Lab Reports, Timelines
John Snow’s 1854 cholera map


I first became familiar with John Snow’s groundbreaking map used to successfully trace the origin of London’s 1854 cholera outbreak when reading Stephen Johnston’s, The Ghost Map: The Story of London’s Most Terrifying Epidemic–and How It Changed Science, Cities, and the Modern World. The map is the epitome of what good data and effective visualizations can accomplish – in this case, pinpointing the cause of a disease with a fatality rate of nearly 50 percent.

When scrolling through info we trust‘s Interacting With History timeline, I realized Snow’s map did not stand alone in the history of data visualization. Multiple graphics, spanning centuries, helped to make sense of past epidemics, including: Giovanni Maria Lancisi’s prescient map speculating the cause of malaria and Florence Nightingale’s “coxcomb” diagrams, plainly illustrating the necessity of good sanitary conditions.

As we live through this ongoing Covid-19 pandemic, past disease-related visualizations feel especially relevant. In addition to being informative and beautiful pieces of history, they offer hope. Hope, that yes we have been here before. Hope knowing that we are now building off of our previous advancements and breakthroughs. Hope that this too shall pass.

I ultimately selected seven visualizations to be displayed in a timeline, aiming to tell a succinct and compelling story about the power of data visualization.

Florence Nightingale’s 1858 “coxcomb” diagrams, visualizing that infectious disease is more deadly than warfare


Timeline JS provided a Google Spreadsheet in which to populate the necessary data. Inputting image links and basic information including dates, headlines, and brief descriptions generated a functioning, easily sharable online timeline. Although the technology was seamless, I did not find the process ideal. Timeline JS offered minimal design options and I struggled with organizing my thoughts and ideas within a spreadsheet format.


Before even working within the framework of Timeline JS, I sough out more disease-related visualizations. Google searches such as ‘disease map’ turned up a variety of informative and interesting articles. I went down many rabbit holes and accumulated too many open tabs and graphics of note. From there, the majority of my time was spent curating a handful of visualizations into a coherent and hopefully compelling story. The choices were not easy and I regret that some graphics did not make the final cut.

The first five slides came together rather quickly as those visualizations were referenced in multiple articles. Trying to find somewhat more recent examples to help bridge the gap between 1853 and present day required further searches. Ultimately, I prioritized story telling over quality of visuals. The 1903 and 1904 graphics, although well-designed and easily legible, are admittedly not groundbreaking examples of data visualization.


The history of data and disease feels more relevant now than ever. I am considering this timeline as a start to what has the potential of becoming a much larger project. The overall idea could be expanded and more generalized to be inclusive of overall public health. Ideally, searches for more examples would go beyond strictly what is available online. A frustrating limitation of many of the graphics I did include was their low resolution, inhibiting me from being able to see and then discuss their finer details.

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