Anyone who experienced the COVID-19 pandemic of 2020 will likely remember this visual.  Intended to illustrate the exponential spread of pandemics, and communicate how preventative measures can impact the speed of growth, it seems to have originated in a 2007 Centers for Disease Control and Prevention report, Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States—Early, Targeted, Layered Use of Nonpharmaceutical Interventions. A meme depicting the “Flatten the Curve” chart went viral after Dr. Drew Harris, an assistant professor at the Thomas Jefferson University College of Population Health, recreated and shared on Twitter a version of the graphic he had adapted from one depicted in the February 29 issue of The Economist. Dr. Harris simply added a line to his version of the graphic, crisply illustrating the idea that failing to take preventative measures would overwhelm and eventually break the healthcare system. [1]

Throughout the 2020 pandemic, graphics—particularly visualizations of data related to the spread and infection rate of COVID-19—were everywhere. As Haleigh Moore of the University of Maryland College of Information Studies wrote:

With new updates developing by the hour amidst the evolving COVID-19 pandemic, trying to grapple at the most relevant information can be overwhelming. Data visualization has helped to synthesize this complex phenomena and shape the timeline of the Coronavirus pandemic that has drastically changed how we go about our daily lives. While commonly used to communicate data to the general population, visualization is now having quite a real-world impact in the face of this crisis. [2]

As Moore suggests, COVID-19 made clear what experts in data visualization have known all along—data visualizations are a potentially impactful way to communicate important, complex information in an efficient and meaningful way. However, the actual impact of data visualizations depends, in large part, upon a data literate population.

Data Literacy and Citizenship
What does it mean to be data literate? Data literacy is the ability to read, analyze, interpret, evaluate, and argue with data and data visualizations. Data visualizations come in a wide array of forms, including charts, graphs, maps, and timelines, and they fulfill a variety of functions. They reveal trends, patterns, and change over time. They help us compare, contrast, recognize proportions, and see relationships.  They show us movement, proximity, distributions, and diffusion. They help us see realities that we cannot see with the naked eye. [3]  

Data literacy is an essential competency for all citizens. Several scholars have argued that sources of information are becoming increasingly multimodal, employing a variety of verbal and visual forms, or modes, of communication, including data visualizations, to convey information. [4] Data visualizations are used to persuade people how to vote, support policies, adopt arguments or agendas, and buy products. They communicate information related to our finances, our job or educational performance, and our health. [5] Organizations like the U.S. Department of Education and the Organization for Economic Cooperation and Development have included the ability to make sense of non-continuous texts like data visualizations in their definition of the basic literacy skills required to “function in society, to achieve one’s goals, and to develop one’s knowledge and potential” [6]. In our data-saturated world, an informed citizen who can make reasoned decisions about matters affecting both their individual interests and the common good must be a data-literate citizen. 

Yet, studies suggest that most people are not data literate. A 2018 global survey of respondents from Europe, Asia, and the U.S. revealed that only 21% of 16- to 24-year-olds are data literate. A 2016 U.S. survey found that only 33% of Americans say that they understand how to read and interpret data. [7] This same survey found, though, that 88% of Americans report that claims are more persuasive if they are supported by data. In other words, people will believe claims supported by data, even if they don't know how to read the data. [8] This lack of data literacy among the public, the authors argued, puts a disproportionate amount of power in the hands of “data influencers,” the approximately six percent of people who understand data and can use it to influence the public’s decision-making processes. Their argument is particularly concerning if one considers the multiple ways in which data surrounding public policy issues can mislead those who are not data literate. [9]

Data Visualizations Can Lie
Indeed, while data visualizations can be an efficient way of spreading helpful and important information, they can also be used to spread misinformation. In his book, How Charts Lie: Getting Smarter About Visual Information, Alberto Cairo uses the election map displayed on Jack Posobiec’s book, Citizens for Trump, to illustrate the potential of data visualizations to mislead if they are not viewed with a critical and skeptical eye. As Cairo explains, the map does not show citizens who voted for each candidate. Rather, it shows counties where each candidate won.  And regardless, populations do not elect U.S. presidents, electoral votes do. [10]  So a more accurate map to explain Donald Trump’s victory in the 2016 presidential election would show state sizes adjusted for their electoral vote contribution, something like the one below, which was created as part of a series of election maps by University of Michigan professor, Mark Newman.

The fact that data visualizations can mislead—or sometimes flatly lie—is the reason that teaching data literacy should be a priority in our schools.  Data visualizations are not going away. As changing technologies make them easier to produce, they are likely to increase in prevalence across sources. And as the next section will argue, teachers of social studies may be the best people to make sure that all students become data literate for their roles as citizens.