I wanted to visualize the shifts in names (both male and female) over a nine-year period in New Zealand using a line chart. I believe a line chart was the best way to display this information because it effectively conveys how the usage of popular baby names changed over time. The peaks and valleys in the chart clearly illustrate the rise and fall of each name’s popularity and how these trends correspond to specific years.
To improve the clarity of the visualization, I adjusted the way the lines were drawn to make them straighter, ensuring that the year and the edges of the lines align. This made it easier to interpret the popularity of each name. I also separated the graphs by male and female names, which helped make the changes for each gender clearer and the overall graph easier to read.
Reflecting on Lin’s lecture, I thought a lot about her discussion of data visualization as a tool for humans to better understand the story that data is telling. I chose a line chart because it effectively tells the story of name usage over time with a quick visual glance. Data visualization, like my project, relates to digital humanities (DH) because it helps convey a message in a way that is easy to understand while also sharing knowledge with others. DH focuses on interpreting and visualizing data and sharing the findings, and data visualization is a crucial step in this process.
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Good job on your data visualization Ngelek! I like how easy it is to compare the count of specific names across the years to see how the popularity of that name changed over time. One thing that I noticed from your data visualization is that many of the male names seem to have gone down over time. I think a visualization like yours makes it easy to make these comparisons and find trends in data. Good work!