Static Visualization:
Visualization Explanation:
The visualization I chose to make is a bar graph that shows the count of different names in two different years, 2001 and 2005. I think a bar graph is a good choice because it clearly shows the change over the years. However, I think it might not have been the best choice because of how many different names there were in the list. Having lots of different bars in the bar graph makes it more difficult to analyze visually.
Visualization Clarity Modification:
I changed the colors of the female names to purple and male names to blue to make it easier to see the differences between the two. I think some variety in color makes the entire bar graph easier to look at too.
My Visualization’s Relationship to Readings:
I think my bar graph is an excellent example of the type of data that Catherine D’Ignazio and Lauren Klein talk about in the chapter of Data Feminism, “What Gets Counted Counts.” The names in the data are all classified as male or female names, which seems pretty unnecessary. Although I stated that the color changes I added make it easier to differentiate between male and female names, I am now wondering if there was a need to differentiate between them in the first place. Many names, if not all of them, do not fit on the gender-binary, although many people want them to. In conclusion, it seems that I fell into the trap of automatically wanting to separate males and females in a gender-binary in data, even though it was not necessary.
Sources
D’Ignazio, C., & Klein, L, Chapter 4, “What Gets Counted Counts,” In Data Feminism (2020).
