Making sense of Covid-19 Maps
By: Kristin Hunter-Thomson
We all are trying to find our new normal as our lives and schools have been turned upside down. And we all are working to make sense of what Covid-19 is and how it may be progressing across the world and each country. Therefore it is not surprising that there are many maps in the news and online that people can access these days about Covid-19.
But what kinds of claims can we or can we not make from these maps? And how can we help our students make sense of these maps? Let’s explore some maps and discuss them from a teaching perspective.
Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Accessed at https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 on April 7, 2020 at 12:15 CET.
The “Coronavirus COVID-19 Global Cases” data dashboard created and updated by the Center for Systems Science & Engineering at Johns Hopkins University has a lot of information packed into it. Let’s just focus on the map in the center of the screen.
What kind of map is it? - This is a Proportional Symbol Map.
What is easier or harder about making sense of this map? -
-
The bubbles are spatially located over the area for which the data they represent are from, this helps us get a broad spatial sense of where confirmed cases of COVID-19 are located. But the size of the bubbles overlap one another making it hard to determine exactly where they are from. Also, the bubble location is centered in the geographic space it represents, not the actual location within that geographic space of the cases.
-
The bubble size indicates a numerical quantity (in this case how many confirmed cases). But it is the area of the bubble, not its diameter, that indicates the numerical quantity and in general we are not very good at comparing differences and similarities in areas, especially when of a circle.
What can we and what can we not take away from these data? -
-
We can take away… the geographic spread of where any confirmed cases of COVID-19 are from this map and a relative sense of which countries (or smaller spatial scale in the 4 nations with more details) have had more or less confirmed cases of COVID-19 to date.
-
We cannot take away… where exactly all existing cases of COVID-19 are from this map, nor how the number of cases spread and/or is spreading geographically across these locations over time, nor how many existing cases of COVID-19 there are total or in any specific geographic location.
Novel Coronavirus (COVID-19) Infection Map: Global Trend. Accessed at https://hgis.uw.edu/virus/ on April 7, 2020 at 12:40 CET.
The “Novel Coronavirus (COVID-19) Infection Map” data dashboard was created and is updated by Bo Zhao, Fengyu Xu, Lola Kang, Joshua Ji, and Steven Bao at the University of Washington’s Center for Studies in Demography and Ecology (CSDE). It has a lot of great information packed into it. Again, let’s just focus on the map in the center of the screen.
What kind of map is it? - This is a Choropleth Map.
What is easier or harder about making sense of this map? -
-
The locations for which the data represents a numerical attribute of that geographic location and we can see the geographic locations clearly in a way we are familiar with looking at the world map. But due to projecting a spherical Earth onto a flat 2D surface the area of these different geographic locations are distorted. For example, Sweden looks much larger than Spain, when it is the opposite.
-
The color indicates a numerical quantity (in this case the number of novel cases) across the geographic space it represents. A legend in the bottom left indicates that the colors represent. But, this implies that the number of cases for that geographic space are equally distributed throughout the area (by the consistent color scheme across it) when in fact there is finer scale distribution of where the cases are actually located.
What can we and what can we not take away from these data? -
-
We can take away… the geographic spread of where novel cases of COVID-19 are from this map and a relative sense of which countries (or states and provinces for some countries) have had more or less novel cases of COVID-19 to date.
-
We cannot take away… where exactly all existing cases of COVID-19 are from this map, nor how the number of cases spread and/or is spreading geographically across these locations over time, nor how many novel cases of COVID-19 there are total.
So where does that leave us with our using these, and many other maps, to help our students make sense of Covid-19?
We need to help our students be cognizant what they are actually looking at both in terms of what kind of map is being used (and it corresponding strengths and weaknesses in representing the data) AND in terms of what kind of claims we can and cannot make from the data in the map. There is no single map that will tell us everything, and unfortunately there is also no map that will predict the future for us. Both we can help our students make sense of the data and strengthen their data literacy skills as we help them engage with the maps and data.
I hope you all are able to stay safe, healthy, and sane.
Check out the blog post “Making sense of Covid-19 Graphs” if you are interested in a similar discussion about different graphs (bar charts, histograms, line graphs, and scatterplots) being shared.
