Tool
I used Flourish to modify the “100 Most Popular Male and Female First Names From New Zealand” dataset. I was particularly interested in the top 10 popular ranking girl names from the years 2001-2010, so I sparsed the data into a CSV file to focus on this specific information.
To make the data more visually appealing and understandable, I created a line graph. The Y axis of the graph shows the ranking from 1-10, and the X axis provides information on the year associated with each name’s ranking.
The graph covers the years 2001 to 2010, and although not all of the years are visible for some of the names due to the differences in ranking throughout the years, we can still see the overall trends in popularity. I added a drop-down menu at the top left of the graph, which enables us to filter the data based on our desired name information.
Visualization and Style
I chose this graph because it visualizes the popularity of the names over the years in a very appealing way. For example, if we look at the name “Olivia,” we can see that it was extremely popular during the years 2001 and 2002, but its ranking changed drastically for the next couple of years before becoming one of the most famous names again from 2005 to 2010, ranking as the 2nd and 3rd for four straight years.
Changes and Clarity
Initially, I created a Stacked Area chart, but it wasn’t highlighting the 1st rank as prominently as the 10th rank due to 10 being a higher number. To address this issue, I made some adjustments and ended up using just the line curve of the area instead. The line curve provided a simple and meaningful visual representation. Even though we had the ‘hover’ option to display the rank, I decided to add the ranking numbers at the bottom of each point on the line to make it more accessible and easier to understand.
Reflection
Reflecting on Lin’s lecture and the readings, I have come to understand the significance of data visualization in the field of Digital Humanities (DH). DH involves using digital tools and technologies to analyze and interpret humanistic data which is a crucial aspect of DH as it enables researchers to communicate complex information in a clear and concise manner.
