Directions for teachers:

trees creating shade in New York City
Increasing tree canopy cover by even five percent could reduce heat-related health problems, particularly if the trees cast shade over pavement as they do on this street in New York City.Alexander Spatari/Getty Images

This lesson focuses on the five data visualizations that appear in the primary source for the Science News article “Heat waves cause more illness and death in U.S. cities with fewer trees.” Ask students to read the Science News article before directing them to the primary source. Split the class into four groups and assign one group to Figure 1, one group to Figure 2, one group to Figures 3 and 4, and one group to Figure 5. The groups will answer the first set of questions about their assigned data visualizations before presenting what they learned to the class. The second set of questions will be answered during a full-class discussion.

Directions for students:

Read the Science News article “Heat waves cause more illness and death in U.S. cities with fewer trees.” Analyze the figure(s) assigned to you by your teacher and answer the first set of questions with your group before presenting what you learned to the class. Answer the second set of questions as directed by your teacher.

Digging into data visualizations
1. What information is being displayed by your figure(s)? Describe the type of data visualization that is being used; identify the variables, parameters or data depicted by the figure; and identify and describe any axes if applicable. Highlight any special or important features of your figure.

Student answers will vary. As an example, Figure 1 illustrates the extent to which trees reduce mortality in different populations with current and possible future tree cover. It is a bar graph, and the variables are reduction in annual mortality, the annual value of avoided mortality, race of people in the municipalities, and the two scenarios for tree cover. The figure has two y-axes. The left y-axis displays reduction in annual mortality and the right y-axis displays the annual value of avoided mortality in billion USD. These axes have different scales and units.

2. Pick out two data points from your figure(s) and state them along with their units. How do the data points help you understand your figure(s)?

Student answers will vary. As an example, Figure 1 shows that approximately 632 non-Hispanic white lives are saved per year by the current tree cover compared with approximately 442 lives saved per year for people of color. Looking at two data points allows me to make a direct comparison between two sets of data.

3. Where do the data in the figure(s) come from? Does the source look credible? How can you tell?

Student answers will vary. For example, the data in Figure 1 come from 2020 U.S. census data, tree cover maps and studies that relate changes in temperature to number of deaths over time. The authors then calculated the impact that planting more trees could have on the mortality rate. The source looks credible, as the authors included a table with their data and described how they completed their calculations in their “Methods” section. The source was also published in a reputable journal, which can indicate that it is a reliable, peer-reviewed source.

4. State the general trend or takeaway displayed by the figure(s).

Student answers will vary. As an example, students may say that the general takeaway from Figure 1 is that increased tree coverage would lead to fewer deaths and decreased loss of revenue. Another major takeaway is that there is more tree coverage for non-Hispanic white people than for people of color, contributing to a greater reduction in mortality for the non-Hispanic white group.

5. In your opinion, did the figure present data in a clear, effective way? Was there anything that could have been potentially misleading or difficult to understand that could cause someone to misinterpret the data? Would there be a better way to represent the data? Why or why not?

Student answers will vary.

6. Describe how what you learned from the data in your figure does or does not support the scientists’ conclusion that you read about in the Science News article.

Student answers will vary.

Diving into data literacy
1. Write a definition for the term “data literacy.”

The ability to understand and draw conclusions from a set of data or a data visualization.

2. Why is it important for data visualizations to be clear and accurate?

Graphs and other data visualizations should be clear and accurate so that individuals are able to draw accurate conclusions about the data that can accurately inform their opinions. A lot of data is used to inform public opinions, policies, government action, and the direction of future scientific studies, so it is important that the visualizations are correct and not confusing or misleading.

3. How can data visualizations intentionally be used to promote misinformation? Can a data visualization contain accurate information but still promote misinformation? Explain and give an example. Think about choices such as relative sizing of different elements, axis scaling and colors.

Student answers will vary. For example, data visualizations can be poorly made or can be made to intentionally mislead individuals who struggle with data literacy or don’t have time to carefully examine figures. One way to make data visualizations intentionally misleading is to present two graphs side by side with different units or different measurements on each axis. If individuals look at the picture alone and do not pay attention to the axes, they may draw incorrect conclusions about the data being presented.

4. What are some methods that can be used to identify whether a misleading data visualization is poorly designed or intentionally promoting misinformation?

Student answers will vary. For example, one way to identify that a data visualization is unclear or promoting misinformation is to check whether the data come from a reliable original source, such as a peer-reviewed research paper or a report from a reputable organization. If the data come from a reliable source and match the data stated in the conclusions or statements being made, the misleading figure is most likely a result of poor design choices. If the data in the data visualization do not match the conclusions of a reputable source or if the visualization appears to be missing information, it could potentially be promoting misinformation.