For my data visualization, I decided to use Flourish to create a grid of line charts. I thought this would be an appropriate way to visualize this data set, as this allows us to see how the popularity of names changed over the 10-year period. Using a grid makes the data visualization much more organized, as you can clearly distinguish how each individual name changed over time. Each specific line chart uses the year as the X-axis variable and the count as the Y-axis variable, so the data points represent the count of a name for that specific year. Additionally, I also used the gender column of the dataset as a row filter for the data visualization. This allows the user to swap between male and female names to be graphed. This improves the clarity of the data visualization, as the user won’t be overwhelmed with a large number of graphs, and can easily compare name popularity among the same gender.
I decided to change the color palette to improve the clarity of the data visualization. I thought using a dark color would make the graph easier to interpret for the reader. As this is a grid of line charts instead of a singular chart, using distinguishing line colors is unnecessary, as there is only one color on each graph. If I used a singular line chart, I would need to pick specific colors so the user could tell the difference between each name on the chart.
One big takeaway I had from Lin’s lecture and the readings was that graphs are a way of telling a story. I think it’s important to choose a data visualization that can best articulate to the readers what that story is. For this dataset, I felt that a line chart was effective as you can clearly see how specific names gained and lost popularity in different years. For example, we can see in my data visualization that the name Ella spiked in popularity in the year 2007. This could lead the reader to research cultural shifts around the name Ella in the year 2007, to discover why this spike may have occurred. This connects to the digital humanities, as culture is a part of what humanities scientists study. Having specific data visualizations like this can allow members of the digital humanities to identify outliers and trends in graphs, which can lead to further study and analysis.
