On February 21 the U.S. reached a grim milestone: half a million deaths due to COVID-19. This NBC News interactive map visualizes the scale of this tragedy with a county-by-county dot map of those deaths, and offers narrative detail in certain places. [Maps Mania]
The New York Times maps the distribution of COVID-19 cases in Los Angeles. “County officials recently estimated that one in three of Los Angeles County’s roughly 10 million people have been infected with Covid-19 since the beginning of the pandemic. But even amid an uncontrolled outbreak, some Angelenos have faced higher risk than others. County data shows that Pacoima, a predominantly Latino neighborhood that has one of the highest case rates in the nation, has roughly five times the rate of Covid-19 cases as much richer and whiter Santa Monica.”
The New York Times maps the risk of getting COVID-19 in the United States on a county-by-county basis (previously: Mapping COVID-19 Exposure Risk at Events). [Maps Mania]
Now that vaccines are available, they can be mapped as well. The U.S. Centers for Disease Control’s COVID Data Tracker includes this map of total doses administered in the U.S.; this NBC News county-level map showing the percentage of Americans living within 50 miles of a pharmacy expected to carry a vaccine dates from December and is probably out of date by now. [Maps Mania]
According to a survey, more than a quarter of the U.S. population would not get a COVID-19 vaccine if it was available to them. This number is not evenly distributed: this map from MIT Technology Review, presented as a map showing whether your neighbours want to get vaccinated, reveals the regional pockets of vaccine hesitancy (see above). (What the actual hell, Louisiana?)
The thing that seems to worry authorities most about COVID-19 is its potential to overwhelm hospitals, at which point the mortality rate really begins to shoot up. In March, the University of Minnesota’s Carlson School of Management released the COVID-19 Hospitalization Tracking Project, which maps, on a county-by-county, basis, the percentage of hospital and ICU beds occupied by COVID-19 patients. [Maps Mania]
Innouveau’s Corona Status Maps are simple yet effective: they show the rate of positive tests at the national, regional, county or city level, depending on the map. They’re animated and have responsive sliders to quickly show how the positivity rate has changed over time; clicking on a region gives a bit more detail as well. With maps of the Netherlands, Amsterdam, the Hague, Rotterdam, the Netherlands plus Germany, Central Europe and Europe, there’s a distinct emphasis in the maps’ focus. [Maps Mania]
Rather than applying restrictions across their entire jurisdictions, several authorities are designating zones to target measures to prevent the spread of novel coronavirus where the spread is at its greatest. Maps can quickly indicate not only where COVID is at its worst, but also where restrictions have been put into place. Two examples: New York City (above left) and the province of Quebec (above right). New York’s map is interactive and has an address search, whereas Quebec’s map is spectacularly ungranular: diagonal lines show that a region has more strict restrictions in some areas but not others, but does not map those areas (which are indicated in text).
Ottawa Public Health has partnered with the Ottawa Neighbourhood Study to produce this interactive map of COVID-19 rates in Ottawa’s neighbourhoods. Both the map and its underlying data are subject to many caveats: the differences between rural and urban zones, between where people live and where people are tested, and other factors affecting testing and susceptibility. Most notably, the map is updated only monthly, so the current map (screenshotted above) does not take into account the rapid increase in positive cases over the past week or two as Ottawa entered the second wave. [Ottawa Citizen]
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.”
The Food & Environment Reporting Network has a map of COVID outbreaks in the U.S. food system. “Since April, FERN has been closely tracking the spread of Covid-19 at meatpacking plants, food processing facilities, and farms. This dashboard is home to our latest reporting on Covid-19 cases and food system workers, and is updated each weekday.” [via]
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?
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]