Data visualization plays a crucial role in DH projects by enabling people to uncover patterns, trends, and insights within large datasets that might not be immediately apparent through traditional analysis methods.
In this lab, I use Rawgraphs.io to process a dataset of the most popular (top 10) baby names in New Zealand from 2001-2010 and visualize them in a line chart. I chose the x-axis to be the year and the y-axis to be the count, with each line representing one name. In this way, I can show changes in the popularity of specific names. In order to improve the clarity of the visualization, I only selected some of the data. If all of them were selected, there would be too much data and it would be too cluttered to be recognizable. Therefore, I removed all the girls’ names and selected only the top 5 for each year. Also, different colors are used to distinguish different names.
This type of data visualization allows for a very clear and straightforward view of the trends reflected in the data. For example, Jack and Joshua have a clear downward trend. Meanwhile, Oliver and Liam are up-and-coming names that may become more dominant over the next few years. This gives us a new perspective, allowing us to observe changes in naming to dynamically reflect social and cultural development.
