Originally, based on my previous statistics knowledge, I thought bar chart is the type of graph which most clearly shows the trends and comparisons of discrete data. However, putting all data in the dataset on the most popular baby names in New Zealand form 2001-2010 made me an extremely long bar chart, and therefore, I gave up this plan and turned to explore more graph types in Flourish.
Here is what it looks like. This is a radial tree showing the most popular names for male and female babies in New Zealand from 2001 to 2010. At first, it was extremely dense as the bar chart was extremely long. The sense I had at the first glance of it was complicated and unclear. Later I decided to categorize the name data with genders of the babies and also created a filter to allow visualization of data from each year between 2001 and 2010.
These are two examples of radial tree showing data from a single year, with one showing that from 2001 and another showing that from 2009.
Since I missed Lin’s lecture on Thursday’s class, I tried to find inspiration in my deer peers’ posts. After viewing almost all the posts, I found the essence Liam Keane extracted from Lin’s lecture in his post(in bold in his last paragraph) was the one I agree most with: guiding your audience towards what you want them to see or understand. By using radial trees with filters to visualize the data, I intend to show both the popular naming trends within each year and the difference between data divided into parts and integrated into a whole.
Conveying information from academia to the public is a very important task of humanities scholars. Within the field of digital humanities, this task is fairly easy with the help of technology. Digital humanists can turn burdensome, boring, and complicated data into clear, delicate, and understandable charts with the help of visualization tools. This is one of the benefits brought to humans by this subject.
