Corona-cartography: what we learned from a year of COVID-19 maps
By: Alan Mcconchie
Tags: Cartography, Pandemic, Covid-19, Maps, Coronavirus

In our last post looking back at the data visualization trends of the coronavirus pandemic, we focused mainly on charts and diagrams, and less on maps and cartography. By and large, the maps of the pandemic were predictable and familiar choropleths and proportional dot maps that did their jobs well and didn’t call attention to themselves. Inevitably, though, there were many examples of misleading maps that failed to follow the cartographic rules, or of maps that were widely misread by the public.

In this post we’ll look at a few of those maps gone wrong, and also look at some very well-done maps that pushed the limits of what we expect from pandemic cartography.

Dot Maps Misinterpreted

In our last post we discussed the popular Johns Hopkins coronavirus dashboard, which, despite being one of the most reliable sources of COVID data and charts, also had one of the most misinterpreted COVID maps. At times it felt like everyone on social media who works with maps found themselves at some point explaining to people how they were reading it wrong (we’re looking at you Lyzi Diamond and Alberto Cairo), ourselves included.

The main problem with the map was its inconsistent level of detail (showing counties in the US, but whole provinces in Canada) that gave many people an incorrect impression of the situation on either side of the border.

Pulitzer Prize winning journalist Laurie Garrett wrote:

“There is one place in the world where a map of #COVID19 cases & deaths reveals a straight line, with a huge amount of disease on one side, very little on the other. Where? The 3000+ mile border [between] US/Canada”.

While Garrett is correct that the spread of COVID was massively worse in the United States than in Canada, that’s not why the map looks like that. The following chart makes the case more persuasively and accurately:

Chart from Our World in Data COVID-19 Data Explorer

So what’s happening in that map? What is it actually showing? The map uses proportional symbols (the red dots), and in the US it has cases broken out by county, so there are lots of tiny dots spread out through the US. But for Canada the cases are aggregated by province, so there is only one dot in the center of each province. This gives the impression that the COVID cases stop at the border, which they do not. In actuality most of the Canadian cases would be clustered right along the north side of the border too, if we had them mapped more precisely than the provincial level.

The Johns Hopkins map gives the impression that Canada has only a few COVID hotspots, and those are scattered far away from the border, but this is not true. The data on the map is not wrong, but it is very misleading. Imagine if the tables were turned: if Canada’s rate were twice the US, but it was still only mapped by province in Canada and by county in the US, you would still see more red on the map in the US and think things were worse here.

In some cases, it’s better to tell the story with a chart, not a map.

Update: I went into more detail about how different levels of aggregation lead to very different results in a twitter thread here:

Choropleth maps gone wrong

One widely shared map example came from Georgia, held up as an example of “lying with statistics” that deliberately manipulated the classifications on their choropleth maps to hide worsening numbers. (For more about choropleths, check out our recent post by Curran Kelleher, “Some thoughts on multivariate maps”).

Two screenshots of the Georgia COVID maps from early July, via Dare Obasanjo

These Georgia maps were criticized in two ways: first, that the colors never changed, despite worsening numbers. However, this could just as easily have been the result of using the defaults in their GIS program. Even if the map maker had wanted to maintain the same class buckets, they would have needed to keep adding new colors to the map, pushing into different parts of the color spectrum as the number of cases skyrocketed beyond what anyone could have predicted at the start of the pandemic. Weather maps have the same problem, as climate change brings storms and heat waves that produce unprecedented temperatures and rainfall, requiring cartographers to add new colors to the end of their color scales.

Others critiqued the choice of colors of the map (a more convincing argument) which used calming blue colors for every class break except for the top bucket, which used red. A more accurate color ramp should have used a more gradual gradient from blue to red, as illustrated in this remake:

Georgia COVID map with an improved color gradient by ThinkFirst News

While it’s true that Georgia Governor Kemp’s administration was caught manipulating their weekly case numbers to make the pandemic in the state seem less severe, it’s not clear if the decisions made on these maps were part of that agenda, or just very poor design choices. Finally, Adam Pearce noted that the map had a line chart right next to it, which somewhat undermines the argument that the visualization is trying to hide the data.

screenshot of the entire Georgia COVID-19 dashboard on July 19, 2020, via Adam Pearce

This observation should give all of us pause; at Stamen we frequently combine maps and charts, especially when we think just one without the other can’t tell the whole story. In fact, we pride ourselves on that being something we almost always do, and we feel good about the fact that we’re giving the viewer a more nuanced picture by presenting all those possible perspectives. But seeing this outrage by people who look at a map out of context without considering the entire interface should be a lesson to all of us who make data dashboards.

Now almost a year later, did Georgia’s map makers take this criticism to heart? Well, it seems they got the memo. The map now has a new legend, and today’s map of cases doesn’t even use any blue color and has, um, a lot more red.

Mapping the progression of time

A lot of the controversy about the Georgia map can be traced back to the fundamental difficulty of showing time on a static map. Geographic maps are great at showing variation across two or three spatial dimensions, but with this strength comes a great weakness in representing the temporal dimension. This is why we devoted so much of our attention to time series charts during the pandemic, and not maps. Trying to understand change over time is especially problematic when we try to compare the legends on static maps created on two different days, as with the Georgia maps: these maps were simply not designed to be compared in that way!

Despite the inherent challenges, we appreciated the few maps that went beyond simple choropleths and dots, and attempted to communicate temporal change on a map. Two very different examples we liked were the Esri CovidPulse map that packed in more information with tiny sparkline charts for each state or county, and the New York Times’s use of evocative bug-like map markers to tell a map-based narrative of the virus’s spread: