I chose to visualize the relative number of babies given each name every year. I chose a heatmap because it assigns each cell a different color based on the relative value of the count, which shows more intuitively how the relative numbers of different names compare within each year and how the popularity of the same name changes over the ten years.
First, I excluded ranking because it is not the focus of what I want to show, and adding it would make the whole graph more cluttered. Second, I set gender as a filter, which separates the names by gender and displays them in two charts. If everything were placed in a single chart, all the names would become messy, and it would be impossible to show which names were popular in which years within each gender. Separating them gives viewers a clearer and more intuitive feel for the data.
Lin’s lecture discussed being selective in designing visualizations of data. When working with data, “exploration” is the author’s homework, while “explanation” is the work that is actually presented to the reader. We cannot hand raw data to readers and leave them to find the patterns on their own; instead, we need to use design to directly present the insights we have distilled.
The same dataset can give rise to entirely different visualizations in different contexts. As the historical cases in L. Klein et al. discussed in “Introduction: A History of Visualization and Power1,” the designer’s purpose determines what a chart looks like. If we want to highlight one central point, we inevitably have to use contrast and simplicity to downplay secondary information, and even filter out noise that might distract the reader. But this also reminds us that data visualization is inherently biased. When we “streamline” data to tell a clear story, we are also making subjective judgments. Therefore, researchers in DH must also stay vigilant to things to be highlighted, and things that are unintentionally hidden.
- âIntroduction: A History of Visualization and Power,â in L. Klein et al., Data by Design: An Interactive History of Data Visualization, 1789-1900. 2026. âŠī¸