The New York Times maps COVID-19 cases at U.S. colleges and universities. The map and searchable database are based on their survey of more than 1,600 post-secondary institutions; the survey “has revealed at least 88,000 cases and at least 60 deaths since the pandemic began. Most of those deaths were reported in the spring and involved college employees, not students. More than 150 colleges have reported at least 100 cases over the course of the pandemic, including dozens that have seen spikes in recent weeks as dorms have reopened and classes have started.”
So many COVID-19 maps: some misleading, some mislabelled or with other design flaws, some lacking key information, some misunderstood or misused. On GIS Lounge, Mark Altaweel explores how the COVID-19 “infodemic”—the overabundance of information, some reliable, some not—has manifested itself in online coronavirus maps.
Accessible via the WHO’s European COVID-19 dashboard, the European Region COVID19 Subnational Explorer maps the incidence of COVID-19 in Europe on a cases-per-100,000-population basis, with layers showing the 7-day, 14-day and cumulative numbers. The site notes that national public health authorities use different criteria and the numbers are not necessarily usefully comparable. Even so. [Maps Mania]
In How to Lie with Maps, Mark Monmonier warns that map readers “must watch out for statistical maps carefully contrived to prove the points of self-promoting scientists, manipulating politicians, misleading advertisers, and other propagandists. Meanwhile, this is an area in which the widespread use of mapping software has made unintentional cartographic self-deception inevitable.”1
So which of these two scenarios—careful contrivance or unintentional self-deception—is at play on the Georgia Department of Public Health’s COVID-19 daily status report page?
In just 15 days the total number of #COVID19 cases in Georgia is up 49%, but you wouldn’t know it from looking at the state’s data visualization map of cases. The first map is July 2. The second is today. Do you see a 50% case increase? Can you spot how they’re hiding it? 1/ pic.twitter.com/wAgFRmtrPk
— Georgia Person (@andishehnouraee) July 17, 2020
Twitter user @andishehnouraee notes the difference in scale between two county-by-county COVID-19 maps of Georgia. The earlier map maxes out at 4,661 cases per 100,000, the later (and as of this writing, current) map maxes out at 5,165 cases per 100,000. As they point out, there has been a 49 percent rise in total COVID-19 cases between the two maps, but you wouldn’t know it at a glance, because the scales have changed in the meantime.
Is this, as @andishehnouraee suggests, a concerted attempt to hide the severity of the outbreak in Georgia—or, as T. J. Jankun-Kelly thinks might be the case, something that happens when you max out the old scale. In other words: bad faith or bad design? (Or both: it can be both.)
Update 19 Jul: See Twitter threads from Darrell Fuhriman and Jon Schwabish disagreeing with critiques of the Georgia Public Health maps. It’s worth clarifying that only one map is ever viewable at the website: the map’s scale has changed over time, but it’s not like they’re side-by-side except in @andishehnouraee’s tweet.
Wearing a mask in public is increasingly being encouraged or required as a measure to slow the spread of COVID-19. The New York Times maps the rate of mask wearing in the United States. The county-level map is based on more than 250,000 responses to a survey conducted in early July, in which interviewees were asked how often they wore a mask in public.
The map shows broad regional patterns: Mask use is high in the Northeast and the West, and lower in the Plains and parts of the South. But it also shows many fine-grained local differences. Masks are widely worn in the District of Columbia, but there are sections of the suburbs in both Maryland and Virginia where norms seem to be different. In St. Louis and its western suburbs, mask use seems to be high. But across the Missouri River, it falls.
The COVID-19 Event Risk Assessment Planning Tool is a county-by-county map of the U.S. that shows the risk of coming into contact with a COVID-positive individual at an event. “This site provides interactive context to assess the risk that one or more individuals infected with COVID-19 are present in an event of various sizes. The model is simple, intentionally so, and provided some context for the rationale to halt large gatherings in early-mid March and newly relevant context for considering when and how to re-open.” A slider changes the size of the event; risk goes up dramatically with bigger events, of course. Which you’d think would be intuitively obvious. You’d really think so, wouldn’t you. [Cartophilia]
COVD-19 is hitting the United States very hard right now. This interactive map from the Harvard Global Health Institute measures COVID-19 risk at the county level. The four colour-coded risk levels are based on a seven-day rolling average of new COVID-19 cases per 100,000 people: less than one means green (“on track for containment”); more than 25 means red (“tipping point”). It’s explained here. [Matthew Edney]
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 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.
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.