In 1991, a Syracuse University Geography professor published a book titled "How to Lie with Maps". It was aimed at the rapidly-emerging community of GIS professionals, students in the mapping sciences, and the non-technical managers responsible for overseeing their technical work. The author's focus is on how cartographic decisions - what to include on a map, what to omit, what scale to use, what design elements to emphasize/deemphasize, color choice, etc. - are tantamount to editorial decisions. The same information can be used to give wildly different impressions to the viewer with just a little cartographic sleight of hand.
The book's take home message: Lying with maps is pretty easy.
The COVID-19 outbreak provides an interesting example of data-driven mapping in a politically-charged environment where message often trumps data. The default map scale (the entire world) is employed here to convey a clear message: Its bad out there. But a simple change in scale conveys a somewhat different one.
Red circles in the maps above show the cumulative number of confirmed cases of COVID-19 at the scale of the continent. The map's message: Its bad out there. Stay home.
Zooming in to the Western U.S., the map conveys a completely different impression.
The map's message: COVID-19 is a big city problem.
Another jump in scale puts us into my local community, an area within a 150-miles of Flathead Lake, Montana. Map message: I think I can see 2 red dots. Can you see any others? Is that one?
While I do not believe the Center for Systems and Engineering (CSSE) at Johns Hopkins is lying with their maps of Coronavirus infections, it is pretty clear that viewing the same data at different scales gives the viewer wildly different impressions of the outbreak. At one scale you get spin (i.e., what does Cleveland have to do with Tacoma?). At another, you get useful information (i.e., There are 4 known cases in my County).
How do day-to-day decisions change if presented one map is shown, but not the other?
Its often said that 80% of information has a geographical component. Disease is no different. The maps above show population density to a large extent determines your risk of contracting COVID-19 over the short term. Over the long term, we all will be exposed. Non-geographic information such as age and whether a patient has a pre-existing ailment also factor in. In fact, co-morbidities likely play a much larger role in determining if a person will die from COVID-19 or recover.
But the cartographer has chosen not to show us those data.
Shutdown will not reduce the total number of people who will eventually contract and be harmed by the virus. Worldwide data already indicates a small percentage of a country's population will die or be permanently harmed by the COVID-19 virus.
"Flattening the curve" gives hospitals more time to deal with the initial pulse of infected people needing care. Herd immunity, which develops over many months, means that the entire population will eventually be exposed. The area beneath the two curves (unflattened, flattened) represents the total number of infections. The areas are equal.
Monmonier, M. (1991) How to Lie with Maps, University of Chicago Press, ISBN-13: 978-0226435923
Center for Systems and Engineering (CSSE), Coronavirus COVID-19 Global Cases, 7 April 2020, 7:30 am