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One of the first uses of a heat map as a form of data visualization is also one of history’s most impactful. In the mid-1800s, there was a worldwide cholera pandemic. Scientists were desperate to determine the cause to put a stop to the pandemic, and one of those scientists, John Snow, studied a particular London neighborhood that was suffering from a large number of cholera cases in 1854. Snow created a map of the outbreak that included small bar charts on the streets indicating the number of people affected by the disease across different locations in the neighborhood. He suspected that the outbreak was linked to water, so he also drew small crosses on the map to indicate water sources. Through this visualization, Snow was able to identify that the people who were dying nearly all had one thing in common—they were drinking out of the same water source. This led to the discovery of cholera being conveyed through contaminated water. Exhibit 4-1A shows Snow’s 1854 cholera map. Software and methods for creating heat maps to visualize epidemics have improved since 1854, but the purpose still exists. Using heat maps to visualize clusters of people impacted by epidemics helps researchers, health professionals, and policymakers identify patterns and ultimately inform decisions about how to resolve epidemics. Without Snow’s hypotheses, methods for testing it, and ultimately communicating the results through data visualization, the 1854 cholera outbreak would have continued with scientists still being uncertain of the cause.
Unfortunately, poor data visualizations are used. Google images for “Worst Visualizations” or “Biased Bar Charts” or similar searches. Provide a screenshot of your finding and explain why it’s a bad visualization.