Data visualization

CarletonStudent
Tue, 11 Oct 2022 04:23:15 +0000
Week 4, Chart, Clear, Concise, correlations, data visualization, Dataset, Digital Humanities, Exploration, Graph, Map, Minard, proportional

Minard’s map:

Minard’s map tells the story of Napoleon’s Russian campaign of 1812 and is one of the best statistical graphs I have ever laid my eyes on, living up to all its hype. Before reading it I didn’t know about the war. Now, I have a good idea of how it unfolded. I know the routes the soldiers took, how many managed to get to Moscow, and the casualties along the way. I can also see the deadly path back to France and the toll the low temperature took on the soldiers. As Minard managed to pack all this complex information in a very clear and concise way I believe his map is outstanding.

His success was dependent on many visual cues. The lines, for example, are proportional to the number of living soldiers at any given time. However, to make it more clear, the author also added figures stating the exact numbers of living troops. The map also uses contrast to convey a point, the thin black line going back clearly states the anti-war position of the author. The temperature is directly connected to points in the black line, which makes it easy for the reader to infer the relationship between the cold and soldier deaths. The geographic locations were also indicated on the map, which gives a good historical overview of the campaign. All of these make the graph very intuitive, easy to understand and informative, even for people unfamiliar with the subject. Which makes the graph very successful.

Lin’s Lecture:

One of the main takeaways for me is that you first need to explore your dataset before creating a chart to display to other people. While doing research, many authors start by trying to prove a point. If they do not explore the dataset before plotting their chart all they will do is use it to prove they are correct, missing many interesting be correlations and not formulating other interesting research questions they can infer based on the dataset. Those new questions can be addressed in future papers and research. I feel this is particularly useful in digital humanities as data is qualitative rather than quantitative. Finding correlations between answers given by people to other variables like age or major might give interesting insights on the thoughts and behaviors of certain groups of people.