United States Supreme Court Zeitgeist
Data Visualization of United States Supreme Court Justices
The United States Supreme Court is one of the most secretive branches of the federal with only nine justices. These nine justices experience little checks and balances given their appointments are for life. As such, it is difficult to identify how a potential justice may vote over the term of their lifetime appointment. Basic demographic (e.g., Law School, age appointed, tenure, etc.) is sometimes the only means of guessing their potential slant. By visualizing the SCOTUS (Supreme Court of the United States), I hope to find potential anomalies that can better explain why a justice from a particular law school was appointed or why they stayed on the Supreme Court for a particular length of time.
Responsibilities: Data Gathering, Data Visualization
For this dataset, I searched the Internet for information regarding each Supreme Court Justice. I utilized a variety of sources from Wikipedia to Adherents.com (seen in the references section). From this data, I came across a total of 154 nodes that includes the Presidents who appointed each Justice and the Justices who were appointed (some Justices were appointed twice and Howard Taft was both a Justice and a President).
The Dataset included the following information:
Religion, Political Party of President appointing, Birth year, Death year, Age appointed, Whether Chief justice or not, Term in days, Law School, Birth State, Why they left their term, and year left term).
NodeXL is a powerful tool that allows users without any programming experience to easily visualize a data network. It performs such functionality in an environment, Microsoft Excel, familiar for many users. NodeXL is particularly strong at allowing users to dynamically filter out content and manually move a node to a particular location. Tool tips explaining functions are also helpful for learning how to use the application. The option to select a specific shape for nodes adds a nice customization for the specific needs of visualizing networks.