For this project, I visualized data on the ten most popular baby names in New Zealand from 2001 to 2010. Instead of focusing on year by year change, I was interested in comparing overall popularity across the decade, while also highlighting differences between male and female naming trends. To do this, I created a circular donut style visualization using Flourish.
The visualization is structured in two layers. The inner ring shows the total count of popular names by gender, split evenly between male and female names. The outer ring breaks each gender category into individual names, with segment size corresponding to how frequently each name appeared in the top rankings over the ten year period. This graph style works well because it communicates part to whole relationships clearly, first at the gender level and then at the individual name level, without overwhelming the viewer with a table of numbers.
I made several changes to the default styling to improve clarity. I used a consistent color scheme to distinguish gender, with one color for female names and another for male names, which makes the structure of the chart immediately legible. I also added direct labels to the name segments, including counts, so viewers do not have to rely on hover interactions or external legends to understand the data. The dark background was chosen to increase contrast and help the lighter segments stand out, improving readability.
This visualization reflects ideas from Lin’s lecture about intentional design and the importance of matching form to purpose. A default chart might list names or rank them in bars, but that approach would not emphasize the hierarchical relationship between gender and individual names. By choosing a circular, layered format, the visualization makes an argument about how we should read the data, starting from broader categories before moving to specific examples.
From a digital humanities perspective, this project demonstrates how visualization can surface cultural patterns in everyday human data. Baby names are deeply personal, yet when aggregated and visualized, they reveal shared cultural preferences and norms. DH work often operates at this intersection between human experience and computational representation, and this visualization shows how thoughtful design choices can turn a simple dataset into a tool for cultural interpretation rather than just data display.
I really like the image you were able to make! It is very easy to follow and by just looking at it without reading the explanation of it I can infer what the purpose of it is! It seems that you really understood what Lin was saying like you outlined in the blog!