The Long Island feline residents volunteered—or more accurately, were volunteered—by their human companions to participate in a domestic cat movement study as part of the international Cat Tracker project.
Each beast was outfitted with a GPS tracker-enhanced harness, which they wore for a week.
(Many cat owners will find that alone something of an achievement.)
Scientists were particularly interested to learn the degree of mayhem these cherished pets were visiting on surrounding wildlife in their off hours.
Anyone who’s been left a present of a freshly murdered baby bunny, mole, or wingless bat can probably guess.
It’s a considerable amount, though by and large the domesticated participants stuck close to home, rarely traveling more than two football fields away from the comforts of their own yards. The impulse to keep the food bowl within easy range confines their hunting activities to a fairly tight area. Woe to the field mice who set up shop there.
Their movements also revealed the peril they put themselves in, crossing highways, roads, and parking lots. Researcher Heidy Kikillus, who tracked cats in New Zealand, reported that a number of her group’s subjects wound up in a fatal encounter with a vehicle.
Generally speaking, gender, age, and geography play a part in how far a cat roams, with males, younger animals, and country dwellers covering more ground. Unsurprisingly, those who have not been neutered or spayed tend to have a freer range too.
“Without the motivations of food and sex, most cats seem content to be homebodies,” zoologist Roland Kays, one of the US Project leaders, noted.
Open access publishing has, indeed, made academic research more accessible, but in “the move from physical academic journals to digitally-accessible papers,” Samantha Cole writes at Vice, it has also become “more precarious to preserve…. If an institution stops paying for web hosting or changes servers, the research within could disappear.” At least a couple hundred open access journals vanished in this way between 2000 and 2019, a new study published on arxiv found. Another 900 journals are in danger of meeting the same fate.
The journals in peril include scholarship in the humanities and sciences, though many publications may only be of interest to historians, given the speed at which scientific research tends to move. In any case, “there shouldn’t really be any decay or loss in scientific publications, particularly those that have been open on the web,” says study co-author Mikael Laasko, information scientist at the Hanken School of Economics in Helsinki. Yet, in digital publishing, there are no printed copies in university libraries, catalogued and maintained by librarians.
To fill the need, the Internet Archive has created its own scholarly search platform, a “fulltext search index” that includes “over 25 million research articles and other scholarly documents” preserved on its servers. These collections span digitized and original digital articles published from the 18th century to “the latest Open Access conference proceedings and pre-prints crawled from the World Wide Web.” Content in this search index comes in one of three forms:
public web content in the Wayback Machine web archives (web.archive.org), either identified from historic collecting, crawled specifically to ensure long-term access to scholarly materials, or crawled at the direction of Archive-It partners
digitized print material from paper and microform collections purchased and scanned by Internet Archive or its partners
general materials on the archive.org collections, including content from partner organizations, uploads from the general public, and mirrors of other projects
Academic publishing boasts one of the most rapacious legal business models on the global market, and one of the most exploitative: a double standard in which scholars freely publish and review research for the public benefit (ostensibly) and very often on the public dime; while private intermediaries rake in astronomical sums for themselves with paywalls. The open access model has changed things, but the only way to truly serve the “best interests of researchers and the public,” neuroscientist Shaun Khoo argues, is through public infrastructure and fully non-profit publication.
Maybe Internet Archive Scholar can go some way toward bridging the gap, as a publicly accessible, non-profit search engine, digital catalogue, and library for research that is worth preserving, reading, and building upon even if it doesn’t generate shareholder revenue. For a deeper dive into how the Archive built its formidable, still developing, new database, see the video presentation above from Jefferson Bailey, Director of Web Archiving & Data Services. And have a look at Internet Archive Scholarhere. It currently lacks advanced search functions, but plug in any search term and prepare to be amazed by the incredible volume of archived full text articles you turn up.
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.
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…
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.
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.
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.
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.
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.”
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