Gapminder World Health Chart Activity


  Date: 2022
  Author: Gapminder
Source: https://www.gapminder.org/fw/world-health-chart/
  License: CC BY
  Domain: Understanding Data
  Subdomain: Mapping Data
  Type of Resource: Activity

The World Health Chart on Gapminder's website is an interactive visualization tool that displays the relationship between income and life expectancy for countries worldwide. It presents this data as a graph where the x-axis represents income (GDP per capita) and the y-axis represents life expectancy. Each country is represented by a bubble, with the size of the bubble indicating population size. The chart allows users to view data from 1800 to 2021, showing how countries have progressed over time in terms of health and wealth. Users can play an animation to see the changes occur dynamically or search for specific countries. The page includes a video explanation by Hans Rosling, demonstrating the correlation between income and health across nations. It also offers downloadable resources such as printable PDFs, presentation files, and a fullscreen version of the chart.

Keywords:  Health data    Global income    Data visualization    “Data Analysis”    Gapminder  

MLA Citation: Free material from www.gapminder.org


Instructor’s Note

For this activity, have students visit the Gapminder World Health Chart website, and review the material presented on that page. The site presents an animated chart that depicts data about a nation’s life expectancy and income over time. Two accompanying videos provide interpretations of the data. Either before class or as part of the exercise, have students watch the animated chart and the YouTube video, “Hans Rosling’s 200 Countries, 200 Years, 4 Minutes.” Divide students into small groups and have them consider the following questions, which can then be discussed by the class as a whole.

  • How are income and life expectancy correlated? How does the correlation change over time?
  • What other kinds of correlation does the chart suggest? (For instance, global regions and income and/or life expectancy?) Does the visualization suggest a correlation between population and income? Or life expectancy?
  • Does the chart allow us to make any conclusions about what causes what? Can we assume that income impacts life expectancy? Or vice versa? What other factors excluded from the visualization might influence the correlation?
  • What kinds of assumptions inform the presentation of data? How might forms of power or privilege play into these assumptions? Might there be other ways of measuring global health? Does the dataset presented say anything about quality of life? Focusing on per capita income (as Rosling notes in passing) doesn’t address distribution of income within a given country. Why would income distribution make a difference to quality of life or life expectancy?