For the lab this week I visualized the data to show the trends in the top female baby names in New Zealand from 2001 to 2010. I chose to use the line chart as it emphasizes change over time and allows for multiple names to be displayed simultaneously.
Some changes I made to improve clarity are:
- Reduced the chart so it only shows names of female babies as including all names would have been overwhelming
- Changed the labels’ positions from inline to side
Lin emphasized the importance of reducing cognitive load in data visualization. I think this aligns with the changes I have made to simplify the chart (although it still looks a bit messy) by reducing the number of names displayed and using end labels. This visualization is an example of how DH principles can turn raw data into meaningful information. By tracing name trends, we can gain insights into societal shifts over time. For instance, the popularity of certain names may reflect trends in naming conventions.
1 thought on “Lab 3”
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Hi Esther! I really like how your line chart turned out. It is very clear to understand and does a nice job showing the difference between years and popularity. I agree that separating the female baby names from the male baby names reduces clutter. I also did something similar in my data visualization. Good work!