Online COVID-19 Maps and the ‘Infodemic’

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Now that most of the world has become well acquainted with how COVID-19 has affected many different countries, GIS professionals and spatial analysts can evaluate the effectiveness of online maps in portraying the effect of the pandemic and how it spread. Overall, many maps, including popular online sites, have been shown to be either inaccurate or misleading, leading some to call this an ‘infodemic’ as initially stated by the Wold Health Organization (WHO).

In a recent academic commentary, researchers found that web-based maps, including many used by news outlets or visited by people interested in getting the latest data about COVID-19 in their area, often had major flaws and misrepresented the outbreak. Common flaws include incorrect and inconsistent use of scales and units of data aggregation, mixing and misuse of bubble charts and heat maps, overly crowded dot/pin maps showing COVID-19 cases as well as health facilities, poorly graduated and classified choropleth maps, overuse of choropleth maps, lack of normalization, lack of representation of uncertainty, lack of temporal data, including model projected data, incorrect use of global maps for local consumption, and generally poor map design.[1]

A snapshot of the Coronavirus COVID-19 Global Cases Near Real-time Mapping Tool by Johns Hopkins CSSE taken on April 21, 2020.
A snapshot of the Coronavirus COVID-19 Global Cases Near Real-time Mapping Tool by Johns Hopkins CSSE taken on April 21, 2020. This online map quickly became one of the most visited Covid-19 related online maps.

In general, it was clear early on in the pandemic that many maps such as the John Hopkins University and New York Times maps, even when useful in displaying some data, were often misunderstood or misused. In part, this cannot be avoided as all maps are generally biased in focusing only on some aspects of data. One common problem early in the pandemic was the over-emphasis on using circles to depict case counts in regions, which became misunderstood as circles sometimes extended across several regions and became confused for area rather than cases of infections.[2] Such data were also not scaled to population levels, making it difficult to determine how prevalent the pandemic really was in given areas that had varying population density. Poor data and misuse of aggregation on sites such as HealthMap also caused misunderstanding in showing countries such as France and Italy, which had comparable spread of the pandemic in the spring 2020, as actually being very different.[3]

Levente Juhász, the lead researcher in the academic study discussed above, also discussed a common problem of web maps as often having a lot of detail and accuracy in what they can display but underlying data, particularly as it was often poor and scaled to country or larger spatial scales, was often used to provide more detailed, local regional information. This also seemed to create a misuse of data and potential misunderstanding as to how the pandemic was spreading.[4] Even when maps may provide useful data, they often mask vital data. For instance, in one study of New York City’s positive COVID-19 cases showed there was a strong correlation to public transport use. Additionally, people who lived in a marginalized status, that is not having adequate health coverage and insurance, were also very susceptible to positive COVID-19 infection results relative to the general population. Many maps that displayed COVID-19 infections did not make it clear during the initial stages of the pandemic the role of transport hubs as well as areas where a heavy preponderance of marginalized individuals were more susceptible. Greater emphasis on potentially vulnerable population areas in maps could have enabled earlier mitigation attempts in these areas. [5]

What the COVID-19 pandemic has taught many of us is the importance of having accurate health data that are provided in an informative spatial format or maps. Maps did create a lot of confusion and misunderstanding of the pandemic, perhaps often driven by a lack of good data but also poor choices in displaying information and appearances choices for maps relative to the available data. Juhász states that better choices of data display and color use, particularly choropleth choices, could have addressed this. Developing a better process for map data choices and developing a better training and use in map creation could help to at least mitigate some of the problems, in the future, with creating maps for news and other outlets that are often tasked with informing the public.

References

[1]    For more on a recent commentary work on the use of web maps in relation to the COVID-19 pandemic, see:  Mooney, P., Juhász, L., 2020. Mapping COVID-19: How web-based maps contribute to the infodemic. Dialogues in Human Geography 10, 265–270. https://doi.org/10.1177/2043820620934926.

[2]    This type of problem of misunderstanding was seen already in March in 2020 when the public and public officials sometimes misunderstood maps according to the Washington Post: https://www.washingtonpost.com/politics/2020/03/11/be-careful-what-youre-learning-those-coronavirus-maps/.

[3]    For more on HealthMap, see:  https://www.healthmap.org/en/

[4]    For more on the interview with Juhász, see:  https://news.fiu.edu/2020/mapping-covid-19-an-interview-with-gis-researcher-levente-juhasz.

[5]    For more on the study on New York and how the infection spread in the early phases of the pandemic, see:  Cordes, J., Castro, M.C., 2020. Spatial analysis of COVID-19 clusters and contextual factors in New York City. Spatial and Spatio-temporal Epidemiology 34, 100355. https://doi.org/10.1016/j.sste.2020.100355

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