Search Results for: visualization

Network Analysis and the Law — 3D-Hi-Def Visualization of the Time Evolving Citation Network of the United States Supreme Court

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What are some of the key takeaway points? (1) The Supreme Court’s increasing reliance upon its own decisions over the 1800-1830 window. (2) The important role of maritime/admiralty law in the early years of the Supreme Court’s citation network. At least with respect to the Supreme Court’s citation network, these maritime decisions are the root of the Supreme Court’s jurisprudence. (3) The increasing centrality of decisions such as Marbury v. Madison, Martin v. Hunter’s Lessee to the overall network. The Development of Structure in the SCOTUS Citation Network The visualization offered above is the largest weakly connected component of the citation network of the United States Supreme Court (1800-1829). Each time slice visualizes the aggregate network as of the year in question. In our paper entitled Distance Measures for Dynamic Citation Networks, we offer some thoughts on the early SCOTUS citation network. In reviewing the visual above note ….“[T]he Court’s early citation practices indicate a general absence of references to its own prior decisions. While the court did invoke well-established legal concepts, those concepts were often originally developed in alternative domains or jurisdictions. At some level, the lack of self-reference and corresponding reliance upon external sources is not terribly surprising. …

6,000 Pages Tell the World’s History [via GE Data Visualization]

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“It’s true. We’ve scanned 6,000 pages of GE’s annual reports to build this interactive visualization. But why? What’s the point? Not only does this provide a rich history of how GE has always been at work building, moving, powering and curing the world, but it is a true reflection of how the economy, U.S. and the world as a whole has progressed from 1892 until 2011. By diving deep into key terms, users can uncover interesting stories about innovation over the last century. Explore for yourself! About this data: The data in this visualization is sourced from all of GE’s annual reports from 1892 until 2011.”

Health InfoScape [ via GE Data Visualization Lab ]

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From the GE Page: “When you have heartburn, do you also feel nauseous? Or if you’re experiencing insomnia, do you tend to put on a few pounds, or more? By combing through 7.2 million of our electronic medical records, we have created a disease network to help illustrate relationships between various conditions and how common those connections are. Take a look by condition or condition category and gender to uncover interesting association. About the Data: “The information used for this visualization is based on 7.2 million patient records from GE’s proprietary database, and represents some of the conditions that commonly affect Americans today. By investigating how different ailments are related, one may gain various insights about condition associations. The numbers and percentages are meant to represent general trends. Looking at the data in new ways like this can help us understand health and gain new insights about how to take better care of ourselves and the healthcare system.”

Six Degrees of Marbury v. Madison : A Sink Based Visualization [v2]

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The visualization above is something we are calling the “six degrees” of Marbury v. Madison.  It was originally produced for use in our paper Distance Measures for Dynamic Citation Networks. Due to space considerations, we ended up leaving it on the cutting room floor.  However, the visual is designed to highlight the idea of a “sink.” Sinks are one of the core concepts which we outline in Distance Measures for Dynamic Citation Networks, 389 Physica A 4201 (October 1 2010). Looking through the prism of a citation network, sinks are the root to which a given legal concept, academic idea or patent based innovation can be drawn. From each citation in a non-sink node, it is possible to trace the chains of citations back to their root (which we call a sink).  In the visualization above, the root or sink node is the famed United States Supreme Court decision Marbury v. Madison.  Starting from the center and working out to the edge, the first ring are cases that directly cite Marbury v. Madison. The next ring are cases which cite cases that cite Marbury v. Madison.  The next ring are cases which cite cases which cases that cite Marbury v. Madison and so on… Anyway, one of the major contributions of our Distance Measures …

David McCandless: The Beauty of Data Visualization [TED]

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From the talk abstract: “David McCandless turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut — and it may just change the way we see the world.”

Six Degrees of Marbury v. Madison : A Sink Based Visualization

clsadmin

The visualization above is something we call “six degrees” of Marbury v. Madison.  It was originally produced for use in our paper Distance Measures for Dynamic Citation Networks. Due to space considerations, we ended up leaving it on the cutting room floor.  However, the visual is designed to highlight the idea of a “sink.” Sinks are one of the core concepts which we outline in our Distance Measures for Dynamic Citation Networks paper.  Looking through the prism of a citation network, sinks are the root to which a given legal concept, academic idea or patent based innovation can be drawn. From each citation in a non-sink node, it is possible to trace the chains of citations back to their root (which we call a sink).  In the visualization above, the root or sink node is the famed United States Supreme Court decision Marbury v. Madison.  Starting from the center and working out to the edge, the first ring are cases that directly cite Marbury v. Madison.  The next ring are cases which cite cases that cite Marbury v. Madison.  The next ring are cases which cite cases which cases that cite Marbury v. Madison and so on… Anyway, one of the major contributions of …

Large Scale ( 130,000 + ) Zoomable Visualization of a Twitter Network

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Starting with the Michael Bommarito’s twitter handle mjbommar, we built this visualization by collecting Mike’s direct friends, friends-friends, friends-friends-friends, etc. until we decided to stop …. just after passing 130,000 total twitter handles.  Using the Fruchterman-Rheingold algorithm, we visualized a network where |V| = 130365, |E| = 197399. Those interested in reviewing some other twitter visualizations, please consult Nathan Yau at Flowing Data who has collected some of his favorites.  To our knowledge, the visualization we offer above is one the larger visualizations of twitter that have been produced to date. When you zoom in, you will notice we have flagged some of the celebrity twitter users we detected in the mjbommar friends-friends-friends, etc. network.  For example, as shown above Ashton Kutcher (aplusk), Chad Ochocinco (OGOchOCinco) and RainnWilson (rainnwilson) are contained therein. Given the budget limitations of this blog, we cannot host this visualization in house. However, if you click the picture above, you can access the visual from Seadragon … a zoomable visualization platform from Microsoft Labs.

The Senate Campaign Contribution Network: A Visualization Repost in Light of the Court’s Decision in Citizens United v. Federal Election Commission

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Today’s decision in Citizens United v. Federal Election Commission has justifiably generated a significant amount of media / blogosphere coverage. For those not familiar with the Court’s decision, there is a full roundup of analysis available at SCOTUS Blog and Election Law Blog. In light of today’s decision we decided to repost highlights of our visualization of the campaign contribution network for the Senators of the 110th Congress. For those interested, the original post is offered here and the documentation is here. Also, there are variety of other related posts related to the 110th Congress available under this tag.  Suffice to say, in light of today’s decision, there is likely to be some significant changes to the contribution network of the 111th Congress (Second Session) ….

Cash for Clunkers – Visualization and Analysis

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Cash for Clunkers: A Dynamic Map of the Cash Allowance Rebate Systems (CARS) Some Background on the Car Allowance Rebate System (CARS) From the official July 27, 2009 press release – “The National Highway Traffic Safety Administration (NHTSA) also released the final eligibility requirements to participate in the program.  Under the CARS program, consumers receive a $3,500 or $4,500 discount from a car dealer when they trade in their old vehicle and purchase or lease a new, qualifying vehicle. In order to be eligible for the program, the trade-in passenger vehicle must: be manufactured less than 25 years before the date it is traded in; have a combined city/highway fuel economy of 18 miles per gallon or less; be in drivable condition; and be continuously insured and registered to the same owner for the full year before the trade-in. Transactions must be made between now [July 27, 2009] and November 1, 2009 or until the money runs out.” On August 6, 2009, Congress extended the program adding $2 billion dollars to the program’s initial allocation. For those interested in background, feel free to read the CNN report on the program extension. On August 13, 2009, the Secretary offered this press release …

Well Formed Eigenfactor.Org–Wonderful Visualization of CrossDisciplinary Fertilization, Information Flow & The Structure of Science [Repost]

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Given our interest in both interdisciplinary scholarship and the spread of ideas, we wanted to highlight one of our favorite projects–eigenfactor.org. Here is basic documentation from their website.  There are also links to academic papers offering far more detailed documentation for the data and algorithm choice.  In particular, read Martin Rosvall and Carl T. Bergstrom, Maps of Random Walks on Complex Networks, Proc. of the Nat. Academy of Sci. 105:1118-1123 (2007).  The above visualizations are written in Flare by Moritz Stefaner. Click on the slide above to reach these interactive visualizations. These mapping offer reveal the reach of various publications across disciplines–some are insular and others have incredible reach.  The inner rings are journals and the outer rings are the host disciplines. Enjoy!