In this post, I will be discussing the data visualization I created for a top 10 popular boy and girl baby names in New Zealand from 2001 to 2010. I chose this method to visualize this data because it captures the change in name popularities over time.
For the visualization, I chose to use a line chart to represent the growth/fall of baby name popularity. This style of graph is appropriate because it clearly shows the trends and change over time. To improve the clarity of the visualization, I made several changes. First, I added a title and axis labels to clearly identify the data being displayed. I also added points at each year of data collection and made the line chart connecting the points straight rather than curved. This hopefully allows the viewer to see that the chart is not of data continuously sampled throughout the entire year. Additionally, I changed the opacity and color palette of the dots and lines to be more pleasing to the eye.
Goal is to show the stories in your data, Guided by story, deliberately crafted,many graph types
Reflecting on the information from Lin’s lecture, it is clear that data visualization plays a crucial role in the Digital Humanities. In DH, visualizations are used to represent and communicate complex information in an accessible and understandable manner. The visualization I created for trends in popular New Zealand baby names is a prime example of this. By presenting the data in a clear and concise manner, the viewer is able to quickly understand the trends and patterns in the data.
Furthermore, the importance of effective data visualization in DH lies in its ability to reveal insights and patterns that may not be immediately apparent from raw data. In this case, the visualization clearly shows the growth/changes over time. This information relates to DH as it sheds light on the changing landscape of the digital world and its impact on society.
In conclusion, the visualization I created for the growth of social media usage effectively represents the data and communicates important trends and patterns. The changes I made to improve the clarity of the visualization demonstrate the importance of effective data visualization in DH. By presenting complex information in a clear and accessible manner, visualizations like these can reveal important insights and shape our understanding of the digital world.
3 thoughts on “Lab Assignment: Exploratory Data Visualization”
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I also experimented with this presentation of the data, but I ended up doing one big bar chart with all the names in one graph. I like how you can see the trends in each individual name over the decade and essentially see multiple “stories” at once in the graphical representation. Did you experiment with any other graph types and did you have to take out any data points? Or were you able to include most, if not all the data from the original data set?
This is an interesting way to show the change in time for each name. A graph showing the change over time for a specific name is a great way to show the data, however I am thinking this might be too many names to each have an individual graph. This makes it a bit tedious to try and compare names to each other.
I find your visualization quite insightful, since you clearly showed the trend of how the frequency of names changed in the past 10 years. But is it possible to convert this massive series of graphs in to two separate smaller graphs, each representing the male names and females names? But nevertheless you did a great job.