I used Flourish to visualize a dataset of New Zealand’s top 10 girls’ names between 2001 and 2010, focusing on a single gender to create a cleaner visualization with less visual noise. The dataset exhibited notable fluctuation over time, so I chose an animated line chart to clearly illustrate how the popularity of individual names evolved across the years. I cleaned the data by creating a pivot table of yearly counts. Then, I refined the visualization in Flourish by adjusting line widths and colors to enhance legibility and reduce visual fatigue from overlapping lines. This process demonstrated how visual representation can transform cultural data into an analytical and communicative tool. By turning the historical baby name dataset into a dynamic chart, I was able to think about the ways to trace patterns, compare temporal fluctuations, and make insights more accessible.
This experience also aligned with the idea of how the structure of a visualization shapes what can be understood from the data. As Klein et al. note in A Counterhistory of Data Visualization, Digital Humanities students should approach interpretation with awareness that every visualization imposes a lens. While a timeline or line chart emphasizes linear narrative and sequence, other visual forms—such as a shuffled arrangement of data points—can encourage exploration and reveal unexpected patterns. Reflecting on my experience, I realized how visualization choices guide attention and shape the stories we tell from data.
2 thoughts on “Data Visualization through Flourish”
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Hi Luha, I think you made a really nice visualization! I used line race chart too, and I think the way you kept both score and rank as variables (I only kept rank) helps people to get more information from one visualization very efficiently. I did a similar thing to separate data for female names and male names since I found the graph would be too messy to read if all the names appears at once. Great job!
I think your visualization effectively captures a key characteristic of the dataset: the substantial fluctuation in names over time. My bar race chart expresses this indirectly, but the line race chart makes the trajectories more salient than the bars. I also agree that every visualization acts as a lens on the underlying data. Researchers should remain mindful of the biases this lens can introduce when interpreting results.