Visibility and Omission

Introduction

In this assignment, I visualize the most popular baby names in New Zealand from 2001 to 2010. Specifically, the emphasis in my visualization is to highlight the chronological change in each name’s rank and count over the years. To show both metrics clearly, I chose to use the bar chart race template from Flourish to visualize this dataset, which displays information related to both rank and count of each name in each year. I have also used color-coded bars for each gender to reveal the diversity of naming patterns for each gender. The dynamic bar chart representation, in my view, makes the information more engaging and intuitive to the audience.

Modifications

I made several adjustments to the dataset to make it compatible with the Flourish visualization template. The original data was in a long format, where each row represented a name–year pair. Since the Flourish bar chart race requires wide-format data, I transformed it so that each name occupies a single row with separate columns for each year’s count. I also added color-coded bars to distinguish genders, helping viewers identify patterns across different groups. These modifications were essential for achieving my visualization goal: to highlight the chronological change in each name’s rank and count over time. 

Reflections

My modification process reflects the idea from this week’s reading that effective visualization can turn individual insights into clear communication for an audience. While color-coding the data by gender, I also became aware of the bias embedded in the dataset itself. The binary gender encoding entirely excludes nonbinary and gender-minority groups in New Zealand. This omission not only erases their presence from the data but also risks reinforcing their marginalization in future research. My experience, therefore, highlights a broader limitation of quantitative research, which often depends on pre-existing datasets that carry implicit biases. In contrast, digital humanities emphasizes the importance of creating and designing datasets intentionally. I believe that more thoughtful conversations about data design will lead to more inclusive and meaningful research in the field.