For this week’s visualization, I used a bump chart from RAWGraphs to show how the most popular baby names in the United States changed from 2001 to 2010. I was less interested in the raw number of babies with each name than in how names moved up and down in the rankings over time. A bump chart felt like a good fit because it lets the viewer follow one name across multiple years and quickly see which names stayed popular, which declined, and which entered the top ten.
One thing that stood out to me was how stable some names were compared with others. Jacob stayed at the top of the male rankings throughout the period, while the female rankings shifted more noticeably. Emily began the decade as the number one female name, but by 2010 Isabella and Sophia had moved ahead of it. Looking at this as lines instead of a table made those changes much easier to notice.
I also changed some of the default presentation choices to make the chart easier to read. I separated male and female names instead of putting all of the lines on one chart, kept rank 1 at the top of the scale, and used labels so the viewer does not have to constantly match lines to a legend. These are small changes, but they reduce clutter and make the main pattern easier to understand.
In addition to that, this project also relates to what we discussed regarding data visualization in class. One of the ideas mentioned in Data Feminism is that a visualization can never be a neutral representation of data. Depending on the design, certain aspects may come out to be more noticeable and depending on the categories used in a dataset, one will be able to see something that will not be possible otherwise. For instance, according to the concept of Data Feminism, a classification system may make certain identities apparent and hide others. One limitation I have found in the dataset is that there is a binary division between the categories of names as the only two categories are male and female.