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.
The European Space Agency maps the drop in nitrogen dioxide concentrations in the atmosphere in the wake of coronavirus lockdowns in many countries (see above). [GIS Lounge]
Meanwhile, CESBIO researcher Simon Gascoin built a map that compares NO2 concentrations over the last 30 days with the same period in 2019.
Data for these analyses generally come from the Copernicus Programme’s Sentinel-5P satellite. The Copernicus Atmosphere Monitoring Service issued a warning last week about using the data improperly.
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.
Previously: Emissions Drop Due to Coronavirus Outbreak.
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].
The New York Times maps the number of cases at the global level and for the United States. It’s also making available county-level coronavirus data assembled from various states and counties, since there seems to be no single agency tracking this at the national level.
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
— Tectonix GEO (@TectonixGEO) March 25, 2020
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.
Gareth Fuller, whom we first heard about thanks to his masterpiece London Town, is in the news again. Now based in Beijing, he found himself forced to self-quarantine for 14 days after returning from a trip to Kuala Lumpur. Fuller has mapped every place he has lived, so he spent his two weeks of isolation creating quarantine maps—one for each day. The maps are claustrophobic—his apartment is 590 square feet—metaphorical, even fantastical. They’re very much on-point in this age of self-isolation and social distancing. They’re available as a set of postcards for £14, which is considerably cheaper than his other limited edition prints.
The Washington Post maps COVID-19 cases by U.S. state and country (above).
Maps Mania also has a list of official government coronavirus maps.
(See my other posts about COVID-19 for maps I’ve already linked to.)
As you may have seen elsewhere, the coronavirus pandemic is having an impact on air pollution, as countries shut down human and economic activity in an attempt to deal with the outbreak. Take nitrogen dioxide. Tropospheric NO2 density decreased significantly over China between January and February, and the same seems to be happening in northern Italy, which normally has some of the most severe air pollution in Europe. See the ESA’s animation:
Previously: Mapping Nitrogen Dioxide Pollution.
Hackers have created fake coronavirus map websites that install malware on users’ computers. According to Reason Security’s analysis, the websites resemble the coronavirus map dashboards produced by legitimate organizations, but prompt users to download an application: the application activates a known malicious piece of malware called AZORult, which collects browser information (cookies, browser histories, IDs and passwords). Not terribly surprising that bad actors are trying to exploit a crisis, but depressing all the same. More at Business Insider, The Hacker News and TechRadar.
Kenneth Field has made no bones about his frustration with maps of the COVID-19 outbreak, many of which have presented data in ways that are at best misleading. A simple choropleth map isn’t always simple. He’s put his thoughts on what not to do, and what to do instead, in this Twitter thread, and followed that up with this article on the ArcGIS blog.
We live in an amazing time as far as cartography is concerned. Technology allows, and actively supports rapid, democratized mapping. Data, compiled and published in near real-time (if not actual real-time) encourages people to get their hands dirty to see what they can make. Media outlets all rush to provide their audience with fast, visible content. Social media drives sharing of these maps at a breathtaking pace. When you throw in a developing human health story the ingredients are ripe for maps to take centre stage, as they have become with the ongoing coronavirus outbreak. Let’s take a look at how maps can help shape the narrative and, as concern (fear?) grows, how to map the data responsibly.
BBC News: “Here’s how a decade-old map showing global air travel was used incorrectly by news websites across the world, leading to headlines such as ‘New map reveals no country safe from coronavirus tentacles’ and ‘Terrifying map reveals how thousands of Wuhan travellers could have spread coronavirus to 400 cities worldwide.’” Blame the usual culprits. [Kenneth Field]
Yet another interactive map tracking the spread of the COVID-19 coronavirus, this one from Dr. Edward Parker of the London School of Hygiene and Tropical Medicine. It compares COVID-19 to other recent outbreaks, with map layers showing the spread of H1N1, SARS, and the 2014 Ebola outbreak. [Maps Mania]
CityLab’s Marie Patino looks at some of the maps tracking the spread of the COVID-19 coronavirus and from there pivots to some of the ways we’ve tracked disease outbreaks and epidemics in the past. Examples can be found as far back as the 17th century—long before John Snow’s cholera map, in other words.
Related reading: Tom Koch’s Disease Maps: Epidemics on the Ground (University of Chicago Press, 2011) and Cartographies of Disease (Esri Press, 2nd ed 2016), Sandra Hempel’s Atlas of Disease (White Lion, 2018) and, of course, The Ghost Map by Steven Johnson (2006).
Another interactive map tracking novel coronavirus infections, this one from University of Washington geographer Bo Zhao. Like the Johns Hopkins map (previously), it compiles information from multiple sources.
The country-level data is collected from WHO, while the data of each province in China is collected from multiple sources such as China’s NHC, Mapmiao and Baidu. Notably, we also refer to CDC to verify the virus spreading status in the U.S. To make a timely data and map updates, we collect the data every 4 hours, and verify the data quality per day. In addition, we plan to provide finer-scale data from China (the county level), U.S. (the state level) and Canada (the province level) in the next version.
Johns Hopkins University’s CSSE has created an interactive map and online dashboard to track the spread of the Wuhan coronavirus. Details at their blog post:
The case data visualized is collected from various sources, including WHO, U.S. CDC, ECDC, China CDC (CCDC), NHC and DXY. DXY is a Chinese website that aggregates NHC and local CCDC situation reports in near real-time, providing more current regional case estimates than the national level reporting organizations are capable of, and is thus used for all the mainland China cases reported in our dashboard (confirmed, suspected, recovered, deaths). U.S. cases (confirmed, suspected, recovered, deaths) are taken from the U.S. CDC, and all other country (suspected and confirmed) case data is taken from the corresponding regional health departments. The dashboard is intended to provide the public with an understanding of the outbreak situation as it unfolds, with transparent data sources.
Meanwhile, the Washington Post has created a series of maps showing where the outbreak started and the nearby areas at risk.
Update, 31 Jan: Maps from the New York Times.