For my visualization, I chose to create a matrix plot in rawgraphs.io. The X-axis is divided by year, while the Y-axis is divided by name. The color of the squares indicates the name’s rank in the corresponding year, and the size of the squares represents the total count of uses of a name. The darker the color, the higher ranked the name was, and the bigger the square, the higher the count of the name.
Because I used a matrix plot, one can notice both individual trends and overall trends. For instance, one can look at the progression of a specific name over the years, like how the name Sophie starts off with a lower rank but steadily increases in count. Besides that, one could also consider different trends between naming boys and naming girls. For example, one may notice that the names Joshua and Jack were more consistently popular than any of the names for girls, as indicated by their squares’ darker color and bigger size. This may indicate that there is more variety when it comes to baby girl names as opposed to baby boy names.
Before I modified anything, rawgraphs.io made it so that the lightest color was associated with the highest rank, and the darkest color was associated with the lowest rank. I found this incredibly unintuitive, since the darkest squares stood out more than the lightest ones, so I switched the shades. Additionally, I changed the color of the graph. I chose to use purple for all names instead of separating the graphs and using gendered colors. I considered the article “What Gets Counted Counts” by Catherine D’Ignazio and Lauren Klein and the section “Rethinking Binaries in Data Visualization.” As the article points out, “pink and blue, after all, is another hierarchy.” Obviously, the very nature of the data is binary, but when considering this quote from the article, I found there was no real need to distinguish between boy names and girl names by using different colors when the contrast comes from the differing shades and sizes.
Overall, my graph relates to the Digital Humanities because I used digital technologies to represent a humanistic trend. This graph could be used to explore how people shift in beliefs and opinions over time, as well as consider the subtle or possibly valuable differences in the way people name girls and boys.
3 thoughts on “Data Visualization: Lab Assignment (Week 3)”
Leave a Reply
This site uses Akismet to reduce spam. Learn how your comment data is processed.
This is a really cool graph, it feels like an organized heat map of the names but also kinda feels like a lot of information being thrown at me at once. I don’t really know how this graph works but I feel like the information would be easier to digest if you divided the data based on sex so that the columns aren’t super long. Besides all of that I think this an awesome way to represent this data.
Hi Trixie! I really like that you chose a matrix plot for your visualization as I find it really easy to understand. The way you organized it with the X-axis for years and the Y-axis for names makes it simple to spot trends. I like the modification you made of changing the lightest color to the lowest rank and darker color to the highest rank. I agree that switching the shades makes more intuitive sense. If you had kept the original default, I likely would have interpreted the graph wrong.
Hey Trixie! I love your distinction of how little changes like color can reinforce social binaries. I also admire how you were able to implement so many variables into your visualization. This was a great way to show how names fell in and out of popularity, and the way you were able to sort by name and give specific data for each one was very intuitive!