Please let me introduce myself. I am an academic law librarian here at Indiana University School of Law (JD, MSLIS). I am also a doctoral student in Information Science at Indiana University.
I work at the intersection of several fields: law, scientometrics, information visualization, spatialization, network science, and legal informatics. All of these fields are relevant to empirical studies of the law. Let me give you a quick gloss as to each. Scientometrics is the study of science through scientific means. It focuses on quantitative measures such as citation counts, the citation half-life of articles in a particular field, co-authoring proclivities and effects in different fields, etc. Information visualization is the mining of large datasets and using various display algorithms to render pictures of the data. The goal is to facilitate insights that would not be possible otherwise. Information visualization is strongly related to spatialization. This is the spatial rendering of inherently non-spatial data. Network science is the study of large networks consisting of nodes and connections between those nodes. Finally, legal informatics is the study of how computing technology impacts the practice and study of law.
Recently, I have been producing information visualizations relevant to the work of the United States Supreme Court. My goal is to use and evaluate these visualizations in an applied educational setting.
The above visualization represents a spatial distribution of all of the justices of the United States Supreme Court that served during the 1956 to 2004 terms. The spatial layout of the justices is based on their cumulative co-voting percentages. The statistics were taken from 49 years worth of the annual Supreme Court review published in the Harvard Law Review. I used the Harvard Law Review’s O method of co-voting (“agreeing in the opinion of the Court or an opinion announcing the judgment of the Court.”) I also normalized the raw numbers over the amount of cases any two justices heard together (N) to get the cumulative voting percentages.
The spatial distribution is the result of the spring force algorithm in a software tool known as Pajek (free for academic use). The spring force algorithm is analogous to rubber bands being placed between the justices with the higher the percentage of co-voting corresponding to a much stronger rubber band. The final distribution is the net affect of all of the different strengths of the edges (co-voting percentages) between the different nodes (justices). There is an implied element of time moving from left to right with long serving justices (co-voting with numerous different justices) being pulled to the center of the image.
The tables in the image give the following aggregate information spanning the 1956 to 2004 terms: (1) the 10 highest cumulative voting percentages, (2) the 12 lowest cumulative voting percentages, and (3) the most cases heard together. What interests me is the fact that Scalia and Thomas, with a cumulative voting percentage of 67%, do not even rank in the top ten. Top honors go to Warren and Marshall at 88%. [Keep in mind that the figures include unanimous cases. For instances, in non-unanimous cases from the 1994 to 2003 terms, Scalia and Thomas vote together at 78%--the highest for the time span. Source: July 2, 2005 New York Times citing the Harvard Law Review statistics.] The lowest cumulative agreement is between Douglas and Rehnquist. They agreed in just 28% of the cases they heard together. The mean voting agreement for all justices over the 1956 to 2004 terms is 59%.
Over the next couple days I will share more of my visualization work relevant to the Supreme Court, will mention others doing work in this area, and will talk more about the field of domain mapping which is the term that best captures my academic work.