I used Rawgraphs to visualize the data of most popular baby names throughout the years in New Zealand. I started off with the line chart because I thought that it would be useful to see the change of popularity over time. The problem I was having is that I cannot figure out how to lower the amount of names that show on the graphs. This first one is too cluttered with information, but the lines are somewhat easier to follow than with other graphs:
I tweaked the width so that the lines were not so steep but to no significant effect. I then tried the bump chart because I figured I could do the same thing but with thicker lines. The lines also would help instantly visualize the rankings:
I shortened the height and increased the width so that the thickness is a little more visible. The grey lines become too much to follow, but I can see the potential in using the colors to make an argument. Before Lin’s lecture, I was thinking of graphs as something that either prove a fact or are false; however, in thinking about visualizations as an argument, I could see how someone could use this to make people focus on names that they would like others to see. For example, if someone would like to make it seem like Emily is making a comeback in popularity, they could cut the width of the above graph slightly shorter to make the trend look concave up, as if it is not plateauing in 2010. Further, someone could, if they know how, exclude names they do not like from the list to make them seem less relative than they actually are. Someone could also color only certain names so that other names blend together. I cannot tell where James is headed, but I can tell you that Benjamin is on the decline by 2008.