We are working hard to produce more original content for the site. In the meantime, we want to share some of our favorite projects and papers. Many of you have probably seen this visualization of housing prices–including the run up to 2007. For those of you not previously familiar, the authors plotted inflation adjusted US Home Prices (1890-2006) on a roller coaster! Pretty creative stuff….
NPR’s Nina Totenberg is reporting that Justice Souter is planning to retire at the end of the current Supreme Court Term. As noted in the NPR report, the short list of replacements may include Elena Kagan, Diane Wood and/or Sonia Sotomayor (who is in the network above near Justice Stevens). If President Obama decides to look beyond this early short list, he might consider one of the socially prominent federal jurist mapped in above visualization. We have a much more detailed prior post on the underlying paper Hustle and Flow: A Social Network Analysis of the American Federal Judiciary located here. To see the full visualization contained within the paper, click on the slide above or click here.
This is the second post related to our recently released paper Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate. We believe a hierarchical depiction of the network helps uncover latent distribution of authority present in the law professor network. Specifically, between 10-15 law schools are responsible socializing a significant percentage of the future legal academics. This extreme skewing of social authority presented in the visualization is also displayed in the above log-log of the degree distribution. While the -1.93 alpha level and size of the network falls outside the traditional power law/scale free range, the plot above does demonstrate the extreme skewing of social authority among institutions of legal education. This is the time of year when there is significant discussion of the US News Rankings. We believe there is a wide-class of available data—data which could be characterized as the revealed preferences of important actors within the American legal system. For example, we have previous offered information on how judges select clerks from law schools? Now, we offer some empirical insight into how hiring committees vote over various institutions? Coming later in the week, we will offer a Netlogo GUI which allows a user to start ideas at various institutions and …
OpenSecrets.org went open with their data today. In honor of this very significant act and its ramifications on future government transparency, I’ve decided to produce a quick visualization to answer a question I’ve long been intrigued by – the publicly traded holdings of Senators. One good proxy to this question is the Personal Financial Disclosure data released today by Open Secrets. In the words of Open Secrets, Any legal ownership a person has in a company or property is classified as an asset, including brokerage accounts, corporate bonds and stocks. For the most part, lawmakers seem to have a stake in big-name, recognizable companies and properties. They need to report only assets worth more than $1,000 at the end of the calendar year, or producing more than $200 of income. (One note about mutual funds: Filers are not required to provide detail on funds’ individual holdings.) Any purchases, sales or exchanges of assets during the year of more than $1,000 must be disclosed as transactions. Reporting the value of a primary residence, unless it produces income, is not required. This visual represents a very rough cut of this data for the holdings of Senators in 2007. As is often the …
In our previous graph visualizations of contributions to members of the House and Senate, there have been two types of entities: Contributors and Congressmen. This division manifests itself in the dynamics of the graph as well – Contributors give only to Congressmen, and Congressmen receive only from Contributors. A network with this property is bipartite, and there are a number of additional ways to represent the relations contained therein. One such representation is called the single-mode projection. In this simplification, only elements from one of the two sets (e.g., Congressmen or Contributors) are displayed. A relationship exists between two elements in the visual if they share a relationship with at least one member of the other group. For instance, both Bernie Sanders and Sam Brownback received campaign contributions from the the National Association of Realtors. Thus, the Congressmen both have a relationship with the same Contributor, and the simplest single-mode projection would represent this as a shared relationship between the two Congressmen. For more on this projection or other representations, see Wasserman and Faust (1994) or M. E. J. Newman, S. H. Strogatz, and D. J. Watts, Phys. Rev. E 64, 026118 (2001). As the Sanders-Brownback example above demonstrates, however, it is …
DOCUMENTATION FOR THE VISUALIZATION Visualizing the Campaign Contributions to the Representatives of the 110th Congress — The House Edition By Michael Bommarito & Daniel Katz University of Michigan Center for the Study of Complex Systems Department of Political Science BASIC OVERVIEW: 110th Congress = January 3, 2007 – January 3, 2009 435 Voting Members of the United States House of Representatives + District of Columbia (Eleanor Holmes Norton) + Puerto Rico (Luis Fortuno) + Virgin Islands (Donna Christian-Green) + American Samoa (Eni F H Faleomavega) + Guam (Madeleine Z Bordallo) Click here and here for the Senators of the 110th Congress. BASIC RULE: Squares (Institutions) Introduce Money into the System and Circles (Congressmen) Receive Money. DATA OVERVIEW: Using recently published data on campaign contributions collected by the Federal Election Commission and aggregated by the Center for Responsive Politics our visualizations track large money donations to members of the 110th Congress over the 2007 – 2008 window. It is important to note that most of these organizations did not directly donate. Rather, as noted by the Center for Responsive Politics “the money came from the organization’s PAC, its individual members or employees or owners, and those individuals’ immediate …
One of our very first posts highlighted a recent article in Science Magazine describing the possibilities of and perils associated with a computational revolution in the social sciences. A very timely article by Paul Ohm (UC-Boulder Law School) entitled Computer Programming and the Law: A New Research Agenda represents the legal studies analog the science magazine article. From information retrieval to analysis to visualization, we believe this article outlines the Computational Legal Studies playbook in a very accessable manner. Prior to founding this blog, we had little doubt that developments in informatics and the science associated with Web 2.0 would benefit the production of a wide class of theoretical and empirical legal scholarship. In order to lower the costs to collective action and generate a forum for interested scholars, we believed it would be useful to produce the Computational Legal Studies Blog. The early results have been very satisfying. For example, it has helped us link to the work of Paul Ohm. For those interested in learning more about not only the potential benefits of a computational revolution in legal science but also some of the relevant mechanics, we strongly suggest you consider giving his new article a read!
This short but cool article from MIT Technology Review discusses recent work by Computer Scientist Jon Kleinberg and his Cornell colleagues. This very nice visualization is the byproduct of their efforts at data mining more than 1 million online news items per day in the weeks leading up to the 2008 presidential election.
CLICK ON IT AND YOU CAN ZOOM IN and READ EVERY LABEL! MOTIVATION: We have become interesting in visualizing the structure of the law including its components and subcomponents. In reduced form, statutes, regulations and certain other units of the law can be characterized in graph theoretical terms. While we do not make deep inroads on the content of this above graph, we do generate a tree traversable visualization for its structure. Much of my training in law school (particularly in the so called “code-based” classes) was focused upon developing mental models for the structure and content of graphs such as the one displayed above. In my case, I believe the usage of such a visualization early in a code-based course would have been beneficial. Thus, we offer this traversable visualization to the world for not only its research value but also for pedagogical purposes. INSTRUCTIONS: Start in the MIDDLE at the “11 U.S.C.__ ” Label and traverse out. BREAKDOWN OF THE VISUAL: GREEN NODE LABELS = for SECTIONS {In the Example below, 11 U.S.C. § 101} YELLOW ARCS — Chapter 7 of Title 11 = LIQUIDATION BLUE ARCS — Chapter 11 of Title 11 = REORGANIZATION (aka “Filing Chapter 11“) RED ARCS and …
As part of our commitment to provide original content, we offer a Computational Legal Studies approach to the study of the current campaign finance environment. If you click below you can zoom in and read the labels on the institutions and the senators. The visualization memorializes contributions to the members of the 110th Congress (2007 -2009). Highlighted in green are the primary recipients of the TARP. In the post below, we offer detailed documentation of this visualization. Three Important Principles: (1) Squares (i.e. Institutions) introduce money into the system and Circles (i.e. Senators) receive money (2) Both Institutions and Senators are sized by dollars contributed or dollars received (3) Senators are colored by Party. To view the full image, please click here. To view the full image, please click here. By Michael Bommarito and Daniel Martin Katz. Center for the Study of Complex Systems Department of Political Science University of Michigan, Ann Arbor
Visualizing the Campaign Contributions to the Senators of the 110th Congress — The TARP EDITION By Michael Bommarito & Daniel Katz University of Michigan Center for the Study of Complex Systems Department of Political Science BASIC OVERVIEW: 110th Congress = January 3, 2007 – January 3, 2009 100 Members of the United States Senate Click Here for the House of Representatives BASIC RULE: Squares (Institutions) Introduce Money into the System and Circles (Senators) Receive Money. DATA OVERVIEW: Using recently published data on campaign contributions collected by the Federal Election Commission and aggregated by the Center for Responsive Politics at http://www.opensecrets.org, our visualizations track large money donations to members of the 110th Congress over the 2003-2008 window. Given that some senators resign or lose reelection, a subset of the senators of the 110th Congress have served less than the full 2003-2008 window. While this imposes some comparability issues, many of these new members faced challenging races and thus attracted significant sums of money. It is important to note that most of these organizations did not directly donate. Rather, as noted by the Center for Responsive Politics “the money came from the organization’s PAC, its individual members or employees or owners, …
This paper written by CLS Blog Co-Founder Daniel Katz and Derek Stafford from the University of Michigan Department of Political Science representes an initial foray into Computational Legal Studies by the graduate students here at the University of Michigan Center for the Study of Complex Systems. The full paper contains a number of interesting visualizations where we draw various federal judges together on the basis of their shared law clerks (1995-2004). The screen print above is a zoom very center of the center of the network. Yellow Nodes represent Supreme Court Justices, Green Nodes represent Circuit Court Justices, Blue Nodes represent Circuit Court Justices. Here is a wide shot of the broader network visualized using the Kamada-Kawai visualization algorithm: Here is the abstract: Scholars have long asserted that social structure is an important feature of a variety of societal institutions. As part of a larger effort to develop a fully integrated model of judicial decision making, we argue that social structure-operationalized as the professional and social connections between judicial actors-partially directs outcomes in the hierarchical federal judiciary. Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given social structure …