For my data visualization project, I uploaded the provided .csv file of the most popular baby names in New Zealand to RAWGraphs 2.0. After messing around with some of the options and settings, I settled on using a bumpchart to create the following visualization:
I wanted to compare how many male and female babies each year were named one of that year’s top ten names. I thought the bumpchart was a good choice for this visualization because it allows you to track total counts by year while also including the proportions of those names which ranked 1-10. I made one bumpchart for female baby names and one for male baby names to be able to compare across the sexes.
I made a few changes from the automatic visualization provided when specifying these variables. Most involved making them appear on the same y-axis scale to facilitate comparison and adding additional white space padding. The significant change I made, though, was to alter the color of the “streams” which represent each rank. Originally, they were blue, and 1 was the lightest shade while 10 was the darkest shade. I switched the gradient to red for purely aesthetic reasons. The shades, though, I switched to increase viewer comprehension. Having 10 be the darkest shade gave it the strongest impression in the chart’s visual hierarchy, which didn’t make sense since it was lowest rank. So, I made 1 the darkest shade of red and 10 the lightest shade of red to give it an ordered visual hierarchy that better represented the data being visualized.
One can learn a few interesting things from looking at this chart. While the popularity of naming female babies one of the 10 most popular names has remained relatively even, less and less male babies in New Zealand have been named the most popular names of their respective years. Despite this, though, more male than female babies are named popular names. This leads one to assume that there is more variation in female names in New Zealand than in male names.
Despite the interesting things these visualizations show, I can’t help but thinking about Chapter 4 of Catherine D’Ignazio and Lauren Klein’s Data Feminism, entitled “What Gets Counted Counts.” This data set reinforces the male/female gender binary by only including those two labels and directly linking names as belonging to one label or the other. If Digital Humanities as a field should be working to dismantle such binaries and patriarchal hierarchies, can these data sets be used in good conscience? On the other hand, if so much data has been collected which subconsciously reinforces existing power structures, is there any way to use that data without perpetuating said structures? It would seem foolish to throw so much information away.
Great job on the graph! I also made two graphs, one for the male baby names and one for the female baby names. A great change in the color shades. I like how you mentioned exactly what I would’ve thought if it had been kept the way it originally was. In my opinion, a darker shade denotes greater importance or value, which would not have made sense for the 10th rank.