Inspired by the charts on Robinhood and Yahoo Finance, Gladys Orteza is turning the charts of notable stocks into landscape artworks, inserting references to the company into the art. The Ford chart at the top has a truck, the Tesla chart features a rocket (a reference to SpaceX), and the Disney one includes the twin suns of Tatooine & a Jawa Sandcrawler.
From January to the end of June, over 500,000 people died of confirmed cases of Covid-19. In order to demonstrate the magnitude of the pandemic, James Beckwith made a time lapse map of each Covid-19 death.
Each country is represented by a tone and an expanding blip on the map when a death from Covid-19 is recorded. Each day is 4 seconds long, and at the top of the screen is the date and a counter showing the total numbers of deaths. Every country that has had a fatality is included.
As was the case with the pandemic, the video starts slow but soon enough the individual sounds and blips build to a crescendo, a cacophony of death. The only way this could be made more ominous & upsetting is by including the first song off of Cliff Martinez’s Contagion soundtrack as a backing track. As Beckwith notes in the description: “It is likely a sequel will need to be made.” (via open culture)
A pair of aquariums in Japan have published flowcharts that track the relationships of their penguins.
Penguins, the way they waddle around and protect their eggs, are often thought of as cute, cuddly and romantic. But those who observe them for extended periods know they have a dark side. Two aquariums in Japan, Kyoto Aquarium and Sumida Aquarium, keep obsessive tabs on their penguins and maintain an updated flowchart that visualizes all their penguin drama.
As Kyoto-based researcher Oliver Jia points out, penguin drama can include serious crushes and heartbreaks but also adultery and egg-stealing. And these Japanese aquariums have it all charted in a flowchart that can be studied for hours.
In 1854, Dr. John Snow produced a map of a London cholera outbreak which showed deaths from the disease concentrated around a water pump on Broad Street. The prevailing view at the time was that cholera spread through dirty air, but Snow hypothesized that it was actually spread through water and constructed this early medical data visualization to help prove it.
Through a mix of personal interviews, clever detective work, and data analysis that included tables and a famous map, Snow managed to stop the outbreak and convince local public health officials, eventually, that cholera could be transmitted through water, not a miasma. Since his breakthrough study, the map has become an iconic piece of epidemiological history, as an illustration of keen detective work, analysis, and visual representation with a map that, even today, tells a story.
Aside from the cluster of deaths around the pump (which could be argued were the result of a miasma cloud and not contaminated water), stories of nearby people who didn’t get sick (brewers who drank the beer they produced rather than well water, people in buildings with their own wells) and far away people who died because they had drunk water from the well were also essential in proving his theory:
I was informed by this lady’s son that she had not been in the neighbourhood of Broad Street for many months. A cart went from broad Street to West End every day and it was the custom to take out a large bottle of the water from the pump in Broad Street, as she preferred it. The water was taken on Thursday 31st August., and she drank of it in the evening, and also on Friday. She was seized with cholera on the evening of the latter day, and died on Saturday
You can read more about John Snow and how his map changed science and medicine in Steven Johnson’s excellent Ghost Map.
For Slate’s 2015 podcast series The History of American Slavery, Andrew Kahn created an interactive visualization of the 20,000+ voyages that made up the Atlantic slave trade that lasted 315 years. A video of the interactive map is embedded above.
As we discussed in Episode 2 of Slate’s History of American Slavery Academy, relative to the entire slave trade, North America was a bit player. From the trade’s beginning in the 16th century to its conclusion in the 19th, slave merchants brought the vast majority of enslaved Africans to two places: the Caribbean and Brazil. Of the more than 10 million enslaved Africans to eventually reach the Western Hemisphere, just 388,747 — less than 4 percent of the total — came to North America. This was dwarfed by the 1.3 million brought to Spanish Central America, the 4 million brought to British, French, Dutch, and Danish holdings in the Caribbean, and the 4.8 million brought to Brazil.
Roughly 400,000 enslaved Africans were brought to the United States before the practice was banned in 1808. The ban was mostly (but not entirely) enforced and yet in 1860, the population of enslaved persons was almost 4 million in the South. That’s because the 1808 ban, according to Ned & Constance Sublette’s book The American Slave Coast: A History of the Slave-Breeding Industry, was a form of trade protectionism that protected the forced breeding of enslaved peoples by American slaveowners. From a review of the book:
In fact, most American slaves were not kidnapped on another continent. Though over 12.7 million Africans were forced onto ships to the Western hemisphere, estimates only have 400,000-500,000 landing in present-day America. How then to account for the four million black slaves who were tilling fields in 1860? “The South,” the Sublettes write, “did not only produce tobacco, rice, sugar, and cotton as commodities for sale; it produced people.” Slavers called slave-breeding “natural increase,” but there was nothing natural about producing slaves; it took scientific management. Thomas Jefferson bragged to George Washington that the birth of black children was increasing Virginia’s capital stock by four percent annually.
Here is how the American slave-breeding industry worked, according to the Sublettes: Some states (most importantly Virginia) produced slaves as their main domestic crop. The price of slaves was anchored by industry in other states that consumed slaves in the production of rice and sugar, and constant territorial expansion. As long as the slave power continued to grow, breeders could literally bank on future demand and increasing prices. That made slaves not just a commodity, but the closest thing to money that white breeders had. It’s hard to quantify just how valuable people were as commodities, but the Sublettes try to convey it: By a conservative estimate, in 1860 the total value of American slaves was $4 billion, far more than the gold and silver then circulating nationally ($228.3 million, “most of it in the North,” the authors add), total currency ($435.4 million), and even the value of the South’s total farmland ($1.92 billion). Slaves were, to slavers, worth more than everything else they could imagine combined.
You can read more about the economics of slavery in this post from 2016, including how American banks sold bonds that used enslaved persons as collateral to international investors. (via open culture)
Using census data (which she acknowledges can be imperfect in capturing the full range of people’s identities), data scientist/artist Mona Chalabi created a drawing of 100 people who are representative of NYC’s population for a NY Times opinion piece on inequality and coronavirus.
When you think about who is most affected by Covid-19, you need to consider inequalities in housing, in access to healthcare, in wealth. And so much of that ends up consistently affecting people of color. You could think of it as overlapping circles in a Venn diagram. Or, you could look at these 100 people.
I have written previously about cartographer Harold Fisk’s wonderful meander maps of the Mississippi River produced for the Army Corps of Engineers. Borrowing the aesthetic of these maps, interactive artist & engineer Robert Hodgin wrote some software called Meander to generate meander maps for fictional rivers.
From an input curve, the terrain, land plots, side roads, highways, marsh land and mountain peaks are generated and prominent features are named. The map is then weathered and rendered in the style of old US Army Corp of Engineers maps from the 1930s and 40s.
You can check some of the generated maps out onTwitter or onInstagram, including some prototypes and animations (this one is my favorite). Hodgin has promised a full write-up of the project; I’ll link to it when he publishes it.
Coincidentally, while I was writing this post I got an email from a reader about an audiovisual installation called Meandering River that displayed “real-time visuals generated by an algorithm and music composed by an A.I.”
This week, Covid-19 passed heart disease and cancer as the leading cause of death per day in the United States. In this graph made by Dr. Maria Danilychev using data from Worldometer and the CDC, you can see that Covid-19 overtook heart disease sometime on Monday or Tuesday.
If the data in NYC is any indication, the number of nationwide Covid-19 deaths may be undercounted, so this transition probably happened sooner.1 Hopefully through the social distancing and other measures put in place to flatten the curve, the number of daily Covid-19 deaths won’t start beating out all other causes combined before it starts declining.
Several months from now, it will be easier to get a more accurate count of how many people died by looking at the “baseline” rate of death and comparing it with the actual numbers. Unless this sort of recount is politicized, which it will be, and *siiiigh*↩
Since 2008, the Hedonometer has been tracking the language we use on Twitter to assign a daily score that measures how collectively happy we are (English tweets only). From the data, you can see that happiness spikes on holidays & after notable news events (same-sex marriage legalization) and unhappiness follows mass shootings, terrorist events, and Trump’s election. But the Covid-19 pandemic has brought Twitter’s collective happiness rating to an overall new low and its first sustained period of unhappiness.
The day they identify as the unhappiest is March 12, 2020, which is the day after Americans finally took Covid-19 seriously. Within the space of a few hours on March 11, the NBA announced it was suspending its season, Tom Hanks revealed that he and his wife Rita Wilson had Covid-19, the WHO declared Covid-19 a pandemic, Donald Trump went on primetime TV to address the nation, and the DJIA closed down 1400 points (it would drop another 2350 points on Mar 12).
People on Spring Break in Florida for the past couple of weeks were famously unconcerned with social distancing measures implementing in other areas of the country to help stem the tide of COVID-19 infections and save lives. Using cellphone location data from just the phones of the people gathered on a single beach in Fort Lauderdale, Florida, this video shows just how far those people spread across the country when they went home, possibly taking SARS-CoV-2 with them. They go everywhere.
Show of hands: who feels uncomfortable being reminded of the extent to which 3rd party companies know the location of our cellphones? With tools like the one demonstrated in the video & other easily available info, it has to be trivial to identify individuals by name using even “randomized” data and so-called metadata. (via @stewartbrand)
The practice of quarantine began during the 14th century, in an effort to protect coastal cities from plague epidemics. Cautious port authorities required ships arriving in Venice from infected ports to sit at anchor for 40 days before landing — the origin of the word quarantine from the Italian “quaranta giorni”, or 40 days.
One of the first instances of relying on geography and statistical analysis was in mid-19th century London, during a cholera outbreak. In 1854, Dr. John Snow came to the conclusion that cholera was spreading via tainted water and decided to display neighborhood mortality data directly on a map. This method revealed a cluster of cases around a specific pump from which people were drawing their water from.
While the interactions created through trade and urban life play a pivotal role, it is also the virulent nature of particular diseases that indicate the trajectory of a pandemic.
One of my big takeaways from the Tracking Infectiousness section of the piece is: holy shit, look at how contagious measles is! An R0 of 16! (The common flu is about 1.5 and ebola is 2.0.) And people want to keep their children from getting vaccinated for this?!
Over the past week or so, echoing public health officials & epidemiologists, I’ve been trying to illustrate the often counterintuitive concept of exponential growth that you see in an epidemic and how flattening the curve can help keep people healthy and alive. But I think people have a hard time grasping what that means, personally, to them. Like, what’s one person in the face of a pandemic?
Well, epidemiologist Britta Jewell had a similar thought and came up with this brilliantly simple graph, one of the best I’ve seen in illustrating the power of exponential growth and how we as individuals can affect change:
Jewell explains a bit more about what we’re looking at:
The graph illustrates the results of a thought experiment. It assumes constant 30 percent growth throughout the next month in an epidemic like the one in the U.S. right now, and compares the results of stopping one infection today — by actions such as shifting to online classes, canceling of large events and imposing travel restrictions — versus taking the same action one week from today.
The difference is stark. If you act today, you will have averted four times as many infections in the next month: roughly 2,400 averted infections, versus just 600 if you wait one week. That’s the power of averting just one infection, and obviously we would like to avert more than one.
So that’s 1800 infections averted from the actions of just one person. Assuming a somewhat conservative death rate of 1% for COVID-19, that’s 18 deaths averted. Think about that before you head out to the bar tonight or convene your book group as usual. Your actions have a lot of power in this moment; take care in how you wield it.
Illustrator Jerry M. Wilson has drawn a series of constellations that explore the etymology of the constellations’ names and related words in several languages. So for example, “Taurus” is Latin for “bull”, which is “toro” in Spanish & Italian and “tyr” in Danish. And then you also have associated words like “toreador” (“bullfighter” in Spanish) and “teurastamo” (Finnish for “slaughterhouse”)…a constellation of words related to “Taurus”.
Including some irregular times off, overall it took me four years to visit every single road on the map. When I started this hobby, it took me 30 to 40 minutes to do the route. Later it expanded to 2 hours to get to the office when I tried to reach the furthest places on my map. One of the main goals was never to be late for work. From the beginning, I planned to visit not only the main roads but every single accessible mews, yard, park trail, and a path that was possible to go through. I used Endomondo app to have a proper record of my journeys and proof that I have been there. After every trip, I prepared my next route in Google maps where it was easy to adjust streets to the next ones and mark points to revisit if I missed something.
The arrangement of the sticks in these Marshall Islands navigational charts represents ocean swells & currents and how they interact with the land, useful information for navigating between islands via canoe. From a Smithsonian Magazine article about these charts:
The chart is less a literal representation of the sea, says museum curator and anthropologist Adrienne Kaeppler, and more an abstract illustration of the ways that ocean swells interact with land. Curved sticks, she explains, show where swells are deflected by an island; short, straight strips often indicate currents near islands; longer strips “may indicate the direction in which certain islands are to be found;” and small cowry shells represent the islands themselves.
The stick charts were preparatory & teaching tools — mariners would memorize the charts before heading out to sea rather than take them along on the boat.
The first 30 seconds of this time lapse video provides a great look into how the 10 satellites that make up the Global Precipitation Measurement Constellation scan the surface of the Earth to provide daily global precipitation maps.
This visualization shows the constellation in action, taking precipitation measurements underneath the satellite orbits. As time progresses and the Earth’s surface is covered with measurements, the structure of the Earth’s precipitation becomes clearer, from the constant rainfall patterns along the Equator to the storm fronts in the mid-latitudes. The dynamic nature of the precipitation is revealed as time speeds up and the satellite data swaths merge into a continuous visualization of changing rain and snowfall.
Over a period of three months, Seung Lee knit a blanket showing a visualization of his infant son’s sleep patterns from birth to his first birthday.
The sleep data was collected with the BabyConnect app which lets you export to CSV. The CSVs were filtered and converted into JSON (using Google Apps Script and Python) which could then be used for visualization and tracking.
Christoph Niemann with a clever take on the Beethoven composition for piano, Für Elise. He’s offering it as a letterpress print — but supplies are low so order quick if you want one.
Designer Scott Reinhard takes old geological survey maps and combines them with elevation data to produce these wonderful hybrid topographic maps. From top to bottom, here are Reinhard’s 3D versions of a 1878 USGS Yellowstone map, a 1904 USGS map of Acadia National Park, and a 1899 USGS map of the Grand Tetons.
If you grew up watching TV (and who didn’t?), this bar chart race animation of the 10 most popular primetime TV shows from 1986-2019 is fascinating.
Ranking is based on the following factors: prime-time first 24 hours audience reports, one week of reported statistics for downloaded copies (pirated), one week of streaming services viewership. Numbers are worldwide with significant bias towards US market up until 2002, afterwards it’s balanced by p2p distribution across the globe.
I’d forgotten what a huge hit ER was in the mid-90s. And note that The Simpsons never cracked the top 10. Ah, I didn’t notice that they snuck in briefly during 1996 — thx @ChasingDom. (via waxy)
Each day since the beginning of October, the team of designers, technologists, and researchers at Beautiful News Daily (a project by Information Is Beautiful) have been posting infographics and data visualizations that share some good news about the world. The site’s tagline is “unseen trends, uplifting stats, creative solutions”.
The bad news we see everyday on news websites, newspaper front pages, and magazine covers is important (or can be, if it’s not designed to keep people frightened and hooked on the news), but the good news is just as significant (or can be, if it doesn’t cause you to forget the world’s true suffering and turmoil).
It’s easy to get started with chart.xkcd. All that’s required is the script included in your page along with a single
You can use it to make line charts, XY charts, bar charts, radar charts, and pie/doughnut charts. I am definitely going to be using this in the future.
Moon dust may not burn you, but it’s no picnic. Like Earth sand, moon dust is effectively made of tiny glass shards, but the sharp edges have not been worn down by erosion. As a result, it can be pretty unhealthy.
From the American Museum of Natural History in NYC, an animated timeline of human evolution, from when hominins first show up in the fossil record in Africa some seven million years ago to the appearance of Homo sapiens about 200,000 years ago. You can see artifacts and fossil remains of many of the hominins at the museum in the Hall of Human Origins. I haven’t been there in awhile…might be time for a visit.
I got this from Open Culture, where Colin Marshall goes into more detail:
And though hominins may have walked upright, they also climbed trees, but eventually lost the grasping feet needed to do so. Later they compensated with the very human-like development of making and using stone tools. Two million years ago, the well-known Homo erectus, with their large brains, long legs, and dextrous hands, made the famous migration out of Africa.
We know that by 1.2 million years thereafter Homo erectus’ brains had grown larger still, fueled by new cooking techniques. Only about 200,000 years ago do we, Homo sapiens, enter the picture, but not long after, we interbreed with the various hominin species already in existence as we spread outward to fill “every geographic niche” of the Earth.
The last bit of the video was unexpectedly sobering:
Homo sapiens were highly adaptable, quickly filling nearly every geographic niche. Other hominins went extinct. Climate pressures and competition with Homo sapiens may have wiped them out.
If we don’t change our ways soon, one way to look at the recent history of life on Earth is that modern humans came along 200,000 years ago and systematically conquered and killed the all of the animals on the planet larger than an ant. Not such a great deal for anything but people.
In 1965, Intel co-founder Gordon Moore predicted that the number of transistors in dense integrated circuits would double each year for the next decade. In 1975, he revised his prediction to a doubling once every two years. And for the past 45 years, Moore’s Law has more or less held. This clever bar chart race visualization shows Moore’s prediction competing through the years with hundreds of real microprocessors, from Intel’s 4004 in 1971 to 2019’s newest CPUs and GPUs.
Check out the lull in the 90s, where the microprocessor industry falls behind Moore’s Law all the way from Intel’s 486 in 1989 to the release of Intel’s Itanium 2 McKinley chip in 2002. And then in the 00s, the chipmakers put their foot on the gas again, more than doubling up on Moore’s Law at times. I wonder if the 90s slump was due more to a lack of industry competition against Intel’s near monopoly…they simply didn’t need to increase the count as quickly with no real competitors breathing down their necks. Then in the 00s, competition flourished. If so, perhaps Moore’s Law should be regarded as just as much of a business prediction (or goal) as one about technology.
In 1936, former director of research at the Cleveland Federal Reserve L. Merle Hostetler published 75 Yrs. of American Finance, a hand-drawn chart of the economic health of the US from 1861 to 1935. The chart, which is horizontally oriented, shows a trending business activity index (which measures productivity) along with other financial data, indicates when Congress is in session, lists notable news events, and shows the high and low of the DJIA (starting in 1898). The graphic at the top shows Hostetler’s chart from 1929-1931, aka the beginning of the Great Depression.
The copy of this chart hosted by the St. Louis Fed goes to 1938…it must have been updated at some point. Also, if you go into the “»” menu in the upper-right corner of the in-page document viewer, you can set it to “horizontal scrolling” for easier viewing. (thx, andy)
As you can see from the charts, the usage of many parks is heaviest in the summer. For instance, Yellowstone is used heavily until the beginning of September and then drops off to almost nothing by mid-October. For some parks, like the Great Smoky Mountains and Shenandoah, usage spikes in the fall during peak foliage.1
Just typing the phrase “peak foliage” during the dog days of summer in Vermont is making me anxious of the approaching winter. Winter here kicked my ass the past two years, so I’m really motivated to not be depressed for 6 months this year. Any (non-obvious) tips? Already planning trips to warm places, socializing, getting outdoors more, and SAD lamping…↩
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