Going into this lab I knew I wanted to find a way to show the change in New Zealand’s top 10 baby names over the years 2001 to 2010. I messed around with a few visualizations but settled on a line chart to try to keep it simple to view.
I settled on this because even at a glance one can see the overall change in popularity for both male and female baby names. With the year on x-axis and count on the y-axis, it provides an easy way to view the shift in trends with the higher peaks representing more popularity in the name.
This chart still appears slightly messy, but I could not figure out how to adjust what I wanted to. Before I get into what I could not change, I will talk about the changes I made. The first thing I did was adjust the coloring since initially only 10 lines were colored and the rest were gray. The other color scheme I tried was more pale, and it was still a bit hard to distinguish the lines. Each line now being a color made it easier to identify patterns. I then decided to split the chart based on gender so viewers could focus on either male or female names. I also adjusted margins to make the axis more visible and smoothed out the lines since time is continuous.
There were two main things that I really wanted to change but just couldn’t figure out. The first is some of the names are cluttered together making it hard to identify which line belongs to which name. I’m not sure how I would rather them be, because all the names at the end of the line(on the right) also made the graph cluttered. The second thing I wanted to adjust is the x-axis. While the years are displayed on this axis they are in the format of 2,001 instead of 2001. I was unable to figure out how to remove the “,”.
Keeping in mind the readings and class discussions, I know there are many ways to display and view data, depending on the story that is being told. Since data can be manipulated to look a certain way, I wanted to keep mine as straightforward and simplified as possible. I remember viewing an example from class with way too much information I was unable to decipher the graph; I tried avoiding that when building mine. It is important to keep in mind information about the data. For instance, the dataset used for this lab only includes baby names from New Zealand. With this in mind, there could have been broader societal reasoning for the shift in popularity of certain names, or other factors influencing this data that we could be blissfully unaware of.
For this lab, I was not given any background information on the data, so by sticking with simplicity, I was able to keep information bias away from the trends I was attempting to show.
2 thoughts on “Lab 3”
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I love how you approached this visualization with simplicity in mind! The choice of a line chart really does make it easy to see the trends at a glance, and splitting the data by gender is a great touch—it helps to reduce some of the clutter while allowing viewers to focus on the patterns they’re most interested in.
Your adjustments to the coloring and smoothing out the lines were spot on. It’s amazing how a little color can make such a big difference in identifying trends. I also really appreciate how you connected your design choices to the readings and class discussions about avoiding overwhelming visualizations.
I appreciate your intention in approaching this graph, and I think your goal of simplicity was definitely achieved. Your graph is not overwhelming to look at, but still is able to communicate a good amount of information. I liked the addition of the color code on the side as well, though in all honesty some of the distinctions between the colors are a bit hard for me to see. It seems as though you did a good job avoiding bias or manipulation of the data, beyond the basic fact that you were translating the data into another medium, leaving the interpretation open to other scholars.