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For the past few years, David McCandless at Beautiful News Daily has been sharing infographics about positive developments in the world, the “stuff we can’t always see because we’re fixated on the negativity of the news”. Now all that good news has been bundled into a new book, Beautiful News: Positive Trends, Uplifting Stats, Creative Solutions. Here are a couple of sample pages from the book:
For his project For What It’s Worth, Dillon Marsh created 1:1 scale visualizations of the minerals extracted from South African mines and placed them in photos of the mines themselves. From top to bottom above, a sphere of the 284,000 metric tonnes of copper extracted from the O’Kiep mine, a sphere of the 9500 metric tonnes of gold from the Free State Gold Field, a sphere of 3850 metric tonnes of platinum extracted in total in South Africa since 1924, and a gemstone of the 7.6 million carats of diamonds extracted from the Koffiefontein Mine.
The diamond in particular, which you might not be able to see in that photo (it’s on a vertical stand right in front of the massive hole), underscores just how tiny the amount of material pulled out of these massive mines is, especially when you factor in all the manpower, machinery, injuries, fatalities, and environmental damage related to mining. All that for a bit of shine. (via clive thompson)
Throughout the relatively short recording career of The Beatles, and as late as 1967’s Magical Mystery Tour, the band’s albums released in the United States were, in some cases, quite different than those released in the UK. Jared Pike explains the differences in this comprehensive video:
Pike also made a chart of all the releases and which albums they appeared on, which you can download here. The image is too tall to include here, but here’s an excerpt that shows some of the reorganization that was done for American audiences:
I just looked on Spotify here in America and they’re using the UK versions of the albums (at least for Rubber Soul and Revolver). I wonder how long that’s been the case? According to Wikipedia, it looks like when the band’s albums were reissued on CD in 1987, the track listings were standardized to the UK/international release.
This is neat: Clive Thompson built a little app that converts tabular data into a bar chart using houses from the video game Townscaper. Says Thompson:
A few days ago I was placing houses in a long row, with varying heights. And when I looked at the jagged result I thought:
Hey, that looks like a bar chart!
That made me wonder, hmmm, could I use Townscaper as a dataviz tool? Could I write code that takes data and turns it into a row of buildings?
See also Dan Malec’s algorithmic Townscaper towns.
Well, I’m not sure this book could be any further up my alley; I mean:
The life and legend of New York City, from the size of its skyscrapers to the ways of its inhabitants, is vividly captured in this lively collection of more than 250 maps, cross sections, flowcharts, tables, board games, cartoons and infographics, and other unique diagrams spanning 150 years. Superstars such as Saul Steinberg, Maira Kalman, Christoph Niemann, Roz Chast, and Milton Glaser butt up against the unsung heroes of the popular press in a book that is made not only for lovers of New York but also for anyone who enjoys or works with information design.
The CDC recently released their report on Mortality in the United States, 2020 and this graph of US life expectancy at birth since 1950 by Christopher Ingraham dramatically summarizes the report’s main finding:
That’s a decrease in life expectancy of 1.8 years from 2019. Here are some more of the report’s significant findings:
In 2020, life expectancy at birth was 77.0 years for the total U.S. population — a decrease of 1.8 years from 78.8 years in 2019. For males, life expectancy decreased 2.1 years from 76.3 in 2019 to 74.2 in 2020. For females, life expectancy decreased 1.5 years from 81.4 in 2019 to 79.9 in 2020.
In 2020, the difference in life expectancy between females and males was 5.7 years, an increase of 0.6 year from 2019.
The age-adjusted death rate for the total population increased 16.8% from 715.2 per 100,000 standard population in 2019 to 835.4 in 2020. Age-adjusted death rates increased in 2020 from 2019 for all race-ethnicity-sex groups, increasing 42.7% for Hispanic males, 32.4% for Hispanic females, 28.0% for non-Hispanic Black males, 24.9% for non-Hispanic Black females, 13.4% for non-Hispanic White males, and 12.1% for non-Hispanic White females.
In 2020, 9 of the 10 leading causes of death remained the same as in 2019. The top leading cause was heart disease, followed by cancer. COVID-19, newly added as a cause of death in 2020, became the 3rd leading cause of death. Of the remaining leading causes in 2020 (unintentional injuries, stroke, chronic lower respiratory diseases, Alzheimer disease, diabetes, influenza and pneumonia, and kidney disease), 5 causes changed ranks from 2019. Unintentional injuries, the 3rd leading cause in 2019, became the 4th leading cause in 2020. Chronic lower respiratory diseases, the 4th leading cause in 2019, became the 6th. Alzheimer disease, the 6th leading cause in 2019, became the 7th. Diabetes, the 7th leading cause in 2019, became the 8th. Kidney disease, the 8th leading cause in 2019, became the 10th leading cause in 2020. Stroke, and influenza and pneumonia, remained the 5th and 9th leading causes, respectively. Suicide dropped from the list of 10 leading causes in 2020.
And from the report’s summary:
From 2019 to 2020, the age-adjusted death rate for the total population increased 16.8%. This single-year increase is the largest since the first year that annual mortality data for the entire United States became available. The decrease in life expectancy for the total population of 1.8 years from 2019 to 2020 is the largest single-year decrease in more than 75 years.
Since more people in the US died of Covid in 2021 than in 2020, I’d expect the decline life expectancy and the rise in death rate to continue.
From Max Roser at Our World in Data: Our history is a battle against the microbes: we lost terribly before science, public health, and vaccines allowed us to protect ourselves.
Science is the foundation for our success. 150 years ago nobody knew where diseases came from. Or more precisely, people thought they knew, but they were wrong. The widely accepted idea at the time was the ‘Miasma’ theory of disease. Miasma, the theory held, was a form of “bad air” that causes disease. The word malaria is testament to the idea that ‘mal aria’ — ‘bad air’ in medieval Italian — is the cause of the disease.
Thanks to the work of a number of doctors and chemists in the second half of the 19th century humanity learned that not noxious air, but specific germs cause infectious diseases. The germ theory of disease was the breakthrough in the fight against the microbe. Scientists identified the pathogens that cause the different diseases and thereby laid the foundation for perhaps the most important technical innovation in our fight against them: vaccines.
Here’s what vaccines did for us, in three charts:
Even among those who accept and understand how good vaccines are at stopping disease, it’s difficult to truly appreciate just how incredible and transformative they have been. By one estimate, vaccines saved between 150 & 200 million lives from 1980 & 2018…and that’s just for smallpox. Covid-19 vaccines have saved hundreds of thousands of lives in Europe and the US in the first year of their availability. Truly a miraculous invention.
Using data from Johns Hopkins, this time lapse video shows the spread of Covid-19 across the US from Feb 2020 to Sept 2021. This looks so much like small fires exploding into raging infernos and then dying down before flaring up all over again. Indeed, forest fire metaphors seem to be particularly useful in describing pandemics like this.
Think of COVID-19 as a fire burning in a forest. All of us are trees. The R0 is the wind speed. The higher it is, the faster the fire tears through the forest. But just like a forest fire, COVID-19 needs fuel to keep going. We’re the fuel.
In other forest fire metaphorical scenarios, people are ‘kindling’, ‘sparks being thrown off’ (when infecting others) and ‘fuel’ (when becoming infected). In these cases, fire metaphors convey the dangers posed by people being in close proximity to one another, but without directly attributing blame: people are described as inanimate entities (trees, kindling, fuel) that are consumed by the fire they contribute to spread.
See also A Time Lapse World Map of Every Covid-19 Death (from July 2020).
Peter Gorman of Barely Maps has published a wonderful little book called Kaleidoscope Brain that contains 100 visualizations of Moby-Dick. Gorman read Herman Melville’s masterpiece last year and made these maps & graphics to help him make sense of it.
I read Moby-Dick in April 2020. For weeks afterward, I couldn’t stop thinking about it. I started making maps and diagrams as a way to figure it out.
Moby-Dick is infamous for its digressions. Throughout the book, the narrator disrupts the plot with contemplations, calculations, and categorizations. He ruminates on the White Whale, and the ocean, and human psychology, and the night sky, and how it all relates back to the mystery of the unknown. His narration feels like a twisting-turning struggle to explain everything.
Reading Moby-Dick actually made me feel like that-like I’d mentally absorbed its spin-cycle style. I developed a case of “Kaleidoscope Brain.” The maps I was making were obsessive and encyclopedic. They were newer and weirder and they digressed beyond straightforward geography.
The book is available as a free download on Gorman’s Patreon — support his efforts if you find them valuable!
Above, from top to bottom: the letters of the alphabet in order of their appearance in the book, the constellation Cetus (aka “The Whale”), every color in the book.
It may seem like sometimes that with the pandemic, we’re back to square one. With the much more contagious Delta variant in play and an increasing number of breakthrough infections, the efficacy of these vaccines that we thought were amazing maybe aren’t? (Or maybe we just need to readjust our expectations?) But in terms of what these vaccines were specifically developed for — reducing & preventing severe disease and death — they are still very much doing their job. Just take a look at this graph from a White House Covid-19 press briefing yesterday:
Even with Delta endemic in the country, the vaccines are providing extraordinary protection against infections severe enough to land folks in the hospital. In a recent CDC study of infections and hospitalizations in Los Angeles County, they report that on July 25, the hospitalization rate of unvaccinated people was 29.2 times that of fully vaccinated persons. 29 times the protection is astounding for a medical intervention. These vaccines work, we’re lucky to have them, and we need to get as many people worldwide as we can vaccinated as quickly as we can. Period.
From XKCD’s Randall Munroe, a chart of the global average temperature over his lifetime. If you’re curious, you can check how much hotter your hometown is now than when you were born.
Charles Gaba has been graphing the Covid-19 vaccination rates of the 50 states (and DC) against the percentage of people who voted for Donald Trump in 2020 and there is unsurprisingly a clear correlation between the two:
As one commenter noted, all of the solidly “blue” states are above the vaxxed national average and all the solidly “red” states are below it. The picture is a little more muddy when you look at the rates at the county level:
The “conservatives are unvaxxed” trend is still there, but a lack of access and education around the vaccines in counties with large Black and Latino populations also plays a large role in whether people are vaccinated or not.
This is an animation of how quickly an object falls 1 km to the surfaces of solar system objects like the Earth, Sun, Ceres, Jupiter, the Moon, and Pluto. For instance, it takes 14.3 seconds to cover that distance on Earth and 13.8 seconds on Saturn.
It might be surprising to see large planets have a pull comparable to smaller ones at the surface, for example Uranus pulls the ball down slower than at Earth! Why? Because the low average density of Uranus puts the surface far away from the majority of the mass. Similarly, Mars is nearly twice the mass of Mercury, but you can see the surface gravity is actually the same… this indicates that Mercury is much denser than Mars.
(via @thekidshouldsee)
For the New Yorker, Hannah Fry wrote a brief history of information visualization, a quiet innovation that has changed the world:
Van Langren could have put these values in a table, as would have been typical for the time, but, as Friendly and Wainer observe, “only a graph speaks directly to the eyes.” Once the numbers were visualized, the enormous differences among them — and the stakes dependent on those differences — became impossible to ignore. Van Langren wrote, “If the Longitude between Toledo and Rome is not known with certainty, consider, Your Highness, what it will be for the Western and Oriental Indies, that in comparison the former distance is almost nothing.”
Van Langren’s image marked an extraordinary conceptual leap. He was a skilled cartographer from a long line of cartographers, so he would have been familiar with depicting distances on a page. But, as Tufte puts it, in his classic study “Visual Explanations” (1997), “Maps resemble miniature pictorial representations of the physical world.” Here was something entirely new: encoding the estimate of a distance by its position along a line. Scientists were well versed in handling a range of values for a single property, but until then science had only ever been concerned with how to get rid of error — how to take a collection of wrong answers and reduce its dimension to give a single, best answer. Van Langren was the first person to realize that a story lay in that dimension, one that could be physically seen on a page by abstracting it along a thin inked line.
Van Langren’s graph, which Fry says “might be the first statistical graph in history”, is pictured at the top of this post.
This chart caught my eye yesterday:
It’s from Robin Dunbar’s recent book, Friends: Understanding the Power of Our Most Important Relationships. You might recall the author’s name from his concept of Dunbar’s number:1 that on average people can maintain about 150 friendships with others, a limit that is determined by human brain size and function. The chart is a more detailed version of the concept: it shows, roughly, the number of people we can have meaningful relationships with at various levels of intimacy. Dunbar explains in this Atlantic interview:
The innermost layer of 1.5 is [the most intimate]; clearly that has to do with your romantic relationships. The next layer of five is your shoulders-to-cry-on friendships. They are the ones who will drop everything to support us when our world falls apart. The 15 layer includes the previous five, and your core social partners. They are our main social companions, so they provide the context for having fun times. They also provide the main circle for exchange of child care. We trust them enough to leave our children with them. The next layer up, at 50, is your big-weekend-barbecue people. And the 150 layer is your weddings and funerals group who would come to your once-in-a-lifetime event.
The layers come about primarily because the time we have for social interaction is not infinite. You have to decide how to invest that time, bearing in mind that the strength of relationships is directly correlated with how much time and effort we give them.
The interview is interesting throughout — there’s evidence that introverts have fewer connections in each layer than extroverts, your numbers go down as you get older when relationship become harder to replace, “falling in love will cost you two friendships”, and how much time is necessary to form a friendship:
It takes about 200 hours of investment in the space of a few months to move a stranger into being a good friend. This fits with our data, which suggests that close friends are very expensive in terms of time investment to maintain. I think the figures are a guideline rather than precise. It just means friendships require work.
I’ve been thinking a lot about friendship over the many months of the pandemic — about how my friend circles changed during that time and what friendship actually means to me. It is not much of an exaggeration to say that my close friends saved my life during the past year — there were 3 or 4 people that I leaned heavily on (and they on me) for advice, sanity checks, shoulder-crying, going on long walks, emotional support, grieving, getting unfunked, relationship advice, and generally feeling like a normal & whole person in the midst of an unprecedentedly awful situation. We talked and cried and raged about anything and everything. We went deep and got intimate. If there’s a silver lining to the pandemic for me, it’s the development and deepening of these incredible friendships. ♥
Fun note on this… When I searched to find the Wikipedia page for Dunbar’s number, Google helpfully(?) gave me the phone number for a local physical therapist named Dunbar.↩
Using data from the United States Geological Survey, River Runner visualizes the path taken by a raindrop from its landing spot to its eventual endpoint. Just click on any spot in the US and it maps out the path the drop would take, complete with a satellite fly-through of the route. I spent many happy minutes playing with this, although the endpoint of “Canada” for a raindrop that lands in my Vermont yard was somewhat unsatisfying.
See also The Marvelous Mississippi River Meander Maps and a map of all of the rivers in the US. (via waxy)
Translating sites, search engines, social networks, browsers, ISPs, and other internet entities into geographic features, Martin Vargic has created a map of the internet circa 2021.
It includes several thousand of some of the most popular websites, represented as distinct “countries”, which are grouped together with others of similar type or category, forming dozens of distinct clusters, regions and continents that stretch throughout the map, such as “news sites”, “search engines”, “social networks”, “e-commerce”, “adult entertainment”, “file sharing”, “software companies” and so much more. In the center of it all can be found ISPs and web browsers, which form the core and backbone of the internet as we know it, while the far south is the domain of the mysterious “dark web”.
From Flowing Data, a stacked bar chart showing the relative population distribution of age generations from 1920 to the present. The thing that’s really apparent to me in this graph is how the size, increased life expectancy, and better quality of life of the Silent and (especially) Baby Boomer generations really shifted the social order in America. It’s a triple whammy: this large group of very healthy people stuck around so much longer than the previous generations that they were able to keep their wealth and political/corporate power instead of handing it off to the next generations. It’s a generational firewall — they didn’t leave any room for their children or grandchildren. Instead, Gen X and Millennials got branded as lazy/apathetic and financially careless. (via @mikey_two)
Courtesy of Geodienst, this is a map of the world’s lighthouses. Where the data is available (and you can see it’s quite sparse for some areas of the world), the map shows the location, color, range, and flashing frequency/pattern of each lighthouse. The color and flashing pattern of a lighthouse is called the characteristic. Each lighthouse has a different characteristic so that mariners can tell them apart and to indicate different water areas. (via strange maps)
In his latest impeccably produced video, Neil Halloran looks at the science of climate change and uncertainty both in science and in the public’s trust of science.
Degrees of Uncertainty is an animated documentary about climate science, uncertainty, and knowing when to trust the experts. Using cinematic visualizations, the film travels through 20,000 years of natural temperature changes before highlighting the rapid warming of the last half century.
The vast majority of climate scientists seem pretty sure that human use of fossil fuels has warmed the Earth and that warming is increasingly having an impact on both nature and society. But how do we, as members of the public with a relatively poor understanding of science, evaluate how certain we should be?
FYI: This video includes some interactive elements that only work if you watch it on Halloran’s website.
Every 10 years, the National Oceanic and Atmospheric Administration (NOAA) updates its definition of what it defines as “normal” weather.
As soon as the 2021 New Year’s celebrations were over, the calls and questions started coming in from weather watchers: When will NOAA release the new U.S. Climate Normals? The Normals are 30-year averages of key climate observations made at weather stations and corrected for bad or missing values and station changes over time. From the daily weather report to seasonal forecasts, the Normals are the basis for judging how temperature, rainfall, and other climate conditions compare to what’s normal for a given location in today’s climate.
They’re set to release the updated 1991-2020 Normals in early May and, crucially, these new normal climate conditions are not adjusted for climate change.
The last update of the Normals took place in 2011, when the baseline shifted from 1971-2000 to 1981-2010. Among the highlights of the rollout was the creation of a map showing how climate-related planting zones across the contiguous United States had shifted northward in latitude and upward in elevation. It was a clear signal that normal overnight low temperatures across the country were warmer than they used to be.
The planting zone maps emphasized a key point about the Normals and climate change: the once-per-decade update means these products gradually come to reflect the “new normal” of climate change caused by global warming. What’s normal today is often very different than what was normal 50 or 100 years ago. This gradual adjustment is the point: the purpose of the Normals is to provide context on what climate is like today, not how it’s changing over time.
This is literally shifting baselines in action.
So what are shifting baselines? Consider a species of fish that is fished to extinction in a region over, say, 100 years. A given generation of fishers becomes conscious of the fish at a particular level of abundance. When those fishers retire, the level is lower. To the generation that enters after them, that diminished level is the new normal, the new baseline. They rarely know the baseline used by the previous generation; it holds little emotional salience relative to their personal experience.
And so it goes, each new generation shifting the baseline downward. By the end, the fishers are operating in a radically degraded ecosystem, but it does not seem that way to them, because their baselines were set at an already low level.
Over time, the fish goes extinct — an enormous, tragic loss — but no fisher experiences the full transition from abundance to desolation. No generation experiences the totality of the loss. It is doled out in portions, over time, no portion quite large enough to spur preventative action. By the time the fish go extinct, the fishers barely notice, because they no longer valued the fish anyway.
I’ve been thinking a lot about shifting baselines recently — specifically in terms of how quickly people in the US got used to thousands of people dying from Covid every day and became unwilling to take precautions or change behaviors that were deemed essential just months earlier when many fewer people were dying. See also mass shootings.
This guide to Covid-19 variants (SARS-CoV-2 viruses that have evolved changes to meaningfully alter their behavior) by Michaeleen Doucleff and Meredith Rizzo at NPR cleverly visualizes how mutations of the virus’s spike proteins help bind it more easily to ACE2 receptors on human cells. The key to the visualization is Meredith Miotke’s illustrations of the viruses using Lego pieces to represent the virus spikes and cell receptors. The usual SARS-CoV-2 has 1x1 Lego pieces that can bind with 1x2 pieces, like so:
But, as everyone who has ever worked with a Lego set knows, a 1x1 piece stuck to a 1x2 piece is not super stable. So when a version of the virus with a 1x2 piece shows up, it’s able to form a better connection to the 1x2 receptor:
The analogy breaks down if you look too hard at it1 but for many people, it can be a quick way to get the gist of the mechanism at work here. (via @EricTopol)
This is a huge pet peeve of mine when people try to poke holes in analogies: by definition, all analogies break down if you examine them too deeply. An analogy is a comparison of two different things that are similar in significant respects. If they were the same in every respect, it’s not an analogy…you’d just be describing one thing.↩
For Design Ah by Daihei Shibata, Unendurable Line is a short film about sudden changes due to “thresholds hidden in everyday life”. The choral accompaniment to this is delightful.
See also Shibata’s Unexpected Outcome. If you’re in the US, you can watch 60 full episodes of Design Ah on THIRTEEN.
Spurred by the pandemic — what he calls “the first experience we’ve had of a global disaster affecting every single person on Earth”1 — Domain of Science’s Dominic Walliman takes stock of many of the possible catastrophes that might befall humanity, ranking possible threats based on their likelihood and the number of potential casualties.
This year was the first experience we’ve had of a global disaster affecting every single person on Earth. And also how unprepared society was to deal with it, despite plenty of people giving warnings that this was going to happen at some stage.
But in the midst of all the doom I started to wonder, what other things could threaten humanity, that we are not thinking about? So I made the Map of Doom to list all the threats to humanity in one place.
The result is the quadrant chart and the video above as well as the Map of Doom.
One could imagine a third dimension of this chart: what, if anything, humans can do about each of these threats. Earthquakes can be detected, buildings can be designed to withstand them, and evacuation procedures enacted and prioritized. Many effects of climate change can be mitigated. Asteroids can be detected, but doing something about them might prove difficult. We’ve lowered the threat of nuclear war — for now. Supervolcanoes? Yikes.
You can find a list of references used in the video’s description. (via open culture)
Well, I think you could make good arguments for Western colonial expansion and capitalism here. Oh and some past volcanic eruptions.↩
Check out this graph from Our World in Data of the price of electricity from new power plants. In 2009, solar was the most expensive energy source and in 2019 it’s the cheapest.
Electricity from utility-scale solar photovoltaics cost $359 per MWh in 2009. Within just one decade the price declined by 89% and the relative price flipped: the electricity price that you need to charge to break even with the new average coal plant is now much higher than what you can offer your customers when you build a wind or solar plant.
It’s hard to overstate what a rare achievement these rapid price changes represent. Imagine if some other good had fallen in price as rapidly as renewable electricity: Imagine you’d found a great place to live back in 2009 and at the time you thought it’d be worth paying $3590 in rent for it. If housing had then seen the price decline that we’ve seen for solar it would have meant that by 2019 you’d pay just $400 for the same place.
The rest of the page is worth a read as well. One reason why the cost of solar is falling so quickly is that the technology is following a similar exponential curve to computer chips, which provide more speed and power every year for less money, an observation called Wright’s Law:
If you want to know what the future looks like one of the most useful questions to ask is which technologies follow Wright’s Law and which do not.
Most technologies obviously do not follow Wright’s Law — the prices of bicycles, fridges, or coal power plants do not decline exponentially as we produce more of them. But those which do follow Wright’s Law — like computers, solar PV, and batteries — are the ones to look out for. They might initially only be found in very niche applications, but a few decades later they are everywhere.
If you are unaware that technology follows Wright’s Law you can get your predictions very wrong. At the dawn of the computer age in 1943 IBM president Thomas Watson famously said “I think there is a world market for maybe five computers.” At the price point of computers at the time that was perhaps perfectly true, but what he didn’t foresee was how rapidly the price of computers would fall. From its initial niche when there was perhaps truly only demand for five computers they expanded to more and more applications and the virtuous cycle meant that the price of computers declined further and further. The exponential progress of computers expanded their use from a tiny niche to the defining technology of our time.
Solar modules are on the same trajectory, as we’ve seen before. At the price of solar modules in the 1950s it would have sounded quite reasonable to say, “I think there is a world market for maybe five solar modules.” But as a prediction for the future this statement too would have been ridiculously wrong.
After Terri Nelson noticed people complaining online about a lack of scent from newly purchased scented candles, Kate Petrova analyzed Amazon reviews for candles from the past three years and found a drop in ratings for scented candles beginning in January 2020 (compared to a smaller ratings decline for unscented candles).
The hypothesis is that some of these buyers have lost their sense of smell due to Covid-19 infections and that’s showing up in the ratings.
For the print version of the NY Times from this past Sunday, information designer Giorgia Lupi created a hand-drawn visualization that “tracks the last time [she] did something before the pandemic hit, and the first time she did something new with social distancing”.
Our lives have been transformed during the Covid-19 pandemic as the activities we used to do every day have been put on hold and new, socially distanced routines have taken their place. Pentagram partner Giorgia Lupi documents these changes in her own life in a data visualization commissioned by The New York Times for the cover of its “At Home” section, which runs as part of the newspaper’s Sunday edition. The hand-drawn visualization is a personal timeline that tracks the “last” time Giorgia did something before the pandemic hit, and the “first” time she did something new as she started to emerge from lockdown.
Not hand-drawn, but I remember pretty clearly what my lasts were:
I don’t remember my firsts as well, although one that sticks out is eating french fries (take-out) in July. On a normal day, french fries are delicious but when you haven’t had them in months, they are otherworldly.
From Stamen’s lead cartographer Alan McConchie, an excellent review of the charts and maps that best communicated vital information about the 2020 election.
After the first night of election results was over, and most of the state races had been called, we settled in for several more days of vote counting in a few key states. Dynamic “choose your own adventure” maps and scenario modeling tools became much less useful, and the landscape of the remaining Electoral College outcomes could be explained better without a map or a chart at all. Simple infographics like this one from the BBC did an excellent job of telling the reader all they needed to know.
You should dig into the article for the commentary and analysis, but I did want to share a couple of my favorite maps/charts:
The graphs by John Harurum in that last image were especially useful for me in seeing how the counting trends were going.
The Swiss cheese model of accident causation is a framework for thinking about how to layer security measures to minimize risk and prevent failure. The idea is that when several layers of interventions, despite their weaknesses, are properly stacked up between a hazard and a potentially bad outcome, they are able to cumulatively prevent that outcome because there’s no single point of failure. During the pandemic, health care workers and public health officials have been using the Swiss cheese model to visualize how various measures can work together to help keep people safe.
Virologist Dr. Ian Mackay has visualized the Swiss cheese Covid-19 defense in a wonderful way (pictured above). Each layer of cheese represents a personal or shared intervention — like mask wearing, limiting your time indoors w/ crowds, proper ventilation, quarantine, vaccines — and the holes are imperfections. Applied together, these imperfect measures work like a filter and can vastly improve chances of success.1 He even added a “misinformation mouse” chewing through one of the cheese slices to represent how deceptive information can weaken these defenses.
Mackay has released this graphic under a Creative Commons license (free to share and adapt w/ attribution) and is available in English, German, French, Spanish, Korean, and several other languages. (via @EricTopol)
It’s interesting that the Swiss cheese model is physically how masks work to stop aerosols and droplets — like layered filters and not sieves.↩
From El Pais, this is an excellent visualization of how Covid-19 spreads indoors via aerosols and what can be done to limit that spread. They go through simulations of three different indoor scenarios that are based on actual events — in a home with friends, in a bar at 50% capacity, and in a classroom — and see what happens when differing levels of precautions are applied: masks, ventilation, and limiting exposure time.
Six people get together in a private home, one of whom is infected. Some 31% of coronavirus outbreaks recorded in Spain are caused by this kind of gathering, mainly between family and friends.
Irrespective of whether safe distances are maintained, if the six people spend four hours together talking loudly, without wearing a face mask in a room with no ventilation, five will become infected, according to the scientific model explained in the methodology.
If face masks are worn, four people are at risk of infection. Masks alone will not prevent infection if the exposure is prolonged.
The risk of infection drops to below one when the group uses face masks, shortens the length of the gathering by half and ventilates the space used.
In all three scenarios, note that distancing is largely irrelevant when people gather indoors for longer periods in poorly ventilated areas. From the school example:
In real outbreaks, it has been noted that any of the students could become infected irrespective of their proximity to the teacher as the aerosols are distributed randomly around the unventilated room.
The only thing that’s disappointing about this piece is that it does not stress enough that finding alternatives to indoor activities with lots of people is the much safer course of action than just cracking a window or masking up. Safety step #1 is still being smart about non-essential activities — masks and ventilation are not magically going to protect you during risky activities. Educating our children is important and difficult (though not impossible) to do outside in many places, so yeah, let’s mask up and ventilate those classrooms. But your indoor birthday party with 10 friends or Thanksgiving dinner with the cousins and grandparents? Or dining out in a room full of strangers at a restaurant? Even with masks and ventilation, it’s not a great idea. Scale it down, move it to Zoom/FT, hold it outdoors (distanced, masked), or just skip it.
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