In 1854, a cholera outbreak in London puzzled the scientific community as well as the local authorities. Dr. John Snow, an epidemiologist studying the outbreak was trying to find the root causes by looking at the data available.
Dr. Snow's approach is very similar to the approach scientists use today to unlock insights from data, but in 1854, things were a bit different. Back then, researchers had very strong preconceptions about how diseases were transmitted, and the concept of germs was not yet understood. To try to understand what was happening, Dr. Snow mapped the cholera cases. Sure, today this might seem an obvious way to tackle this challenge, but back then, it was groundbreaking.
Data visualization enabled Dr. Snow to recognize that the cases were clustered around water pumps, specifically the one in Broad Street. This insight opened an amazing new branch of questions and answers for this outbreak and epidemiology in general.
As part of our demo contest, Jason Thomas created this visual tribute to John Snow taking advantage of maps, bar charts and slicers in Power View and Power BI.
We invite you to explore Dr. Snow's findings by using this interactive visualization. Play with the different radius and pumps to see the impact they had in cholera cases. Also, check out the London map to see where pumps where located back in 1854 and their impact.
You can also hear from Jason first hand on how he used Excel and Power BI to build this modern age tribute.
Let us know what you think about this visualization and find more information about Power BI at www.PowerBI.com.
"The visualization that changed the world of data" -- and here I thought you were going to mention that famous infographic of Napoleon's disastrous Russian Campaign -- www.youtube.com/watch.
I suppose that is more about graphically representing many dimensions, and not about analyzing large quantities of data to look for data relationships.
Good point Eric. I believe there are many analyses that match this title but this one is definitely on the top 3 because of the impact and the breakthrough. It always gets called out as a great example of data visual analysis.
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