Last week two of the largest American freight railroads agreed to a merger with Union Pacific purchasing Norfolk Southern. Railroads have long played an important part in the history of the United States, from the Second Industrial Revolution to settlement and development of the West, through to the time zones in which we live and the laws on monopolies and corporations by which we live. Even in my own family history, my 3×great-grandfather was crushed between two rail cars and instantly killed whilst working for the Reading Railroad.
This merger, however, will create a truly transcontinental railroad spanning from the East Coast to the West Coast. Fortunately the New York Timescovered the news and included a graphic to showcase the new network—if approved by the Administration, which…really?…do we have any doubt?—with the Union Pacific in red and Norfolk Southern in orange.
The graphic is great. It need not be overly complex. With other railroads shown in light grey for context, the graphic highlights both the expansiveness and pervasiveness of the two networks. In a normally operating world, we probably would see significant concerns from the government about monopolistic concentrations of freight—we are talking about 40% of freight traffic here being controlled by one company. But this ain’t exactly Kansas. Except, it kinda is. Regardless, I doubt this merger gets held up.
A minor note, I appreciate that this map includes the state-level administrative units for both Mexico and Canada—Cuba too, but that is less important for this article. The only concern would be, I would probably have used a thicker white stroke for the international borders, because how many readers would know on the above map where the United States ends and “not the United States” begins…
Apologies, all, for the lengthy delay in posting. I decided to take some time away from work-related things for a few months around the holidays and try to enjoy, well, the holidays. Moving forward, I intend to at least start posting about once per week. After all, the state of information design these days provides me a lot of potential critiques.
Let us start with the news du jour , the application of tariffs on China and the delayed imposition on both Canada and Mexico. Firstly, let us be very clear what a tariff is. A tariff is a tax paid by importers or consumers on goods sourced from outside the country. In this case, we are talking about Canadian, Mexican, and Chinese imports and the United States slapping tariffs on goods from those countries. Foreign governments do not pay money to the United States, neither Canada, nor Mexico, nor China will pay money to the United States.
You will.
You should expect your shopping costs to increase, whether that is on the price of gasoline (imported from Canada), fast fashion apparel (from China), or avocados (from Mexico). On the more durable goods side, homes are built with Canadian lumber and your automobiles with parts sourced from across North America—the reason why we negotiated NAFTA back in the 1990s.
Now that we have established what tariffs are, why is the Trump administration imposing them? Ostensibly because border security and fentanyl. What those two issues have to do with trade policy and economics…I have no idea. But a few news outlets created graphics showing US imports from our top-five trading partners.
First I saw this graphic from the New York Times. It is a variation of a streamgraph and it needs some work.
A streamgraph type chart from the New York Times
To start, at any point along the timeline, can you roughly get a sense of what the value for any country is? No. Because there is no y-axis to provide a sense of scale. Perhaps these are the top import sources and these are their share of the total imports? Read the fine print and…no. These are the countries with a minimum of 2% share in 2024, which is approximately 75% of US imports.
This graphic fails at clearly communicating the share of imports. You need to somehow extrapolate from the y-height in 2024 given the three direct labels for Canada, Mexico, and China what the values are at any other point in time or for any other country.
Nevertheless, the chart does a few things nicely. It does highlight the three countries of importance to the story, using colours instead of greys. That focuses your attention on the story, whilst leaving other countries of importance still available for your review. Secondly, the nature of this chart ranks the greatest share as opposed to a straight stacked area chart.
Overall, for me the chart fails on a number of fronts. You could argue it looks pretty, though.
The aforementioned stacked area charts—also not a favourite of mine for this sort of comparison—forces the designer to choose a starting country in this case and then stack other countries atop it.
A stacked area chart from the BBC
What this chart does really well, especially well compared to the previous New York Times example is provide content for all countries across all time periods by the inclusion of the y-axis. Like the Times graphic it focuses attention on Canada, Mexico, and China with colour and uses grey to de-emphasise the other countries. You can see here how the Times’ decision to exclude all countries below 2% can skew the visual impact of the chart, though here all countries below Japan (everything but the top-five) are grouped as other.
Personally, the inclusion of the specific data labels for Canada, Mexico, and China distract from the visualisation and are redundant. The y-axis provides the necessary framework to visually estimate the share. If the reader needs a value to the precision level of tenths, a table may be a better option.
I could not find one of the charts I thought I had bookmarked and so in an image search I found a chart from one of my former employers on the same topic (though it uses value instead of share) and it is worth a quick critique.
A stacked area chart from Euromonitor International
Towards the end of my time there, I was creating templates for more wide-screen content. My fear from an information design and data visualisation standpoint, however, was the increased stretch in simple, low data-intensity graphics. This chart incorporates just 42 data points and yet it stretches across 1200 pixels on my screen with a height of 500.
Compare that to the previous BBC graphic, which is also 1200 pixels, but has a greater height of 825 pixels. Those two dimensions give ratios of 2.4 for Euromonitor International and 1.455 for the BBC. Neither is the naturally aesthetically pleasing golden ratio of 1.618, but at least the BBC version is close to Tufte’s recommended 1.5–1.6. The idea behind this is that the greater the ratio, the softer the slope of the line. This can make it more difficult to compare lines. A steeper slope can emphasise changes over time, especially in a line chart. You can roughly compare this by looking at the last few years of the longer time span in the BBC graphic to the entirety of this graphic. You can more easily see the change in the y-axis because you have more pixels in which to show the change.
Finally we get to another New York Times graphic. This one, however, is a more traditional line chart.
A line chart from the New York Times
And for my money, this is the best. The data is presented most clearly and the chart is the most legible and digestible. The colours clearly focus your attention on Canada, Mexico, and China. The use of lines instead of stacked area allow the top importer to “rise” to the top. You can track the rapid rise of Chinese imports from the late 1990s through to the first Trump administration and the imposition of tariffs in 2018—note the significant drop in the line. In fact you can see the impact in Mexico becoming the United States’ top trading partner in recent years.
Over the years, if I had a dollar for every time I was told someone wanted a graphic made “sexier” or with more “sizzle” or made “flashier”, I would have…a bigger bank account. The issue is that “cooler” graphics do not always lead to clearer graphics. Graphics that communicate the data better. And the guiding principle of information design and data visualisation should be to make your graphics clear rather than cool.
Credit for the New York Times streamgraph goes to Karl Russell.
Credit for the BBC graphic goes to the BBC graphics department.
Credit for the Euromonitor International graphic goes to Justinas Liuima.
Credit for the New York Times line chart goes to the New York Times.
Sometimes in the course of my work I stumble across graphics and work that I previously missed. In this case I was seeking a post about one of my favourite infographics, but it turned out I’ve never posted about it and so I will have to rectify that someday. However in my searching, I came upon an article from the New York Times last year where they wrote about research from MIT that compared the carbon dioxide emissions—bad for the environment and climate—per mile to the average monthly cost of a wide range of 2021 vehicles. The important distinction here is that average monthly cost is not the sticker price of a vehicle, but rather the sticker price plus lifetime operating costs. (For their analysis, the authors assumed a 15-year lifespan and 13,000 miles driven per year.)
Why is this so important? It’s pretty simple, really. In the United States, vehicle emissions are the largest source of carbon emissions. And the vast majority of that is due to passenger vehicles. If we as a society want to get serious about reducing our carbon footprint, the biggest changes we need to make are reducing our amount of driving, moving more people into mass transit, or switching out people’s gas-powered vehicles for electric vehicles.
The New York Times turned their work into a really nice static datagraphic. It is static, so there is no real interactivity if you want to compare your vehicle to others. However, the designers did choose some popular models and identified some of the key outliers.
There are nice annotations here that double their effort as a legend here.
The designers group the cars, represented by dots, into colour fields. These do a good job of showing how there is overlap between the different types of vehicles. Not all hybrid and plug-in vehicles are cheaper or even less CO2 emitting than some gas-powered vehicles, typically your smaller compacts and hatchbacks. Each colour field is linked to a textual annotation that also functions as a legend.
That alone is very helpful in understanding the differences, subtle and not-so-much, between the types of vehicles. Later on in the article the designers also used a scatter plot of a narrower set of data to compare a select set of vehicles.
Oh, there’s your Tesla.
Here we can see that one cannot simply assume that all electric vehicles are cheaper long-term than their gas-powered compatriots. Here we can see that the Nissan Altima, whilst emitting more CO2, compares favourably with the Tesla Model 3 in both the long-term cost but also in the upfront sticker price.
Despite finding this article a year and a half late, we can tie this to current events in that President Biden’s climate bill creates tax credits for electric vehicles. While the bill is perhaps not as significant as many would like, it is remarkable for still being a lot of money devoted to reducing our emissions. And when it comes to electric vehicles, one of the key components is the creation of tax credits. These would help mitigate those upfront sticker costs of electric vehicles. Because whilst they may generally be cheaper in the long-run, you still need to put up more money than their conventionally-powered alternatives either as lump sums or down payments. And with interest rates rising, what you need to cover via an auto loan will become more expensive.
Overall this is a really nice piece. Should I ever need to buy another vehicle, I would love to see this as a resource available to the general public. Unfortunately it only compares 2021 vehicles. And it does make me wonder where my 2005 vehicle compares. Probably not too terribly favourably.
I had something else for today, but this morning I opened the door and found my morning paper. Nothing terribly special. No massive headline. No large front-page graphic. See what I mean?
But then as I bent down to pick it up, I spotted a little tree map. But it turned out it wasn’t a tree map. It was a rectangle, largely, but it was actually a county map of Kansas. It was so small it fit within a single column.
The map showed those counties that had a majority vote in favour of keeping abortion rights. And then those counties that also voted for Trump in 2020 were outlined in orange—a good colour pairing. Turned out a number of counties did.
Without wading into the politics of it, because that’s a separate article, this was a great little map. It didn’t need to be crazy complicated or even large.
Credit for the piece goes to the New York Times graphics department.
For my readers in the northern hemisphere, which is the vast majority of you, we are in the middle of meteorological summer, the dog days. And whilst my UK and Europe readers continue to bake under temperatures greater than 40ºC (104ºF), the northeast United States and Philadelphia in particular is looking at a heatwave starting today that’s forecast to peak at a temperature of 38ºC (100ºF) this weekend and a heat index reaching 41ºC (106ºF) tomorrow.
Not cool.
Yesterday we examined a completely different topic, property tax increases in Philadelphia, but we contrasted that work with a heat index map from the New York Times. With the heatwave beginning this afternoon, however, it seemed apropos to revisit that contrasting article.
It begins with the map that we looked at yesterday. Of course yesterday was Tuesday. Today is Wednesday, and so you can already compare these two maps to see how and where the heat has shifted. Spoiler: the Southeast and Midwest.
Definitely not cooler.
It does so with a nice simple three-colour unidirectional spectrum from a light yellow to a burnt orange. And you can see the orange spreading up from the Gulf Coast and along the southeastern Atlantic Seaboard.
For those not familiar, the heat index is basically what the air “feels like” taking into consideration the actual temperature and the relative humidity in the air. Humans cool themselves via perspiration and when the air is excessively humid our ability to perspire decreases and thus the body begins to run hotter. Warmer temperatures allow the atmosphere to increase the amount of moisture it can contain and you can see all that Gulf and subtropical moisture carrying itself into the hot air moving up from the south.
Very not cool.
The piece also offers a look at the forecast for the heat index, showing the next six days. These small multiples allow the reader to see the geographic progression of the heat. Whereas today will be particularly for parts of the Midwest in southern Illinois and Indiana, tomorrow will see the worst for the Eastern Seaboard. Luckily the heat index retreats a bit, though as I noted above, the temperatures will continue to rise until Sunday, meaning higher temperatures, but lower relative humidity. For Philadelphia in particular we talking about 50% relative humidity tomorrow and only 35% on Sunday. That makes a big difference.
The not coolest.
Overall this is a great piece despite the content.
Personally, I just can’t wait until summer.
Credit for the piece goes to Matthew Bloch, Lazaro Gamio, Zach Levitt, Eleanor Lutz, and John-Michael Murphy.
The other day I was reading an article about the coming property tax rises in Philadelphia. After three years—has anything happened in those three years?—the city has reassessed properties and rates are scheduled to go up. In some neighbourhoods by significant amounts. I went down the related story link rabbit hole and wound up on a Philadelphia Inquirerarticle I had missed from early May that included a map of just where those increases were largest. The map itself was nothing crazy.
A pretty standard map here.
We have a choropleth with city zip codes coloured by the percentage increase. I was thrown for a bit of a loop as I immediately perceived the red representing lower values and green higher values, the standard green to red palette. But given that higher values are “bad”, I can live red representing bad and sitting at the top of the spectrum.
I filed it away to review later, but when I returned I visited on my mobile phone. And what I saw broadly looked the same, but there was a disconcerting difference. Take a look at the legend.
One little difference…
You can see that instead of running vertically like it did on the desktop, now the legend runs horizontally across the bottom. In and of itself, that’s not the issue. Though I do wonder if this particular legend could have still worked in roughly the same spot/alignment given the geographic shape of Philadelphia along the Delaware River.
Rather look at the order. We go from the higher, positive values on the left to the negative, lower values on the right. When you read the legend, this creates some odd jumps. For example, we move from “+32% to +49%” then to “+15% to +31%”. We would normally say something to the point of the increase bins moving from “+15% to +31%” then to “+32% to +49%”. In other words, the legend itself is a continuum.
The fix for this would be to simply flip the running order of the legend. Put the lower values on the left and then step up to the right. For a quick comparison, I visited the New York Times website and pulled up the first graphic I could find that looked like a choropleth. Here we have a map of the dangerous temperatures across the United States.
Definitely staying inside today.
Note how here the New York Times also runs their legend horizontally below the graphic. But instead of running high-to-low like in the Inquirer, the Times runs low-to-high, making for a more natural and intuitive legend.
This kind of simple ordering change would make the Inquirer’s map that much better.
Credit for the Inquirer piece goes to Kasturi Pananjady and John Duchneskie.
Credit for the Times piece goes to Matthew Bloch, Lazaro Gamio, Zach Levitt, Eleanor Lutz, and John-Michael Murphy.
Editor’s note: I was having some technical issues last week. This was supposed to post last week.
Editor’s note two: This was supposed to go up on Monday. Still didn’t. Third time’s the charm?
Yesterday I wrote about a piece from the New York Times that arrived on my doorstep Saturday morning. Well a few mornings earlier I opened the door and found this front page: a map of the western United States highlighting the state of New Mexico.
That doesn’t exactly look like a climate I’d enjoy.
Unlike the graphic we looked at yesterday, this graphic stretched down the page and below the fold, not by much, but still notably. The maps are good and the green–red spectrum passes the colour blind test. How the designer chose to highlight New Mexico is subtle, but well done. As the temperature and precipitation push towards the extreme, the colours intensify and call attention to those areas.
Also unlike the graphic we looked at yesterday, this piece contained some additional graphics on the inside pages.
Definitely not a place where I want to be.
These are also nicely done. Starting with the line chart at the bottom of the page, we can contrast this to some of the charts we looked at yesterday.
Burn, baby, burn.
Here the designer used axis lines and scales to clearly indicate the scale of New Mexico’s wildfire problem. Not only can you see that the number of fires detected has spiked far above than the number in the previous years back to 2003. And not only is the number greater, the speed at which they’ve occurred is noticeably faster than most years. The designer also chose to highlight the year in question and then add secondary importance to two other bad years, 2011 and 2012.
The other graphics are also maps like on the front page. The first was a locator map that pointed out where the fires in question occurred. Including one isn’t much of a surprise, but what this does really nicely is show the scale of these fires. They are not an insignificant amount of area in the state.
Pointing out where I really don’t want to be in New Mexico.
Finally we have the main graphic of the piece, which is a map of the spread of the Calf Canyon and Hermits Peak fire, which was two separate fires until they merged into one. The article does a good job explaining how part of the fire was actually intentionally set as part of a controlled burn. It just became a bit uncontrolled shortly thereafter.
Nope. Definitely not a place to be.
This reminded me of a piece I wrote about last autumn when the volcano erupted on La Palma. In that I looked at an article from the BBC covering the spread of the lava as it headed towards the coast. In that case darker colours indicated the earlier time periods. Here the Times reversed that and used the darker reds to indicate more recent fire activity.
Overall the article does a really nice job showing just what kind of problems New Mexico faces not just now from today’s environmental conditions, but also in the future from the effects of climate change.
Credit for the piece goes to Guilbert Gates, Nadja Popovich, and Tim Wallace.
Friday the Bureau of Labour Statistics published the data on the jobs facet of the American economy. Saturday morning I woke up and found the latest New York Times visualisation of said jobs report waiting for me at my door. The graphic sat\s above the fold and visually led the morning paper.
Almost out of the hole.
We have a fairly simple piece here, in a good way. Two sections comprise the graphic. The first uses a stacked bar chart to detail the months wherein the US economy lost jobs during the previous two and a half years. We can take a closer look in this second photo that I took.
But the recovery hasn’t been uniformly good for all.
Here we can see the stacked bars pile up with the most recent bars to the right. Some of the larger bars have labels stating the number of jobs either lost (top) or gained (bottom). I’m not normally a fan of stacked bar charts, because they don’t allow a reader to easily discern like-for-like changes. In this instance, the goal is to show how close all the little bits have come towards making up the three negative bars. Where I take issue is that I would prefer the designers used some sort of scale to indicate even a rough sense of how many jobs the various bars represent.
That issue crops up again to a slightly lesser degree with the bottom set of graphics. These compare the growth of hourly earnings and inflation both from February 2020. During the first few months of the pandemic and its recession, you can see earnings for those most directly impacted by shutdowns drop. But there is no negative scale accompanying the positive scale and that makes it difficult to determine just how far earnings fell for those in, say, leisure and hospitality.
The second part of the graphic works overall, however it’s just some of the finer design details that are missing and take away from the graphic’s overall effectiveness.
This all fits part of a larger trend in data visualisation that I’ve been noticing the last few months. Fewer charts seem to be using axes and scales. It’s not a good thing for the field. Maybe some other day I’ll write some things about it.
For this piece, though, we have an overall solid effort. Some different design decisions could have made the piece clearer and more effective, but it still does the job.
Credit for the piece goes to Ben Casselman, Ella Koeze, and Bill Marsh.
Yesterday I focused on the big graphic from the New York Times that crossed the full spread of the front/back page. But the graphic was merely the lead graphic for a larger piece. I linked to the online version of the article, but for this post I’m going to stick with the print edition. The article consists of a full-page open then an entire interior spread, all in limited colour. The remainder of the extensive coverage consists of photo essays and interviews that understandably attempt to humanise the data points, after all, each dot from yesterday represented one individual, solitary, human being. That is an important element of a story like this and other national and international tragedies, but we also need to focus on the data and not let the emotion of the story overwhelm our rational and logical analysis.
Sometimes it’s hard to realise we’re in the third year of this pandemic.
From a data visualisation standpoint the first page begins simply enough with a long timeline of the Covid-19 pandemic charting the number of absolute deaths each day. As we looked at yesterday, the absolute deaths tell part of the story. But if we were to have looked at the number of absolute cases in conjunction with the deaths, we could also see how the virus has thus far evolved to be more transmissible but less lethal. Here the number of daily deaths from Omicron surpassed Delta, but fell short of the winter peak in early 2021. But the number of cases exploded with Omicron, making its mortality rate lower. In other words, far more people were getting sick, but as far fewer were dying.
An interesting note is that if you take a look at the online version, there the designers chose a more stylised approach to presenting the data.
All the dots
Here they kept the dot approach and simply stacked and reordered the dots. However, I presume for aesthetic reasons, they kept the stacking loose dots and dropped all the axis lines because it does make for a nice transition from the map to this chart. But they also dropped all headings and descriptors that tell the reader just what they are looking at. These decisions make the chart far less useful as a tool to tell the data-driven element of the story.
There are three annotations that label the number of deaths in New York, the Northeast, and the rest of the United States. But what does the chart say? When are the endpoints for those annotations? And then you can compare the scale of the y-axis of this chart and compare it to the printed version above. A more dramatic scale leads to a more dramatic narrative.
This sort of visual style of flash and fancy transitions over the clear communication of the data is why I find the print piece more compelling and more trustworthy. I find the online version, still useful, but far more lacking and wanting in terms of information design.
The interior spread is where this article shines.
Just a fantastic spread.
From an editorial design standpoint, the symmetry works very well here. It’s a clear presentation and the white space around the graphic blocks lets that content shine as it should in this type of story. Collectively these pieces do a great job telling the story of the pandemic thus far across the nation. The graphics do not need a lot of colour and make do with sparse flash. Annotations call the reader’s attention to salient points and outliers.
Very nice work here.
From a content standpoint, I would be particularly curious if we have robust data for deaths by education level. Earlier this year I recall reading news about a study that said education best correlated to Covid cases, and I would be curious to see if that held true for deaths. Of course these charts do a great job of showing just how effective the vaccines were and remain. They are the best preventative measure we have available to us.
More really nice graphics
Here I disagree with the design decision of how to break down the states into regions. The Census Bureau breaks down the United States into four regions using the same names as in the graphic above. However, if you look closely at the inset map, you will see that Delaware, Maryland, and West Virginia in particular are included as part of the Northeast. (I cannot tell if the District of Columbia is included as part of the Northeast or South.)
Now compare that to the Census Bureau’s definition:
How the government defines US geography
If you ask me to include Delaware and Maryland as part of the Northeast, well, if you’re selling it, I’ll buy it. After all, just because the Census Bureau defines the United States this way does not mean the New York Times has to. Both are connected to the Northeast Corridor via Amtrak and I-95 and are plugged into the Megalopolis economy. Maybe the Potomac should be the demarcation between Northeast and South. But I struggle to understand West Virginia. Before you go and connect it to the Northeast, I would argue that West Virginia has far more in common with the Midwest geographically, economically, and culturally.
More critically, given this issue, it strikes me as a serious problem when the online version of the chart—with the aforementioned issues—does not even include the little inset to highlight this at best unusual regional definition.
Where would you place West Virginia?
And so while I have reservations about the data—how would the data have looked if the states were realigned?—the design of the line charts overall is good.
Again, I am talking about the print version, not that online graphic. I would argue that the above screenshot is barely even a chart and more “data art” or an illustration of data. Consider here, for example, that for the South we have that muted slate blue for the dots, but the spacing and density of the dots leads to areas of lighter slate and darker slate. But a lighter slate means more space between stacked dots and darker slate means a more compact design. A lighter colour therefore pushes the “edge” of the line further up the y-axis and artificially inflates its value, not that we can understand what that value is as the “chart” lacks any sort of y-axis.
Finally the print piece has a set of small multiples breaking down deaths by income in the three largest American cities: New York, Los Angeles, and Chicago. These are just great little charts showing the correlation between income and death from Covid, organised by Zip code.
But this also serves as a stark reminder of just how much better the print piece is over the online version. Because if we take a look at a screenshot from the online article, we have a graphic that addresses all the issues I pointed out earlier.
Why couldn’t the online article kept to this style?
I am left to wonder why the reader of the online version does not have access to this clearer and more accurate representation of the data throughout the piece?
To me this article is a great example of when the print piece far exceeds that of the online version. Content-wise this is a great story that needed to be told this weekend, but design wise we see a significant gap in quality from print to online. Suffice it to say that on Sunday I was very glad I received the print version.
Credit for the piece goes to Sarah Almukhtar, Amy Harmon, Danielle Ivory, Lauren Leatherby, Albert Sun, and Jeremy White.
This past weekend the United States surpassed one million deaths due to Covid-19. To put that in other terms, imagine the entire city of San Jose, California simply dead. Or just a little bit more than the entire city of Austin, Texas. Estimates place the number of those infected at about 80 million. Back of the envelope maths puts that fatality rate at 1.25%. That’s certainly lower than earlier versions of the virus, which has evolved to be more transmissible, but thankfully less lethal than its original form.
Sunday morning I opened the door to my flat and found the Sunday edition of the New York Times waiting for me with a sobering graphic not just above the fold, nor across the front page. No, the graphic—a map where each dot represents one Covid-19 death—wrapped around the entire paper.
Above the foldFull pageFull spread
You don’t need to do much more here. Black and white colour sets the tone simply enough. Of course, a bit more critically, these maps mask one of the big issues with the geographic spread of not just this virus but many other things: relatively few people live west of the Mississippi River.
Enormous swathes of the plains and Rocky Mountains have but few farmers and ranchers living there. Most of the nation’s populous cities are along the coast, particularly the East Coast, or along rivers or somewhat arbitrary transport hubs. You can see those because this map does not actually plot the locations of individual deaths, but rather fills county borders with dots to represent the deaths that occurred within those limits. That’s why, particularly west of the Mississippi, you see square-shaped concentrations of deaths.
A choropleth map that explores deaths per capita, that is after adjusting for population, shows a different story. (This screenshot comes from the New York Times‘ data centre for Covid-19.
A somewhat different story
The story here is literally less black and white as here we see colours in yellows to deep burnt crimsons. Whilst the big map yesterday morning concentrated deaths in the Northeast, West Coast, and around Chicago we see here that, relative to the counties’ populations, those same areas fared much better than counties in the plains, Midwest, and Deep South.
A quick scan of the Northeast and Mid-Atlantic states shows that only one county, Juniata in Pennsylvania, fell into the two worst deaths per capita bins—the deeper reds. Juniata County sits squarely in the middle of Pennsyltucky or Trumpsylvania, where Covid countermeasures were not terribly popular. No other county in the region shares that deep red.
Look to the southeast and south, however, and you see lots of deep and burnt crimsons dotting the landscape. This doesn’t mean people didn’t die in the Northeast, because of course they did. Rather, a greater percentage of the population died elsewhere when, as the policies enacted by the Northeast and West Coast show, they didn’t need to.
After all, injecting bleach was never a good idea.
