The method I chose to visualize the data set of the top baby names was a hierarchy tree map. This graph style is appropriate because it shows not just which names were popular, but how popular each name was compared to the other names which were popular in different years. The size of the name shows the relationships between both the years and the names. It also splits up the genders to allow comparisons between the genders as well as name popularity within the genders.
I made a couple of changes to the visualization to improve clarity. First, I changed the “size by” from years to count. This made the visualization more clear by creating accurate proportions to how popular the names were as compared to just the year determining the size. However, I still wanted the year to be utilized so the second change I made to the visualization was adding a filter by feature where views can select a particular year to see individually. This allows the user to see what names are from a specific year and compare the years that way.
The process of making this graph made me think a lot about Lin’s lecture. She talked about how every single decision in the design process has an effect on how your visualization is perceived. There truly is no such thing as a small decision because no decision that you make will be neutral. When it comes to color, choosing the wrong one can lead to an incorrect association or even the formation of a bias that you never intended. A simple design mistake can cause the point of your project to be lost. I took all of this into account, and it really slowed down my decision-making process and made me double-check everything before I moved on. When it comes to digital humanities as a whole, these visualizations play a key role in identifying patterns, forming opinions, and communicating key aspects of different data sets. Overall, when it comes to the design of visualizations, it is crucial to be careful and nuanced with your decisions in order to present the data in the best way possible.