Exploring Data Visualization through Baby Name Trends

For this week’s lab assignment, I chose to visualize the data set using Flourish. From there, I chose a beeswarm plot to show the changes over time all at once. While a bar chart race would represent this change over time in an engaging way, I found it hard to use it to determine any trends with so much information flying by. Rather, a beeswarm plot allows you to see all of these points at once without it being too overwhelming as it visualizes the count of each name by the size of each node. If you want to look for more information on a certain name in a certain year, you can hover over that node to look at exactly what the count associated with it is as well as the ranking. There are options to add labels to each node to represent the ranking of each node, but I found that this was too visually overwhelming and didn’t add much to the interpretation of the data.

To make this visualization more clear than the default styling (shown below) provided, I first changed the two colors used to distinguish female and male names as they were originally both shade of blue while didn’t provide much contrast between the two types of names. I also adjusted the theme so the background would be dark and the white text would pop a bit more than the initial grey and white. I also increased the maximum size of each point so that there was more distinction between the popularity of each name without needing to scroll over each point. Finally, I added a title to allow the visualization to stand on its own without needing additional context to understand it.

Reflection

When making this visualization, I considered why these names needed to be categorized by gender and if that actually added anything to the significance of the data. This consideration made me think of the “What Gets Counted Counts” reading and how we should consider the data itself more than just creating a result. While that was the assignment, if we were considering a longer project, data sets can’t just be put into the software without any cleaning or thought beforehand. This idea was mentioned in Lin’s slides, and opens up further consideration for modifying data sets or omitting certain categories in order to focus on visualizing a certain part or trend from the larger data set. Overall, this exercise helped me think more about what needs to considered about a data set so that it can be visualized effectively and engagingly.