I chose to seperately visualize male and female baby names, as I thought it would “declutter” the visual representation. I think my chosen graph style is a reflection of trends, as it is easy to visualize what names became more/less popular by looking at whether or not the curve is trending positively or negatively. However, I would have ideally been able to further “declutter” this visual representation, so while I think the ideas get across, I do think it could be more straightforward.
The first thing I did to change the clarity of the image is to change the color of the male and female baby name trends, as it made it easily distinguishable. Then, I seperated the two genders into two different graphs so there weren’t a ton of lines on just one graph. Furthermore, I verified that both graphs used the same scales, thus making it easier to find similar/different trends between the genders. However, I wish I could have made it more clear, I think that an animated visualization could have been easier to comprehend. As this is one of my first times experimenting with data visualization, I really could not fully figure out the animated options in a way I was happy with. I further wish that I could make the names more clear, especially the ones that have very similar trends.
I definitely took some information from Lin’s lecture into account when creating this representation, especially trying to create an element of visual differences by using color. I had a hard time thinking of other ways to create visual difference, other than seperating the genders visually, both by creating two graphs and adding colors to distinguish the two categories. Another thing I thought about was how to display the quantitative information, and I decided it was important to display both the count and the years clearly. Overall, although I thought heavily about the information from the lecture, due to my lack of familiarity with these tools, I had some difficulty accomplishing my goals.
