charts, maps, diagrams, or wayfinding systems—I am fortunate that my day job focuses on data visualisation. Outside of work, I try to stay busy with personal design work. Away from the world of design, I enjoy cooking and reading and am interested in various subjects from history and geography to politics to science to the arts. And I allow all of them to influence my work.
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.
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.
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.
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.
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.
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.
Like many Americans I closely followed the outcome of yesterday’s historic vote by the House of Representatives to impeach President Trump for his incitement of an insurrection at the US Capitol in a failed coup attempt to overturn the 2020 election.
Words I still never thought I’d write describing an American election.
So at the end of the vote, I created this first graphic to capture the bipartisan nature of the impeachment. Ten Republicans broke ranks and voted with the Democrats. Keep in mind that in 2020, zero Republicans did the same. Justin Amash had by then resigned from the Republican Party and sat as an independent.
But I was also interested in how “courageous” these votes could be seen. Trump remains immensely popular with his base despite his attempt to overthrow the US government and keep himself in power. Did the Republicans who supported impeachment sit in districts won by Biden?
The answer? Not really. Two did: congressmen from New York and California. But a look at the other eight reveals they represent Trump-supporting districts.
To be fair, there are probably three tiers of seats in that group. Liz Cheney, the No. 3 Republican in the House, is in her own Trump-supporting seat as Wyoming’s at large representative. But four other Republicans have seats where Trump won by more than 10 points.
Three more Republicans are in seats I’d label competitive, but lean Republican.
Clearly the argument can be made that for most of these Republicans, it was not a politically safe choice to vote for impeachment. House seats will be redistricted this year for the 2022 midterms, but I’ll be curious to see how these Republicans fare in those redistricting proceedings and then in the ultimate elections thereafter.
I meant to publish this yesterday, but this piece also offers a reminder that the hardest part of a data-driven story is usually finding the data. I was unable to find a single source of data for all the numbers I needed by the time I switched on for work. And so this had to wait until last night when I found what I needed.
And of course upon waking up this morning I found a few new articles with the data and more recent figures.
Since 2016, Trump has made building a great, big, beautiful wall on the US-Mexican border his signature policy. Of course, most illegal immigrants cross the border legally at checkpoints and normal ports of entry. A significant number are people who overstay the limits on their visas. So the efficacy of a great, big, beautiful wall is really not that great.
He also claimed that he would make Mexico pay for it.
So as he prepares to leave office, Trump this week is going on something of a victory tour and touting up his administration’s successes. The first stop? Alamo, Texas to highlight his wall.
Let’s look at that wall and how much the administration has accomplished.
For context, the US border with Mexico is nearly 2000 miles long. As of 18 December, the administration had built 452 miles, less than a quarter of the border’s total length.
Crucially, most of that construction merely replaced sections of existing wall and fence scheduled for replacement. The total amount of new wall built, as of 18 December, totals about 40 miles.
The cost of that 452 miles? More than $15 billion.
The last time we checked in on Covid-19 in the states of Pennsylvania, New Jersey, Delaware, Virginia, and Illinois, things were peaking across the five states. As I said then:
If you look at the very tippy tip top of the curves in the other four states, we might just be seeing an inflection point.
And in the month since, my highly scientific term of “tippy point” appears to have been correct. New cases did begin to drop and by the start of the Christmas holiday we began to see real improvement. I should point out that deaths continued to rise, however, but we should expect that because deaths lag new cases by sometimes as many as four to six weeks.
So how are things now, a month hence?
Well as you can see with new cases, not great and getting worse. Pennsylvania, New Jersey, Delaware, and Illinois all bottomed out prior to the holidays, and since then have been rising. It speaks to a surge in new cases likely caused by gatherings centred on the holidays.
The good news—if you can call it that—is that in Pennsylvania and Illinois, whilst cases rebound, they have not yet reached their mid-December peak in Pennsylvania and mid-November peak in Illinois. It’s worth pointing out that Chicago and separately Illinois instituted lockdowns earlier than the other four states prior to the holidays. That may account for the more dramatic reduction in those states.
The bad news is that in New Jersey and Delaware, the rebounds have now surpassed the peaks we saw in mid-December and cases continue to climb with new daily records pointing towards escalation of new cases in those states.
But the really bad news is in Virginia, where the inflection point was there—note the little mini “W” at the top of the chart—but that new cases declined neither significantly long nor in significant numbers such that there was no real holiday decline. Instead, at best we could describe it as numbers paused for two weeks before resuming their upward trends.
How about deaths?
Again, fairly grim news here. A month ago we were talking about rising rates of deaths in all but Illinois. And in fact, Illinois is the only state where the death rate is significantly lower than what it was in mid-December.
In New Jersey and Virginia, we see two states where the rising death rate perhaps slowed, but it never really entered into decline. Pennsylvania and Delaware offer perhaps static death rates. In fact, Pennsylvania just yesterday surpassed its mid-December peak level.
But keep in mind that deaths lag new cases by somewhere between two to four weeks, sometimes longer. What this means is that with new cases now rebounding and in fact surpassing their peaks from a month ago, we can expect that the end of January and beginning of February could be particularly deadly.
The situation is dire in the United States and things are going to get worse before they get better.
Yesterday was maybe the last election day for the 2020 US General Election. (There are still a few US House seats yet to be called, most notably a contested race in upstate New York.) These were a pair of runoff elections in Georgia for the state’s two US Senate seats (one for a full, six-year term, the other to finish out the final two years of a retiring senator).
I spent most of the night eating pizza and tracking results. One thing that I keep tabs on (in the sense of open tabs in the browser) is the New York Times needle forecast. It has its problems, but I wanted to highlight something I think was new last night. Or, if it wasn’t, I didn’t notice it back in November.
Below the needle was a simple table of results.
In the past, the needle was a bit opaque and it consumed data and spat out forecasts without users having a sense of what was driving those forecasts. Back in November, there were a few instances where states published incorrect data—that they later fixed—and when the needle consumed it, the needle forecast incorrect results.
But now we have a clear record of what data the forecast consumed in the table below the needles. It’s fairly straightforward as tables go. But tables don’t have to be sexy to be clear and effective.
The table lists the time when the data was added, the number of votes added, the type of vote added, and then the actual data vs. what was expected. And ultimately how that changed the needle. This goes a long way towards data transparency.
Simple colour use, bright blues and reds, show when the result/data favoured the Republican or Democrat. Thin, light strokes instead of heavy black lines for rows and columns place the visual emphasis on the data. And smaller type for the timestamp places the less important data at a lower level of importance.
It’s just very well done.
Credit for the piece goes to Michael Andre, Aliza Aufrichtig, Matthew Bloch, Andrew Chavez, Nate Cohn, Matthew Conlen, Annie Daniel, Asmaa Elkeurti, Andrew Fischer, Will Houp, Josh Katz, Aaron Krolik, Jasmine C. Lee, Rebecca Lieberman, Jaymin Patel, Charlie Smart, Ben Smithgall, Umi Syam, Miles Watkins and Isaac White.