Search Results for: visualization

Visualizing the Campaign Contributions to Senators in the 110th Congress — The TARP EDITION [Repost from 3/26])

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This is a repost our previous Senators of the 110th Congress Campaign Finance Visualization. Last week we highlighted one specific element of the graph (i.e. Senator Dodd and the TARP Banks). Now, we wanted to bring the full graph back to the front of the page for your consideration.  Here is the content of the old post with a few small additions…. “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.  One Important Point the Visualization Algorithm we use does force the red team, blue team separation.  Rather, the behavior of firms and senators produces the separation. Four 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– Larger = More Money  (3) Senators …

Christakis and Fowler in Wired Magazine

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Today marks the official release of Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives by Nicholas A. Christakis & James H. Fowler.  There has been some really good publicity for the book including the cover story in last Sunday’s New York Times Magazine. However, given the crisp visualizations — my favorite is the above article from Wired Magazine.  Click on the visual above to read the article!

The Structure of the United States Code

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Formally organized into 50 titles, the United States Code is the repository for federal statutory law. While each of the 50 titles define a particular substantive domain, the structure within and across titles can be represent as a graph/network. In a series of prior posts, we offered visualizations at various “depths” for a number of well know U.S.C. titles. Click here and click Here for our two separate visualizations of the Tax Code (Title 26).  Click here for our visualization of the Bankruptcy Code (Title 11).  Click here for our visualization of Copyright (Title 17). While our prior efforts were devoted to displaying the structure of a given title of the US Code, the visualization above offers a complete view of the structure of the entire United States Code (Titles 1-50). Using Seadragon from Microsoft Labs, each title is labeled with its respective number. The small black dots are “vertices” representing all sections in the aggregate US Code (~37,500 total sections). Given the size of the total undertaking, in the visual above, every title is represented to the “section level.”  As we described in earlier posts, a “section level” representation halts at the section and thus does not represent any …

Visualizing Dynamic Networks with Python, Igraph, and SONIA

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igraph2sonia Example 1 from michael bommarito on Vimeo. When it comes to quickly motivating a point or engaging students in a classroom, one of the most effective tools is visualization. Not only do movies provide fun and excitement, but they also allow viewers to leverage the abilities of the visual cortex to infer dynamics and patterns in the animated system. For our recent research, dynamic graphs are the type of system of interest. As I’ve covered before, Python is my language of choice for most programming tasks. Furthermore, Python is a very accessible language, even for beginners. However, when it comes to visualizing dynamic networks, we need another tool.  Our tool of choice is SONIA, the Social Network Image Animator. I thought I’d provide a helpful little function to generate SONIA input files from igraph objects, along with a few examples. This function takes as input an igraph.Graph object and a file name to store the SONIA output in. Every vertex in the Graph object should have a time attributed specified, either simply as an integer indicating the start time, or as a tuple or list of the form (startTime,endTime). Check out the following two examples if you need more …

Copyright → Title 17 U.S. Code w/ Sea Dragon From Microsoft Labs

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  This is part of our ongoing visualizations of the United States Code. For previous posts visualizing other portions of the code see Title 26 Tax and Title 11 BK. So, we wanted to test out the new Sea Dragon Visualizer from Microsoft Labs and thought Title 17 Copyright would be a fun way to give it a go.  In this visual, each of the chapters under Title 17 is separately colored. To use the visual, start in the center with the large label “Title 17 U.S.C.” and traverse the graph all the way out to any section or subsection. Sea Dragon should allow the user to smoothly zoom in and read any node.  We love the interface.   In our view, the Full Screen Visual is the best.  You can access it by clicking the Full Size Button on the far right.  Also, if for whatever reason you zoom in too far, just use the Home Button to go back to the Full Image. Enjoy but note SeaDragon relies upon Silverlight and Javascript (so you might need to install this).

Law in Structure of Academic Disciplines [Repost]

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This article offers a very interesting insight into the structure of academic disciplines. Using a variety of sources, the authors collected nearly 1 billion interactions from scholarly web portals including Thomson Scientific, Elsevier, JSTOR, etc.    Residing between Economics, Sociology and International Studies, notice the location for legal studies in the upper center portion of this screen print. The Full Size visualization as well as relevant analytics are  available within the paper.  Among other things, the approach undertaken by Johan Bollen, Herbert Van de Sompel, Aric Hagberg, Luis Bettencourt, Ryan Chute, Marko A. Rodriguez & Lyudmila Balakireva provides an alternative view of the current structure of the academic disciplines from that offered in existing bibliometric studies.

Tracking the TARP [From Information Aesthetics]

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Information Aesthetics is now highlighting Subsidyscope — a project designed to track how various institutions receive federal monies.  Of particular interest is their visualization of disbursements under the Troubled Asset Relief Program (TARP). Sponsored by the PEW Charitable Trust, the site also contains .csv files for most of the underlying data.

Visualizing Job Losses from 2007-2009

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For those of you who may have missed it, here is this visualization of geographic density of job losses around the country. As a person living in Michigan, it is fairly sobering. Click above to see the full movie starting in January 2007 and utilizing data through March 2009.      

Measuring the Centrality of Federal Judges

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Above are some graph statistics from our paper Hustle and Flow: A Social Network Analysis of the American Federal Judiciary.  The paper measures the aggregated window of 1995-2005. These, of course, are not the only measures of centrality but they are commonly used in the network science literature. If you are interested — click through to the paper for a description of how these measures are calculated.   We believe these and other elements of the paper offer a good cut on the question of social prestige within the American Federal Judiciary. For example, in the paper we offer a graph visualization where Judge now Justice Alito as well as Judge turned Attorney General Michael Mukasey occupy an extremely central social position (just outside of the Top 25).  We think this is useful because their social prestige is measured at a time period prior to their respective nominations. With appropriate control variables for the respective office in question … perhaps we might have been able to predict their nomination? For purposes of the current Supreme Court opening and conditioned on the selection of a lower court justice with “blue team” credentials, we believe these graph statistics indicate Judge Sonia Sotomayor, Judge Merrick Garland , Judge William Fletcher & Judge …

The Bailout Breakdown from the Associated Press

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Datavisualization.ch/ recently highlighted this interactive “Bailout Breakdown” offered by the Associated Press….. “Bailout Breakdown from Associated Press is an interactive applet that lets the user analyze the recipients and amounts of the $700 Billion bailout plan from the American government. The data is presented as a scatterplot with additional information about the representations when the hovers over a plotted item. The markers are color coded to distinguish between pending, pre-approved, approved and paid status.”  At the end of his post, Benjamin Wiederkehr offers some principled critiques of the visualization techniques employed by the authors. Notwithstanding, we still thought it was still worthy of highlighting.

Classifying the US Patent Hierarchy

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The United States Patent and Trademark Office patent classification scheme organizes 3 million patents into about 160,000 distinct patent classes. This visualization by Katy Börner, Elisha Hardy, Bruce W. Herr II, Todd M. Holloway, & W. Bradford Paley considers the organizational schema used to classify patents at the US Patent Office.  Their article Taxonomy Visualization in Support of the Semi-Automatic Validation and Optimization of Organizational Schemas was published in the Journal of Informetrics in 2007. From the Abstract: “The taxonomy visualization and validation (TV) tool introduced in this paper supports the semi-automatic validation and optimization of organizational schemas such as file directories, classification hierarchies, taxonomies, or other structures imposed on a data set for organization, access, and naming. By showing the “goodness of fit” for a schema and the potentially millions of entities it organizes, the TV tool eases the identification and reclassification of misclassified information entities, the identification of classes that grow too large, the evaluation of the size and homogeneity of existing classes, the examination of the “well-formedness” of an organizational schema, and more.”