Lab 6- Data Visualization

I used Flourish to make a graph with the Top 10 baby names for both male and female babies from 2001-2010 in New Zealand. Originally, I had used a line chart to show the baby names change in popularity over time. However, once I discovered I could make an interactive filter with the year, I decided to make a bar char instead to allow for more clarity. The interactive filter really helps to decrease the amount of data that’s visible at one time without changing the amount of information the viewer gets overall! Given the fact that this is qualitative data with a count attached to it, I think a bar chart is the most appropriate.

I had to add the filter for year from the default setting, and I also changed the color because the original coloring was just too bright and difficult to see. I also chose to leave the “rank” out of the graph because since the bars appear in order of count anyway, the rank is obvious without having to actually show a separate label or data point.

I thought of Catherine D’Ignazio’s and Lauren Klein’s article “What Gets Counted Counts” a lot while making this graph. Obviously, the role of a gender binary is very clear in this data since the original data was organized by “male” and “female” baby names. I’m personally very supportive of the idea of de-gendering names because I think part of inclusivity means removing any assumptions that a particular name is “feminine” or “masculine.”

However, as D’Ignazio and Klein point out, you can’t just ignore historic classification systems and pretend they’re meaningless. This is literally true of this data set because it’s impossible to completely remove the gendered aspect of the data without making it misleading. For each year, the data included the top 10 male names for babies and then the top ten female names, but when you mix them all together, it just appears like the 20 most popular names for all babies. The 11th most popular male baby of 2003 could be more popular than even the most popular female baby name. Still for the viewer, that name wouldn’t even make the list at all.

I wanted to find a way to make a separate filter for “male” and “female,” along with “year,” but the site would only allow me to use one filter. I would have to make two separate charts in order for gender to be totally clear. I tried to make it clear within the title: “Top 10 Male and Female Baby Names In New Zealand By Year” that the graph shows only the 10 names for each. While I tried my best, I still fear that I fell to some of the issues with ambiguous labels that we discussed in class.