For this week’s lab, data visualization, I chose to implement a line graph to present the trend of the 10 most popular baby names in New Zealand from 2001 to 2010 using the website called Rawgraph.io. This website allows me to choose what type of graph style is the most appropriate for my data set and assign variables to different axes. I think the line graph is a good choice for showing trends over time. Therefore, I decided to demonstrate the change in the popularity of the 10 baby names in ten years. In my line graph, the x-axis represents the year, and the y-axis represents the count of each year. It shows how the count of each name varies over the ten years very straightforwardly.
To improve the clarity of the visualization, I adjusted several features of the graph. I changed the height of the graph so that the variations are more obvious, and I changed the width so that the lines are not all packed as many of them overlap. Also, I changed the lines of female names to purple and the lines of male names to purple so that readers can easily interpret the gender information in this graph. Moreover, I changed the curve type from curvy to linear because the given data only collects the total counts of names each year. Since the data cannot show the change in name popularity throughout a specific year, the linear curve type is more appropriate. It indicates that the collected data are discrete rather than continuous, and readers can get the exact counts by comparing the peaks to the numbers on the y-axis.
One of my main takeaways from Lin’s lecture is that the interpretation of data sets largely depends on data visualization, and different approaches would result in completely different interpretations. Especially in the field of DH, data visualization plays an essential role in telling the stories behind data. Taking my visualization of the 10 most popular baby names in New Zealand, I chose to demonstrate the trends of the change in the popularity of baby names. there are definitely other ways to present the data and draw different conclusions, such as making comparisons and calculating proportions. No matter what stories are being told through the presentation of data, the most important part of data visualization is to convey complicated information in an accessible way.
1 thought on “Lab Week 4: DATA VISUALIZATION”
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I think that you took important steps to make this graph more readable, such as changing the colors to be dependent on gender. I do think that the graph is somewhat difficult to read because of the intersection of the lines, but you can see some general trends. Maybe splitting the charts by gender would have been helpful, but it’s good otherwise!