Jonathan Crowe blogs about maps at The Map Room. His essays and reviews have been published by AE, Calafia, The New York Review of Science Fiction, the Ottawa Citizen, Strange Horizons and Tor.com. He lives in Shawville, Quebec.
We’re inviting readers to draw a map of your life, community, or broader world as you experience it under coronavirus. Your map can be as straightforward or subjective as you wish. You might show key destinations, beloved neighbors, a new daily routine, the people or restaurants you miss, the future city you hope to see, or anything else that’s become important to you right now. It might even be a map of your indoor life. For an added challenge, try drawing from memory.
Deadline is 20 April, with a selection of submissions to be featured in a future article.
The March 2020 issue (PDF) of Calafia, the journal of the California Map Society, has as its theme the mapping of space. It also has something from me in it: my review of the third edition of Nick Kanas’s Star Maps: History, Artistry, and Cartography. An excerpt:
It’s important to remember a book’s target audience—its imagined ideal reader. In the case of Star Maps this is Kanas’s younger self, who came to map collecting via his lifelong interest in amateur astronomy. “I was frustrated that there was not a single book on celestial cartography that could inform me about the various aspects of my collecting,” he writes in the preface to the first edition. “What I needed was a book that not only was a primer for the collector but also had sufficient reference detail to allow me to identify and understand my maps. Nothing like this appeared, so I decided to write such a book some day” (p. xxi). In other words, for a compendium this is a surprisingly personal book, one that reflects his own journey into the subject and, presumably, his interests as a collector.
I’ll post the full review on The Map Room once I’ve checked my draft against the published copy. In the meantime, check out the issue of Calafia (PDF) in which it appears. (Update, 24 Jun 2020: Here it is.)
CityLab maps the percentage of U.S. households with no internet access by school district—an increasingly important number as schools close to deal with the COVID-19 pandemic, and explore online classes as an alternative delivery system in the meantime. That’s a problem for kids who don’t have internet at home—and an even bigger problem where more kids are in that situation.1
ArcGIS-based dashboards tracking the spread of the novel coronavirus are now reasonably common, but the first was produced by Johns Hopkins University’s Center for Systems Science and Engineering. As Nature Index reports in this behind-the-scenes look at the JHU coronavirus dashboard, the decision to launch was spur of the moment, but now the dashboard and its underlying data get more than a billion hits every single day, and it is now managed by a team that numbers nearly two dozen. [GIS Lounge]
Este Geraghty, Esri’s chief medical officer, suggests five ways that maps can help communities respond to COVID-19. Very much in a GIS context: putting data on a map and letting users—officials in this case—make decisions based on that data.
Daniel Huffman finally finished a map he’d been working on, off and on (though mostly off), for years. Landforms of Michigan appeared in draft form on this 2016 blog post about mapping terrain using Photoshop layers; last week, Daniel says, “I finally overcame my inertia enough to finish it.” It’s available as a large poster on Zazzle.
The National Maritime Museum is closed right now, obviously, but in the meantime they’ve given us a virtual version of one of their treasures: a 1541 table globe by Gerardus Mercator.
Concentrations of NO2 in the atmosphere are highly variable in space and time: they typically vary by one order of magnitude within each day and quite substantially from one day to another because of the variations in emissions (for example the impacts of commuter traffic, weekdays and weekend days) as well as changes in the weather conditions. This is why, even if observations are available on a daily (currently available from satellites) or even hourly (ground-based observations) basis, it is necessary to acquire data for a substantial period of time in order to check that a statistically robust departure from normal conditions has emerged.
Cloud cover is a factor that needs to be taken into account as well.
Three versions are available: a 42×52-inch (107×132.5-cm) poster, a 44×54-inch (111.7×138.3-cm) giclée print in a limited edition of 1,200, and a 48×59-inch (121×149.6-cm) giclée print in a limited edition of 400. Prices will be shown in your local currency: in Canadian dollars they’re $95, $490 and $765, respectively. These are discounted prices for pre-orders. Shipping outside Australia will be by UPS (I was quoted a shipping fee of US$35 at checkout), and will begin on April 16.
My main concerns are where I’m going to put it, and how I’m going to have it framed. But I’ll worry about that later.
Cartographer Hans van der Maarel, of Red Geographics, is interviewed about what it’s like to be a cartographer for a podcast called Discover Your Talent—Do What You Want. I’ve never heard of this podcast, but seems to focus on careers and career planning, which explains some of the questions. The episode is thirty minutes long. [via]
On a personal level, the coronavirus map I stare at the most is the one closest to home: a dashboard that shows the regional incidence of COVID-19 in Quebec. Maintained by two geographers at Laval University, it’s extremely helpful in that it shows the per capita rate as well as the raw numbers, which highlights (for example) just how many cases there are in the Eastern Townships, and how few there are here in the Outaouais, as a percentage of the population. [Le Droit]
Less helpful is New York City’s map showing the percentage of patients testing positive for COVID-19, because its neighbourhood detail is so difficult to interpret, as Patch’s Kathleen Culliton points out. “Neighborhoods are designated by numbers instead of name—408 is Jamaica, Queens, by the way—and the percentages are not connected to population data but to those tested. The number of people tested per zone? Not included. The population [per] zone? Not included.” [Kenneth Field]
It’s hard to maintain social distancing in a dense urban environment like New York, but that doesn’t mean that rural areas are inherently safer. Identifying areas that would be hit harder by the coronavirus can be a factor of age and various social vulnerability factors (such as poverty and vehicle access); John Nelson looks at the intersection of age and social vulnerability in this StoryMap and this blog post. The Washington Post’s maps of vulnerability are based on age and flu rates. A third example is Jvion’s COVID Community Vulnerability Map, which is based on anonymized health data from some 30 million Americans [ZDNet].
Want to see the true potential impact of ignoring social distancing? Through a partnership with @xmodesocial, we analyzed secondary locations of anonymized mobile devices that were active at a single Ft. Lauderdale beach during spring break. This is where they went across the US: pic.twitter.com/3A3ePn9Vin
Failing to observe social distancing makes the pandemic worse. You might have seeen Tectonix’s video on Twitter, drawn from the location data of mobile devices that were active at a single beach in Florida over spring break, and followed them home. As CTV News reports, the video has drawn fire from privacy advocates, though Tectonix asserts that the data was anonymized and collected with user consent. Meanwhile, the New York Times explores several scenarios of coronavirus spread, comparing what might happen with some control measures, more severe control measures, and no action taken at all.
Cheung and Lee plotted the orbits of navigation satellites from the United States’s Global Positioning System and two of its counterparts, Europe’s Galileo and Russia’s GLONASS system—81 satellites in all. Most of them have directional antennas transmitting toward Earth’s surface, but their signals also radiate into space. Those signals, say the researchers, are strong enough to be read by spacecraft with fairly compact receivers near the moon. Cheung, Lee and their team calculated that a spacecraft in lunar orbit would be able to “see” between five and 13 satellites’ signals at any given time—enough to accurately determine its position in space to within 200 to 300 meters. In computer simulations, they were able to implement various methods for improving the accuracy substantially from there.
A mini-network of relays—a couple of satellites in lunar orbit, say—could improve accuracy further. [Geography Realm]
Star Maps: History, Artistry, and Cartography
