For this blog post I decided to build a data visualization for the data given to us for our final project. This data is information about artists and artworks that have been acquired by the Museum of Modern Art (MoMA) since its start in 1929. I created this visualization with Tableau and used the dimension “gender” and measure “number of records.” I then chose to present the data in the form of “packed bubbles” as I think it reveals a lot about the selected data.
The first revelation is that a lot of data cleaning will need to be done. For example, this visualization shows that some pieces are labeled “()(Male)” while others are labeled “(Male)()” which are actually the same category. There are many other situations like this which means that many categories will need to be merged with software such as OpenRefine. Also if you click on the circles you find that many pieces of data have things like “()” and “()()()” which both indicate that their is no information available. The visualization above also has a large circle with no label which also indicates that no information was found about gender for those pieces of data. As a result, if my group decides that gender is something we want to look into, then we know we can exclude this data.
Another thing this visualization shows is the fact that a lot of these artworks were worked on by multiple people. This was hard to see when just looking at the data table given that the row was so narrow that it only revealed the first gender. As a result, if my group decides to work with gender, we’ll need to decide whether we want to look at pieces of artwork that were worked on by just a single person or if we want to include pieces that were worked on by multiple people.
I like that you talked about how your data visualization has helped you with your data and understanding your data as far as how to clean it. I’m intrigued by your use of bubble graphs to show gender. I personally wouldn’t have thought to do that, but it’s good that it was able to help you understand your data and the assistance cleaning offers.
Great blog post! I have always liked visualizations that make use of the different circle sizes as I find them easy to follow. It’s great that the process of making these visualizations led you to discover problems with your data set. Maybe you could have used different colors to differentiate between male and female. I am really curious to see what visualizations you will use for your final project!