Happy Friday, all. Another week and we made it.
This Friday I want to highlight a graphic from xkcd that, strictly speaking, isn’t really data visualisation, but it does speak to that world because it’s about the underlying data.
And as with the best humour, there’s an element of truth in it.
Credit for the piece goes to Randall Munroe.
In 2014, what became known as little green men invaded Crimea, Ukraine. No, these were not aliens, but what we’ve later learned were unmarked Russian Army soldiers. They routed what little resistance Ukraine mustered in 2021 Crimea is de facto Russian, though de jure it remains Ukrainian.
Following Crimea, insurrections erupted in the Donbas, part of the mainland—yes, Crimea is connected through a thin strip of land, but in many ways it’s effectively not part of the mainland—bordering Russia. We suspect these too also included Russian Army regular, most notably the downing of Malaysian Airlines Flight 17. That civilian passenger airliner, filled with 298 people, was shot down by an SA-11 Gadfly, in use by the Russian Army across the border. But overall, the feeling is that in Ukraine, Russia uses paramilitary forces and private mercenaries, or at worst, soldiers “on holiday” to do Russia’s dirty work.
I covered the invasion of Crimea and the operations in Donbas extensively. Well, extensively for me.
In the years since, we’ve seen the emergence of the Wagner Group, a private mercenary group similar in concept to Blackwater. You have some fighting to do, they’ll do it for cash. But Blackwater, whose name has changed several times over the years, was largely staffed by ex-soldiers and had some infantry weapons to support them. Wagner Group is different.
And to see how different, you need only read this great BBC article that exposes some of the group’s details because of a tablet left behind by a Wagner mercenary. It is a bit of a lengthy read, but it’s well worth it. Wagner has been engaged/hired in Ukraine, Syria, and now Libya where it fights against the UN-backed government in Tripoli.
The data visualisation and information design here is mostly around forms and some illustrations of mines—the blow up and kill/main people kind, not the mineral extraction kind. But what sells the idea that Wagner is really more a shadowy appendage of the Russian state than some rogue private mercenary army are things like this document.
It, as the file photo hints, shows that Wagner is requesting a T-72 main battle tank for their operations in Libya. Blackwater committed crimes in foreign countries, but it never operated modern main battle tanks.
I also highlighted two other requisitions contained above the T-72. In purple we have an ask for two BTR-82s. These are more modern versions of the Soviet staple, BTR-80, a wheeled armoured personnel carrier. Then in orange we have a request for one BMP-2. This is a tracked infantry fighting vehicle.
In other words, Wagner is requesting the equipment necessary to field a scaled down version of a modern armoured division with heavy tanks and supporting infantry vehicles, both tracked and wheeled. It also contains requests for 120mm mortars, highlighted by the BBC. These are not things that a private mercenary army would have floating around a warehouse.
For this and other funding-related reasons, Wagner Group is increasingly seen as a part of the Russian government’s, i.e. Putin’s, foreign and security policy apparatus. The Russian state might not be able to be officially involved in Libya or Chad or the Central African Republic, where rumours abound of Russian-speaking mercenaries, but Wagner can because officially it doesn’t exist.
Credit for the piece goes to the BBC.
After twenty years out of power, the Taliban in Afghanistan are back in power as the Afghan government collapsed spectacularly this past weekend. In most provinces and districts, government forces surrendered without firing a shot. And if you’re going to beat an army in the field, you generally need to, you know, fight if you expect to beat them.
I held off on posting anything about the Taliban takeover of Afghanistan simply because it happened so quick. It was not even two months ago when they began their offensive. But whenever I started to prepare a post, things would be drastically different by the next morning.
And so this timeline graphic from the BBC does a good job of capturing the rapid collapse of the Afghan state. It starts in early July with a mixture of blue, orange, and red—we’ll come back to the colours a bit later. Blue represents the Afghan government, red the Taliban, and orange contested areas.
The graphic includes some controls at the bottom, a play/pause and forward/backward skip buttons. The geographic units are districts, sub-provincial level units that I would imagine are roughly analogous to US counties, but that’s supposition on my part. Additionally the map includes little markers for some of the country’s key cities. Finally in the lower right we have a little scorecard of sorts, showing how many of the nearly 400 districts were in the control of which group.
Skip forward five weeks and the situation could not be more different.
Almost all of Afghanistan is under the control of the Taliban. There’s not a whole lot else to say about that fact. The army largely surrendered without firing a shot. Though some special forces and commando units held out under siege, notably in Kandahar where a commando unit held the airport until after the government fell only to be evacuated to the still-US-held Hamid Karzai International Airport in Kabul.
My personal thoughts, well you can blame Biden and the US for a rushed US exodus that looks bad optically, but the American withdrawal plan, initiated by Trump let’s not forget, counted on the Afghan army actually fighting the Taliban and the government negotiating some kind of settlement with the Taliban. Neither happened. And so the end came far quicker than anyone thought possible.
But we’re here to talk graphics.
In general I like this. I prefer this district-level map to some of the similar province-level maps I have seen, because this gives a more granular view of the situation on the ground. Ideally I would have included a thicker line weight to denote the provinces, but again if it’s one or the other I’d opt for district-level data.
That said, I’d probably have used white lines instead of black. If you look in the east, especially south and east of Kabul, the geographically small areas begin to clump up into a mass of shapes made dark by the black outlines. That black is, of course, darker than the reds, blues, and yellows. If the designers had opted for white or even a light shade of grey, we would enhance the user’s ability to see the district-level data by dropping the borders to the back of the visual hierarchy.
Finally with colours, I’m not sure I understand the rationale behind the red, blue, yellow here. Let’s compare the BBC’s colour choice to that of the Economist. (Initially I was going to focus on the Economist’s graphics, but last minute change of plans.)
Here we see a similar scheme: red for the Taliban, blue for the government. But notably the designers coloured the contested areas grey, not yellow. We also have more desaturated colours here, not the bright and vibrant reds, blues, and yellows of the BBC maps above.
First the grey vs. yellow. It depends on what the designers wanted to show. The grey moves the contested districts into the background, focusing the reader’s attention on the (dwindling) number of districts under government control. If the goal is to show where the fighting is occurring, i.e. the contest, the yellow works well as it draws the reader’s attention. But if the goal is to show which parts of the country the Taliban control and which parts the government, the grey works better. It’s a subtle difference, I know, but that’s why it would be important to know the designer’s goal.
I’ll also add that the Economist map here shows the provincial capitals and uses a darker, more saturated red dot to indicate if they’d fallen to the Taliban. Contrast that with the BBC’s simple black dots. We had a subtler story than “Taliban overruns country” in Afghanistan where the Taliban largely did hold the rural, lower populated districts outside the major cities, but that the cities like the aforementioned Kandahar, Herat, Mazar-i-Sharif held out a little bit longer, usually behind commando units or local militia. Personally I would have added a darker, more saturated blue dot for cities like Kabul, which at the time of the Economist’s map, was not under threat.
Then we have the saturation element of the red and blue.
Should the reds be brighter, vibrant and attention grabbing or ought they be lighter and restrained, more muted? It’s actually a fairly complex answer and the answer is ultimately “it depends”. I know that’s the cheap way out, but let me explain in the context of these maps.
Choropleth maps like this, i.e. maps where a geographical unit is coloured based on some kind of data point, in this instance political/military control, are, broadly speaking, comprised of large shapes or blocks of colour. In other words, they are not dot plots or line charts where we have small or thin instances of colour.
Now, I’m certain that in the past you’ve seen a wall or a painting or an advert for something where the artist or designer used a large, vast area of a bright colour, so bright that it hurt your eyes to look at the area. I mean imagine if the walls in your room were painted that bright yellow colour of warning signs or taxis.
That same concept also applies to maps, data visualisation, and design. We use bright colours to draw attention, but ideally do so sparingly. Larger areas or fields of colours often warrant more muted colours, leaving any bright uses to highlight particular areas of attention or concern.
Imagine that the designers wanted to highlight a particular district in the maps above. The Economist’s map is better designed to handle that need, a district could have its red turned to 11, so to speak, to visually separate it from the other red districts. But with the BBC map, that option is largely off the table because the colours are already at 11.
Why do we have bright colours? Well over the years I’ve heard a number of reasons. Clients ask for graphics to be “exciting”, “flashy”, “make it sizzle” because colours like the Economist’s are “boring”, “not sexy”.
The point of good data visualisation, however, is not to make things sexy, exciting, or flashy. Rather the goal is clear communication. And a more restrained palette leaves more options for further clarification. The architect Mies van der Rohe famously said “less is more”. Just as there are different styles of architecture we have different styles of design. And personally my style is of the more restrained variety. Using less leaves room for more.
Note how the Economist’s map is able to layer labels and annotations atop the map. The more muted and desaturated reds, blues, and greys also allow for text and other artwork to layer atop the map but, crucially, still be legible. Imagine trying to read the same sorts of labels on the BBC map. It’s difficult to do, and you know that it is because the BBC designers needed to move the city labels off the map itself in order to make them legible.
Both sets of maps are strong in their own right. But the ultimate loser here is going to be the Afghan people. Though it is pretty clear that this was the ultimate result. There just wasn’t enough support in the broader country to prop up a Western style liberal democracy. Or else somebody would have fought for it.
Credit for the BBC piece goes to the BBC graphics department.
Credit for the Economist piece goes to the Economist graphics department.
In last week’s update we looked at how in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois the numbers of new cases of Covid-19 were trending in the wrong direction. This past week they continued to do much the same.
This week I want to begin with New Jersey, because last week I noted how the growth in the number of new cases was holding steady. In other words, the number of new cases, whilst growing, was growing by roughly the same number of cases each day. We contrasted that with the other four states where we witnessed increasing numbers of new cases each day.
New Jersey’s continued to see similar growth, fairly flat, though it has increased ever so slightly. And in the other states we continue to see increasing numbers of new cases, but that accelerating growth may be tailing off. That doesn’t mean we are seeing new cases decline—far from it. Instead we are seeing the number of new cases become slightly smaller each day. And if you look ever so closely at the tails of each chart above, you can see how the slope of the line, the seven-day average, is no longer bending upward but is straightening out to a line instead of a curve or, in some cases, maybe even beginning to flatten out as one does as one would approach a peak.
This doesn’t mean we are at the point of seeing this fourth wave peak, but the first indication of such a thing happening would be a slowdown in the numbers of new cases. And so moving forward over the next two weeks or so, we’ll want to see if that continues.
In absolute terms, I mentioned last week that I wouldn’t be surprised if Illinois surpassed its springtime seven-day average peak of 3390 new cases per day. Fortunately, we haven’t yet hit that milestone. We are, however, just under 200 new cases per day away from that. This can speak to that slight slowdown in the numbers of new cases.
We also looked at how in the tri-state area all three states were well below their springtime peaks. That continues to be the case. However, Delaware is nearing that peak.
When we look at deaths, we also see very much the same story as last week.
Delaware continues to be the exception where we saw deaths climb by just one. But when we look at the other four states, the concern last week was Illinois where we saw a significant jump in the rate. Fortunately that has slowed down over the past week and deaths climbed from 12 per day to just 13 per day. Similarly, the rate in Pennsylvania and Virginia has also slowed down slightly with 9 and 7 people dying each day in those states, respectively.
The good news is in New Jersey. There the death rate has slowed so much so that the average hasn’t changed. Last week it was 6 per day. As of yesterday’s data update, it’s still sitting on 6.
And we need to mention again that these deaths and the hospitalisations that we don’t track are almost all happening solely in the unvaccinated population. If you haven’t been vaccinated yet, you really need to. Because these vaccines have been proven safe; they’ve been proven effective; and they’re free if you’re worried about cost.
Credit for the piece is mine.
My regular readers will know that I am a fan of the Boston Red Sox, an American baseball team located in Boston, Massachusetts. I would consider myself a bit more involved than a casual fan in that I keep tabs on the team’s prospects.
For those unfamiliar with baseball, the sport works by keeping development pipelines of young talent fed through what we call a farm system. In essence a number of teams owned or contractually linked to the Major League team develop young players until they are ready to debut at the sport’s highest level.
Very few of total number of players in the system will ever get called up to “the Show”. In fact, in the history of the sport only 20,000 men have reached that level. Most of the rest will peak somewhere in the Minor Leagues. Most that reach the Majors will have been at some point prospects. And so to keep tabs on your team’s prospects and farm system sets one apart, in my mind, from the casual fan who simply knows a few of the team’s star players and enjoys a hot dog and a pint of beer at the stadium a few times a summer.
Red Sox fans are fortunate to have a website dedicated to coverage of Boston’s farm system, SoxProspects.com. They rank the system’s Top 60 prospects using their own methodology and research and publish the list online for fans like myself to enjoy.
Last week they updated their rankings. Long story short, the pandemic has impacted baseball and the development of young players. Consequently, the rankings changed significantly. What I really wanted to see was a visualisation of all the changes. So I took it upon myself to do just that using their data.
Now, if you also happen to be a Red Sox fan, I highly recommend their site. It’s fantastic. Normally I would take the train up to Trenton and see the Portland affiliate when it played there, but the Trenton team no longer exists. I’m not sure when I’ll get to see a Red Sox minor league team again. But hopefully sometime soon, because there look to be some good players coming up.
So I’ll be looking forward to, hopefully, a good run of contending teams in the coming years.
Credit for the piece is mine.
Happy Friday, all. We made it through another week of Covid, vaccinations, asteroids, and all that pleasant stuff. So let’s end with an upbeat note.
Over on YouTube there’s a channel I have long enjoyed, CCP Grey, who creates videos about, well lots of things, but sometimes really interesting historical, geographical, and political topics.
This week he released a video about Tiffany. As in the name Tiffany.
In addition to some great 80s aesthetics, the video touches on a couple of things that particularly interest me.
You see names are an important part of genealogical research. After all, almost all of us have names. (Some infants died without names.) Now in my family, on both my mother’s and father’s side I have a lot of Johns. In fact, I broke a line of five consecutive John Barrys. But occasionally a family will have a rarer or more uncommon name that allows you to trace that individual and therefore his or her family through time and space/place.
Grey tracks the history of the name Tiffany from its possible origin to some reasons for its popularity in the 1980s. And that includes some great graphics like this chart tracking the number of children with the name.
In the screenshot above you can see one thought he has on why the name took off in the latter half of the 20th century after languishing for centuries.
But he also examined the family history of one Tiffany and how that became important in the cultural zeitgeist. And to do so he used a family tree.
It’s a nine-minute long video and well worth your time.
I think what’s interesting to consider, however, is how this story could be told for many if not most names. There’s a reason they exist and how, by pure happenstance, they survive and get passed down family lines.
Though I have to say I did a quick search in my family tree and I have not a single Tiffany.
Credit for the piece goes to CCP Grey.
Of course the inside threat are those little bodies of coronavirus causing Covid-19. We cover them a lot here. But there are also threats from little bodies outside, way outside. Like asteroids impacting us. And that was the news yesterday when NASA announced improved data from a mission to the asteroid Bennu allowed it to refine its orbital model.
And we have reason to ever just so very slightly worry. Because there is a very slight chance that Bennu will impact Earth. In 2182. The New York Times article where I read the news included a motion graphic produced by NASA to explain that the determining factor will be a near pass in 2135.
Essentially, the exact course Bennu takes as it passes Earth in 2135 will determine its path in 2182. But just a few slight variations could send it colliding into Earth. Though, to be clear, it’s only a 1-in-1750 chance.
NASA used the metaphor of keyholes to explain the concept. The potential orbits in 2135 function as keyholes and should Bennu pass into the right keyhole, it will setup a collision with Earth in 2182. Hence the use of little keyholes in the motion graphic that accompanied the article.
But who knows, if we’re lucky the United Federation of Planets will still be formed in 2161 and the starship Enterprise will gently nudge Bennu back into a non-threatening orbit.
Credit for the piece goes to NASA.
Get your shots.
It’s pretty much that simple. But for just under half the country, it’s not getting through. So I went looking for some data on the breakdown of Covid-19 cases by vaccinated and unvaccinated people.
I found an analysis by the Kaiser Family Foundation (KFF), a non-profit that focuses on health and healthcare issues. They collected the data made available by 24 states—not all states provide a breakdown of breakthrough cases—and what we see across the country is pretty clear. If you want more details on their methodology, I highly recommend you check out their analysis.
In all but Arizona and Alaska, vaccinated people account for less than 4% of Covid-19 cases. In most of these states, it’s less than 2%. For the states that we regularly cover here—Pennsylvania, New Jersey, Delaware, Virginia, and Illinois—we have New Jersey, Delaware, and Virginia represented in the data set.
Delaware leads the three with vaccinated people accounting for just 1% of Covid-19 cases. Virginia is 0.7% and New Jersey is just 0.2%. In other words, in New Jersey almost nobody vaccinated is catching Covid-19 over the observation period.
And when we look at the vaccinated population, we can see what breakthrough events—cases, hospitalisations, and deaths—they are experiencing.
In almost all states, less than 0.5% of vaccinated people are getting Covid-19. Only in Arkansas do we see a number greater than that: 0.54%. In no state do we have more than 0.6% of vaccinated people requiring hospitalisation. And with that number so low, it won’t surprise you that in no state do we have more than 0.01% of vaccinated people dying.
In other words, the rapidly climbing numbers of new cases and slowly rising deaths that we looked at yesterday, that’s almost all in people who haven’t yet gotten vaccinated.
Get your shots.
Credit for the piece is mine.
Late last week I provided a brief update on the Covid-19 situation in Pennsylvania, New Jersey, Delaware, Virginia, and Illinois. Today I wanted to circle back to my statement that I’d update everyone again early this week. Of course, we had to wait until states began reporting their Monday data to get a better sense of where we are at in terms of new cases and deaths.
Spoiler: nowhere good.
Let’s start, as usual, with new cases.
We can see just from the tail end of the charts above that new case growth is accelerating in nearly all five states. Nearly because New Jersey’s growth has remained fairly constant, in other words the number of new people getting infected is not becoming larger each day but remaining relatively flat. That said, compared to 28 July, my last more thorough update, the seven-day average for new cases is still up by 66%.
In the other four states we see accelerating growth, i.e. the number of people infected grows daily. Virginia and Illinois are perhaps in the worst positions. Consider that earlier this spring during the Third Wave, Virginia peaked with a seven-day average of 1615 new cases per day. Yesterday the seven-day average reached 1625. This Fourth Wave is making more people sick now than they were in the spring. Illinois is not yet at the peak of its Third Wave, 3390 new cases per day, but yesterday the Land of Lincoln reached 2713. It’s not far from that ugly benchmark. Can Illinois’ seven-day average see an increase of about 600 new cases per day in a week? Consider that one week ago the average was at 1914. That’s an 800-new case increase. I would expect that if my next update is next Tuesday we will find Illinois in a worse position now than it was in this past spring.
What about the last two states of the tri-state area? Fortunately—for now—both Pennsylvania and Delaware remain below, roughly by half or so, their springtime peaks during the Third Wave. In part, that’s because—along with New Jersey—the Northeast has some of the highest rates of vaccination. But none of those states are near the levels we would need for herd immunity, especially given the increased transmissibility of the Delta variant.
In Pennsylvania the seven-day average for new cases is now just shy of 1500 new cases per day. Interestingly, if we halve the Monday data that includes both Sunday and Monday the daily numbers of new cases have declined for five consecutive days. I wouldn’t expect that trend to continue given the rampancy with which Delta is spreading throughout the Commonwealth, but that would be the signal in the data we would be looking for when this Fourth Wave breaks.
Delaware reports much the same. Cases are significantly up, but now so much so as to outpace the Third Wave. The First State’s seven-day average now sits at 185 new cases per day, but for the past four days the daily number has exceeded 200. Unlike Pennsylvania, that’s not the signal we would want to see to give us a sense the wave might be breaking.
What about deaths? Last week I mentioned we were seeing those numbers begin to creep back up despite falling during the initial weeks of the Delta wave.
The tail ends here, with the exception of Illinois, are far harder to see. In Illinois, on 28 July the seven-day average for deaths bottomed out at 4 deaths per day. Deaths have climbed ever since, tripling to 12 deaths per day. Prior to yesterday, the state had seen double-digit daily deaths for five consecutive days for the first time since early June. These are signs that deaths are heading in the wrong direction. But if we want to try and find a glimmer of hope, those deaths started at 18 on 4 August, but have dropped each day landing at 10 on 8 August and just 6 yesterday. Fingers crossed?
In the remaining states the picture is similar in that deaths are rising, but not nearly as badly as they are in Illinois. In Illinois the death rate tripled, but to be fair it also did so in Delaware. Though that meant climbing from 0.1 to 0.3. In the states where we are seeing deaths from Covid-19, the rates have not even doubled. Pennsylvania and New Jersey are the two closest to hitting that grim number. Their seven-day averages of 3.6 and 3.7, respectively, have reached only 6.6 and 6.4, respectively. Certainly not good, but perhaps we can be cautiously optimistic given the states’ relatively high rate of vaccination.
In Virginia we have seen the death rate climb from an average of 4.4 per day, nearly the same rate as Illinois, which has a lower overall rate of vaccination, to only 5.6 deaths per day as of yesterday.
It is important to note that vaccinations are doing a good job at keeping the vaccinated from needing to go to hospital or even dying. The phrase “pandemic of the unvaccinated” is very accurate. Whilst the vaccinated can become infected, most suffer very mild symptoms or are asymptomatic. The reason for masking is that the Delta variant can infect the vaccinated to such a degree that, whilst not sick, they can infect the unvaccinated.
If you have not been vaccinated yet, it is critical that you do so. They are safe. They are effective. And they are free. There are only a few valid reasons for not receiving the vaccination. And “not wanting it” or “not needing it” or “not trusting the government” or “not sure whence the virus came” are not valid excuses.
Earlier this morning (East Coast time) the Intergovernmental Panel on Climate Change (IPCC), the UN’s committee studying climate change, released its latest review of climate change. This is the first major review since 2013 and, spoiler, it’s not good.
I’ve read some news articles about the findings, but I want to critique and comment upon some of the graphics contained within the report itself. This started going too long, however, so I think I will break this into several shorter, more digestible chunks.
And I want to start with the first chart, two line charts that lay out the temperature changes we’ve seen.
One of the first things I like here is the language. Often we might see these or similar charts that simply state temperatures from the year 1 through 2020. One of the common reasons I hear from people that deny climate change is that “people weren’t recording temperatures back in 1 AD.
They would be correct. We do not have planet-wide meteorological observations from the time of Julius Caesar. But in the year 2021 we do have science. And that allows us to take other evidence, e.g. dissolved carbon dioxide in ice, or tree ring size, &c., and use them to reconstruct the temperature record indirectly.
And reconstruct is the word the IPCC uses to clearly delineate the temperature data pre- and post-1850 when their observed data set begins.
The designers then highlight this observed data set, broadly coinciding with the Industrial Revolution when we as a species began to first emit extra greenhouse gasses into the atmosphere. You can see this as a faint grey background and a darker stroke along the x-axis.
Additionally, the designers used annotations to call out the first main point, that warming in the last almost two centuries is far beyond what we’ve seen in the last two millennia.
The second annotation points to a bar, reminiscent of the range of a box plot, that exists outside the x-axis and almost embedded within the y-axis. This bar captures the range of temperatures reconstructed in the past 100,000 years. And by including it in the chart, we can see that we have just recently begun to exceed even that range.
In the second chart, we have the entire background shaded light grey and the whole x-axis in a darker stroke to remind us that we are now looking at the Industrial/Post-Industrial era. But what this chart does is do what scientists do, test whether natural, non-manmade causes can fully explain the temperature increase.
They can’t.
The chart plots the modelled data looking at just natural causes vs. modelled data looking at natural causes plus human impacts. Those lines and their ranges are then compared to the temperatures we’ve observed and recorded.
Since the 1930s and 40s, it’s been a pretty clear and consistent tracking with natural plus manmade causes. For years the scientific community has been in agreement that humanity is contributing to the rising temperatures. This is yet more evidence to make the point even more conclusively.
These are two really good charts that taken together show pretty conclusively that humanity is directly responsible for a significant portion of Earth’s recent climate change.
I’ll have more on some other notable graphics in the report later in the week, so stay tuned.
Credit for the piece goes to the IPCC graphics team.
