The flurry of COVID-19 maps that have emerged in the first half of this year will be something that future cartographers and librarians will look back on, both in terms of historical records that need preserving, which is the subject of this CityLab interview with Library of Congress map librarian John Hessler, and in terms of best practices for disease mapping—what to do and what not to do when mapping a pandemic—which is the subject of this Financial Times video interview with Kenneth Field. (Both from early May; I’m playing catchup right now.)
Tomorrow (23 April 2020), the Library of Congress will be livestreaming No One Was Immune: Mapping the Great Pandemics from Columbus to COVID-19, in which John Hessler and Marie Arana will “discuss the sweep of history from the 1500s smallpox pandemic that decimated the indigenous population of the Americas to the meticulous work that is being done now to map COVID-19.” To be streamed on the Library’s Facebook page and YouTube channel at 7 PM EDT. [WMS]
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.
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 (@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.
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.)
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.
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.
The Washington Post has mapped the spread of the new strain of coronavirus, which appeared last month in Wuhan and has since spread. They’ll be updating the map, and this won’t be the only map tracking the disease, so this isn’t the final word on the subject.
The New York Times maps confirmed measles cases in the United States as of April 29, 2019. “Measles was declared eliminated in the United States in 2000 but the highly contagious disease has returned in recent years in communities with low vaccination rates. The number of cases reported this year is already nearly double last year’s count and has surpassed the previous post-elimination high of 667 cases in 2014.”
The Atlantic’s Ed Yong looks at a problem in the public health response to this month’s Ebola virus outbreak in the Democratic Republic of Congo: inaccurate maps of the areas affected by the virus.
On Thursday, the World Health Organization released a map showing parts of the Democratic Republic of the Congo that are currently being affected by Ebola. The map showed four cases in Wangata, one of three “health zones” in the large city of Mbandaka. Wangata, according to the map, lies north of the main city, in a forested area on the other side of a river.
That is not where Wangata is.
“It’s actually here, in the middle of Mbandaka city,” says Cyrus Sinai, indicating a region about 8 miles farther south, on a screen that he shares with me over Skype.
Almost all the maps of the outbreak zone that have thus far been released contain mistakes of this kind. Different health organizations all seem to use their own maps, most of which contain significant discrepancies. Things are roughly in the right place, but their exact positions can be off by miles, as can the boundaries between different regions. […]
To be clear, there’s no evidence that these problems are hampering the response to the current outbreak. It’s not like doctors are showing up in the middle of the forest, wondering why they’re in the wrong place. “Everyone on the ground knows where the health zones start and end,” says Sinai. “I don’t think this will make or break the response. But you surely want the most accurate data.”
Data from NASA’s earth-observing satellites is being used to predict future malaria outbreaks in the Amazon rainforests of Peru. To be sure, as the above video shows, this is really about taking geospatial and remote sensing data from several different sources and correlating them to build a predictive model: it’s John Snow’s cholera map at large scale and for the satellite age.