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]
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.
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.
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.
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.