Data Visualization – The Minard Map

Charles Minard’s map of Napoleon’s losses of troops over time is likely called one of the best visualizations ever created because of how it blends geography and numeric data in a way that is intuitive to the human brain. The map shows Napoleonic troops through two lines, a beige one entering Russia, and a black one leaving. The number of troops correlates to the thickness of each line over time. Below it lies a graph of temperatures, connected to specific moments on the map. 

In doing this, a remarkable amount of data is fit onto two different dimensions. Firstly, the geographic route of Napoleon and his troops. Next, the number of troops, showing how they dwindled with each defeat. Lastly, the climate date which shows how cold impacted the number of troops. By easily combining all these pieces of data in a way which is easily understood at a glance, Minard was able to show the evolution of the Napoleonic invasion of Russia to many people simultaneously and effectively, thus becoming one of the most valuable pieces of data visualization ever. 

We discussed the importance of visualization in data visualization, in that it is important to consider how visualization allows for conclusions from your data collection, rather than just easily showing aggregated results. This is an important idea reflected in the Minard map, where the level of losses over time is shown in a way that is more interesting and informative than a simple linear graph would be. The creativity shown in finding a way to connect numerical and geographic elements in a specific order, connected to temperature data, helps make this map one of the best examples of data visualization I’ve seen because it presents data in a unique way that no other form of visualization could match in the ability to convey a variety of forms of data.

1 thought on “Data Visualization – The Minard Map

  1. I agree that the map presents a lot of data in a cohesive way to build a detailed picture. I like how you explained the data’s connection to temperature, as that was something I didn’t fully get.

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