Revisit Scenes of Daily Life in Amsterdam in 1922, with Historic Footage Enhanced by Artificial Intelligence

Welkom in Amsterdam… 1922.

Neural network artist Denis Shiryaev describes himself as “an artistic machine-learning person with a soul.”

For the last six months, he’s been applying himself to re-rendering documentary footage of city life—Belle Epoque ParisTokyo at the start of the the Taishō era, and New York City in 1911—the year of the Triangle Shirtwaist Fire.

It’s possible you’ve seen the footage before, but never so alive in feel. Shiryaev’s renderings trick modern eyes with artificial intelligence, boosting the original frames-per-second rate and resolution, stabilizing and adding color—not necessarily historically accurate.




The herky-jerky bustling quality of the black-and-white originals is transformed into something fuller and more fluid, making the human subjects seem… well, more human.

This Trip Through the Streets of Amsterdam is truly a blast from the past… the antithesis of the social distancing we must currently practice.

Merry citizens jostle shoulder to shoulder, unmasked, snacking, dancing, arms slung around each other… unabashedly curious about the hand-cranked camera turned on them as they go about their business.

A group of women visiting outside a shop laugh and scatter—clearly they weren’t expecting to be filmed in their aprons.

Young boys looking to steal the show push their way to the front, cutting capers and throwing mock punches.

Sorry, lads, the award for Most Memorable Performance by a Juvenile goes to the small fellow at the 4:10 mark. He’s not hamming it up at all, merely taking a quick puff of his cigarette while running alongside a crowd of men on bikes, determined to keep pace with the camera person.

Numerous YouTube viewers have observed with some wonder that all the people who appear, with the distant exception of a baby or two at the end, would be in the grave by now.

They do seem so alive.

Modern eyes should also take note of the absences: no cars, no plastic, no cell phones…

And, of course, everyone is white. The Netherlands’ population would not diversify racially for another couple of decades, beginning with immigrants from Indonesia after WWII and Surinam in the 50s.

With regard to that, please be forewarned that not all of the YouTube comments have to do with cheeky little boys and babies who would be pushing 100…

The footage is taken from the archival collection of the EYE filmmuseum in Amsterdam, with ambient sound by Guy Jones.

See more of Denis Shiryaev’s  upscaled vintage footage in the links below.

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Ayun Halliday is an author, illustrator, theater maker and Chief Primatologist of the East Village Inky zine. Follow her @AyunHalliday.

16 Ways the World Is Getting Remarkably Better: Visuals by Statistician Hans Rosling

It certainly may not feel like things are getting better behind the anxious veils of our COVID lockdowns. But some might say that optimism and pessimism are products of the gut, hidden somewhere in the bacterial stew we call the microbiome. “All prejudices come from the intestines,” proclaimed noted sufferer of indigestion, Friedrich Nietzsche. Maybe we can change our views by changing our diet. But it’s a little harder to change our emotions with facts. We turn up our noses at them, or find them impossible to digest.

Nietzsche did not consider himself a pessimist. Despite his stomach troubles, he “adopted a philosophy that said yes to life,” notes Reason and Meaning, “fully cognizant of the fact that life is mostly miserable, evil, ugly, and absurd.” Let’s grant that this is so. A great many of us, I think, are inclined to believe it. We are ideal consumers for dystopian Nietzsche-esque fantasies about supermen and “last men.” Still, it's worth asking: is life always and equally miserable, evil, ugly, and absurd? Is the idea of human progress no more than a modern delusion?




Physician, statistician, and onetime sword swallower Hans Rosling spent several years trying to show otherwise in television documentaries for the BBC, TED Talks, and the posthumous book Factfulness: Ten Reasons We’re Wrong About the World—and Why Things Are Better Than You Think, co-written with his son and daughter-in-law, a statistician and designer, respectively. Rosling, who passed away in 2017, also worked with his two co-authors on software used to animate statistics, and in his public talks and book, he attempted to bring data to life in ways that engage gut feelings.

Take the set of graphs above, aka, “16 Bad Things Decreasing,” from Factfulness. (View a larger scan of the pages here.) Yes, you may look at a set of monochromatic trend lines and yawn. But if you attend to the details, you'll can see that each arrow plummeting downward represents some profound ill, manmade or otherwise, that has killed or maimed millions. These range from legal slavery—down from 194 countries in 1800 to 3 in 2017—to smallpox: down from 148 countries with cases in 1850 to 0 in 1979. (Perhaps our current global epidemic will warrant its own triumphant graph in a revised edition some decades in the future.) Is this not progress?

What about the steadily falling rates of world hunger, child mortality, HIV infections, numbers of nuclear warheads, deaths from disaster, and ozone depletion? Hard to argue with the numbers, though as always, we should consider the source. (Nearly all these statistics come from Rosling’s own company, Gapminder.) In the video above, Dr. Rosling explains to a TED audience how he designed a course on global health in his native Sweden. In order to make sure the material measured up to his accomplished students’ abilities, he first gave them a questionnaire to test their knowledge.

Rosling found, he jokes, “that Swedish top students know statistically significantly less about the world than a chimpanzee," who would have scored higher by chance. The problem “was not ignorance, it was preconceived ideas," which are worse. Bad ideas are driven by many -isms, but also by what Rosling calls in the book an “overdramatic” worldview. Humans are nervous by nature. “Our tendency to misinterpret facts is instinctive—an evolutionary adaptation to help us make quick decisions to avoid danger,” writes Katie Law in a review of Factfulness.

“While we still need these instincts, they can also trip us up.” Magnified by global, collective anxieties, weaponized by canny mass media, the tendency to pessimism becomes reality, but it's one that is not supported by the data. This kind of argument has become kind of a cottage industry; each presentation must be evaluated on its own merits. Presumably enlightened optimism can be just as oversimplified a view as the darkest pessimism. But Rosling insisted he wasn’t an optimist. He was just being “factful.” We probably shouldn’t get into what Nietzsche might say to that.

via Simon Kuestenmacher

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

Simulating an Epidemic: Using Data to Show How Diseases Like COVID-19 Spread

Disease modeling as a science has come into its own lately, for heartbreakingly obvious reasons. What may not be so obvious to those of us who aren't scientists is just how critical data can be in changing the course of events in an outbreak. Virus outbreaks may be “acts of God” or acts of unregulated black markets and agribusinesses, but in either case, statistical models can show, concretely, how collective human activity can save lives—and show what happens when people don’t act together.

For example, epidemiologists and biostatisticians have shown in detail how social distancing led to a “decline in the proportion of influenza deaths,” one study concludes, during the 1918 flu pandemic. The same researchers also saw evidence in their models that showed “public risk perception could be lowered” when these practices worked effectively, leading people think they could resume business as usual. But “less social distancing could eventually induce another epidemic wave.”




To say that it’s a challenge to stay inside and wait out COVID-19 indefinitely may be a gross understatement, but hunkering down may save our lives. No one can say what will happen, but as for how and why it happens, well, “that is math, not prophecy,” writes Harry Stevens at The Washington Post. “The virus can be slowed,” if people continue “avoiding public spaces and generally limiting their movement.” Let’s take a look at how with the model above. We must note that the video above does not model COVID-19 specifically, but a offers a detailed look at how a hypothetical epidemic spreads.

Created by YouTuber 3Blue1Brown, the modeling in the top video draws from a variety of sources, including Stevens’ interactive models of a hypothetical disease he calls “simulitis.” Another simulator whose work contributed to the video, Kevin Simler, has also explained the spread of disease with interactive models that enable us to visualize difficult-to-grasp epidemiological concepts, since "exponential growth is really, really hard for our human brains to understand” in the abstract, says YouTube physics explainer Minute Physics in the short, animated video above.

Deaths multiply faster than the media can report, and whatever totals we come across are hopelessly outdated by the time we read them, an emotional and intellectual barrage. So how can we know if we’re “winning or losing” (to use the not-particularly-helpful war metaphor) the COVID-19 fight? Here too, the current data on its previous progress in other countries can help plot the course of the disease in the U.S. and elsewhere, and allow scientists and policy-makers to make reasonable inferences about how to stop exponential growth.

But none of these models show the kind of granularity that doctors, nurses, and public health professionals must deal with in a real pandemic. “Simulitis is not covid-19, and these simulations vastly oversimplify the complexity of real life,” Stevens admits. Super-complicating risk factors like age, race, disability, and access to insurance and resources aren’t represented here. And there may be no way to model whatever the government is doing.

But the data models show us what has worked and what hasn't, both in the past and in the recent present, and they have become very accessible thanks to the internet (and open source journals on platforms like PLOS). For a longer, in-depth explanation of the current pandemic's exponential spread, see the lecture by epidemiologist Nicholas Jewell above from the Mathematical Sciences Research Institute (MSRI).

It may not sway people who actively ignore math, but disease modeling can guide the merely uninformed to a much better understanding of what’s happening, and better decisions about how to respond under the circumstances.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

Linked Jazz: A Huge Data Visualization Maps the Relationships Between Countless Jazz Musicians & Restores Forgotten Women to Jazz History

Having watched the development of interactive data visualizations as a writer for Open Culture, I’ve seen my share of impressive examples, especially when it comes to mapping music. Perhaps the oldest such resource, the still-updating Ishkur’s Guide to Electronic Music, also happens to be one of the best for its comprehensiveness and witty tone. Another high achiever, The Universe of Miles Davis, released on what would have been Davis’ 90th birthday, is more focused but no less dense a collection of names, record labels, styles, etc.

While visualizing the history of any form of music can result in a significant degree of complexity, depending on how deeply one drills down on the specifics, jazz might seem especially challenging. Choosing one major figure pulls up thousands of connections. As these multiply, they might run into the millions. But somehow, one of the best music data visualizations I’ve seen yet—Pratt Institute’s Linked Jazz project—accounts seamlessly for what appears to be the whole of jazz, including obscure and forgotten figures and interactive, dynamic filters that make the history of women in jazz more visible, and let users build maps of their own.




Jazz musicians “are like family,” Zena Latto, one of the musicians the project recovered, told an interviewer in 2015. A multi-racial, transnational, actively multi-generational family that meets all over the world to play together constantly, that is. As a form of music built on ensemble players and journeymen soloists who sometimes form bands for no more than a single album or tour, jazz musicians probably form more relationships across age, gender, race, and nationality than those in any other genre.

That organic, built-in diversity, a feature of the music throughout its history, shows up in every permutation of the Linked Jazz map, and comes through in the recorded interviews, performances, and other accompanying info linked to each musician. Like the Universe of Miles Davis, Linked Jazz leans heavily on Wikipedia for its information. And in using such “linked open data (LOD),” as Pratt notes in a blog post, the project “also reveals archival gaps. While icons such as John Coltrane and Miles Davis have large digital footprints, lesser-known performers may barely have a mention”—despite the fact that most of those players, at one time or another, played with, studied under, or recorded with the greats.

Such was the case with Latto, who was mentored by Benny Goodman and toured throughout the 1940s and 50s with the International Sweethearts of Rhythm, “considered the first integrated all-women band in the United States.” Latto was “part of a network that stretched from New York to New Orleans,” but her name had disappeared entirely until Pratt School of Information professor Cristina Pattuelli found it on a tattered flyer for a Carnegie Hall concert. “Soon, through Linked Jazz, Lotta had a Wikipedia page and her interview was published on the Internet Archive.”

Linked Jazz’s focus on women musicians does not mean gender segregation, but a rediscovery of women's place in all of jazz.  Like all of the other filters, the Linked Jazz data map’s gender view shows both men and women prominently in the little photo bubbles connected by webs of red and blue lines. But as you begin clicking around, you will see the perspective has shifted. “Linked Jazz has concentrated on processing more interviews with women jazz musicians,” writes Pratt, “and these resources have been enhanced by a series of Women of Jazz Wikipedia Edit-a-thons in 2015 and 2017.”

Likewise, the inclusion of these interviews, biographies, and recordings have enhanced the breadth and scope of Linked Jazz, which as a whole represents the best intentions in open data mapping, realized by a design that makes exploring the daunting history of jazz a matter of strolling through a digital library with the entire catalog appearing instantly at your fingertips. The project also shows how thoughtful data mapping can not only replicate the existing state of information, but also contribute significantly by finding and restoring missing links.

via

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

Behold the New York City Street Tree Map: An Interactive Map That Catalogues the 700,000 Trees Shading the Streets of New York City

It may sound odd, but one of the things I miss most about living in New York City is the ability to hop on a bus or train, or walk a few blocks from home, and end up lounging in a forest, the cacophony of traffic reduced to a dim hum, squirrels bounding around, birds twittering away above. Such urban respites are plentiful in NYC thanks to its 10,542 acres of forested land, “about half as much as the Congaree Swamp in South Carolina,” notes James Barron at The New York Times, in one of the most densely populated urban areas in the country.

“Most of the city’s forest is deep in parks”—in Central Park, of course, and also Prospect Park and Riverside, and dozens of smaller oases, and the lush Botanical Gardens in the Bronx. The city’s forests are subject to the usual pressures other wooded areas face: climate change, invasive species, etc.




They are also dependent on a well-funded Parks Department and nonprofits like the Natural Areas Conservancy for the preservation and upkeep not only of the large parks but of the trees that shade city streets in all five boroughs.

Luckily, the city and nonprofit groups have been working together to plan for what the conservancy’s senior ecologist, Helen Forgione, calls “future forests,” using big data to map out the best paths for urban woodland. The NYC Parks department has been busy compiling figures, and you can find all of their tree stats at the New York City Street Tree Map, which “brings New York City’s urban forest to your fingertips. For the first time,” the Parks department writes, “you have access to information about every street tree in New York City.”

Large forested parks on the interactive map appear as flat green fields—the department has not counted each individual tree in Central Park. But the map gives us fine, granular detail when it comes to street trees, allowing users to zoom in to every intersection and click on colored dots that represent each tree, for example lining Avenue D in the East Village or Flatbush Avenue in Brooklyn. You can search specific locations or comb through citywide statistics for the big picture. At the time of this writing, the project has mapped 694,249 trees, much of that work undertaken by volunteers in the TreesCount! 2015 initiative.

There are many more trees yet to map, and the department’s forestry team updates the site daily. Out of 234 species identified, the most common is the London Planetree, representing 12% of the trees on the map. Other popular species include the Littleleaf Linden, Norway Maple, Pin Oak, and Ginko. Some other stats show the ecological benefits of urban trees, including the amount of energy conserved (667,590,884 kWh, or $84,279,933.06) and amount of carbon dioxide reduced (612,100 tons).

Visit the New York City Street Tree Map for the full, virtual tour of the city’s trees, and marvel—if you haven’t experienced the city’s vibrant tree life firsthand—at just how green the empire city’s streets really are.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

The 1855 Map That Revolutionized Disease Prevention & Data Visualization: Discover John Snow’s Broad Street Pump Map

No, he didn’t help defeat an implacable zombie army intent on wiping out all life. But English obstetrician John Snow seems as important as the similarly-named Game of Thrones hero for his role in persuading modern medicine of the germ theory of disease. During the 1854 outbreak of cholera in London, Snow convinced authorities and critics that the disease spread from a contaminated water pump on Broad Street, leading to the now-legendary infographic map above showing the incidences of cholera clustered around the pump.

Snow’s persistence resulted in the removal of the handle from the Broad Street pump and has been credited with ending an epidemic that claimed 500 lives. The Broad Street pump map has become “an enduring feature of the folklore of public health and epidemiology," write the authors of an article published in The Lancet. They also point out that, contrary to popular retellings, the “map did not give rise to the insight” that the pump and its germ-covered handle caused the outbreak. “Rather it tended to confirm theories already held by the various investigators.”




Snow himself published a pamphlet in 1849 called “On the Mode of Communication of Cholera” in which he argued that “cholera is communicated by the evacuations from the alimentary canal.” As he reminded readers of The Edinburgh Medical Journal in an 1856 letter, in that same year, “Dr William Budd published a pamphlet ‘On Malignant Cholera’ in which he expressed views similar to my own.” Germ theory had a long, distinguished history already, and Snow and his contemporaries made sound, evidence-based arguments for it.

But their position “largely went ignored by the medical establishment,” notes Randy Alfred at Wired, “and was opposed by a local water company near one London outbreak.” The accepted, mainstream scientific opinion held that all disease was spread through “miasma,” or bad air. Pollution, it was thought, must be the cause. After the pump handle’s removal, Snow published an 1855 monograph on waterborne diseases. This was the first public appearance of the legendary map—after the removal of the handle.

Helping to inform Snow’s map, another investigator, parish priest Henry Whitehead had “concluded that it was the washing of soiled diapers into drains which flowed to the communal cesspool that contaminated the pump and started the outbreak,” writes Atlas Obscura. Whitehead, a former critic of germ theory, later pointed out that the removal of the pump handle didn’t actually stop the epidemic, which, he said, “had already run its course” by that point.

Nonetheless, Snow and other proponents of the theory were vindicated, Whitehead had to admit, and Snow's intervention “had probably everything to do with preventing a new outbreak.” The simple, yet sophisticated data visualization would lead to radical new ways of conceptualizing disease outbreaks, helping to stop or prevent who knows how many epidemics before they killed hundreds or thousands. Snow’s map also deserves credit for giving “data journalists a model of how to work today.”

It was hardly the first or only data visualization of cholera outbreaks of the time. "As early as the 1830s," Visual Capitalist points out, "geographers began using spacial analysis to study cholera epidemiology." But Snow's was by far the most influential, and effective, of them all. In his TED talk above, journalist Steven Johnson (author of The Ghost Map:The Story of London's Most Terrifying Epidemic and How It Changed Science, Cities, and the Modern World) tells the story of how the outbreak, and Snow's theory and map, "helped create the world that we live in today, and particularly the kind of city that we live in today."

Read a Q&A with Johnson here; head over to The Guardian's Data Blog to see Snow's visualization recreated over a modern, satellite-view map of London and the Soho neighborhood of the famous Broad Street pump; and learn more about Snow and deadly cholera outbreaks in the crowded European cities of the early 19th century at the John Snow Archive and Research Companion online.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

Napoleon’s Disastrous Invasion of Russia Detailed in an 1869 Data Visualization: It’s Been Called “the Best Statistical Graphic Ever Drawn”

It’s tempting to associate data visualizations with PowerPoint and online graphics, which have enabled an unheard-of capacity for disseminating full-color images. But the form reaches much further back in history. Further back, even, than the front pages of USA Today and glossy sidebars of Time and
Newsweek. In 1900, for example, W.E.B. Du Bois made impressive use of several full-color data visualizations for the First Pan-African Conference in London, with no access whatsoever to desktop publishing software or a laser printer.

Almost fifty years before Du Bois turned statistics into swirls of color and shape, Florence Nightingale used her little-known graphic design skills to illustrate the causes of disease in the Crimean War and John Snow (not Jon Snow) illustrated his revolutionary Broad Street Pump cholera theory with a famous infographic street map.




Around this same time, another data visualization pioneer, Charles Joseph Minard, produced some of the most highly-regarded infographics ever made, including the 1869 illustration above of Napoleon’s march to, and retreat from, Moscow in the War of 1812. View it in a large format here.

Made fifty years after the event, when Minard was 80 years old, the map has been called by the bible of data visualization studies—Edward Tufte’s The Visual Display of Quantitative Information—“probably the best statistical graphic ever drawn.” Over at thoughtbot.com, Joanne Cheng sums up the context, if you needed a historical refresher: “The year is 1812 and Napoleon is doing pretty well for himself. He has most of Europe under his control, except for the UK.”

Angered by Czar Alexander’s refusal to support a UK trade embargo to weaken their defenses, Napoleon “gathers a massive army of over 400,000 to attack Russia.” The campaign was disastrous: overconfident advances on Moscow turned into devastating wintertime retreats during which the Grande Armée only “narrowly escaped complete annihilation.” So, how does Minard’s 1869 Tableau Graphique tell this grand story of hubris and icy carnage? And, Cheng asks, “what makes it so good?”

Cheng breaks Minard’s series of jagged lines and shapes down into more conventional XY axis line graphs to show how he coordinated a huge amount of information, including the locations (by longitude) of different groups of Napoleon’s troops at different points in time, their direction, and the precipitously falling temperatures in the stages of retreat. He drew from a list of the best historical sources he could consult at the time, turning dense prose into the spare, clean lines that set data scientists’ hearts a-flutter.

Minard began his career in a much more recognizably 19-century design field, building bridges, dams, and canals across Europe for the first few decades of the 1800s. As a civil engineer “he had the good fortune to take part in almost all the great questions of public works which ushered in our century,” noted an obituary published in Annals of Bridges and Roads the year after Minard’s death in 1870. “And during the twenty years of retirement, always au courant of the technical and economic sciences, he endeavored to popularize the most salient results.”

He did so by venturing outside the subject of engineering, while using the “innovative techniques he had invented for the purpose of displaying flows of people” on paper, writes Michael Sandberg at DataViz. In order to tell the tragic tale” of Napoleon’s crushing defeat “in a single image,” Minard imagined the event as a dynamic physical structure.

Minard’s chart shows six types of information: geography, time, temperature, the course and direction of the army’s movement, and the number of troops remaining. The widths of the gold (outward) and black (returning) paths represent the size of the force, one millimetre to 10,000 men. Geographical features and major battles are marked and named, and plummeting temperatures on the return journey are shown along the bottom.

This was hardly Minard’s first infographic. In fact, he made “scores of other graphics and charts,” National Geographic writes, “as well as nearly 50 maps. He pioneered several important thematic mapping techniques and perfected others, such as using flow lines on a map.” (See other examples of his work at National Geographic’s site.) Minard may not be much remembered for his infrastructure, but his ability, as his obituarist wrote, to turn “the dry and complicated columns of statistical data” into “images mathematically proportioned” has made him a legend in data science history circles.

Again, view Minard's visualization of Napoleon's failed invasion in a large format here.

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Josh Jones is a writer and musician based in Durham, NC. Follow him at @jdmagness

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