Found a well-structured dataset on Recovery Act contracts at fedspending.org. Created a network map for the Awards funded by Department of Defense (except military departments) category. Also serves as a crude user interface to the data. Clicking on contractor nodes links to full record with information about the contract.
Would love to build something like this for the whole dataset, or for the TARP funds.
Fedspending.org already does some flash-based geographic maps to show which states the money is (initially) ending up in.
(click for interactive version)
UPDATE Feb 11, 2010
When working with the this data, I was very surprised to see a name I recognized jump off the screen. Nehalem River Dredging is small 2-6 person operation with a single boat based in my home town. Could they really be receiving a $47,150,000 contract with the Army Corps? Almost 50 million dollars!? That probably greater than the entire yearly economic output of the town. Since this data is known to have some highly-politicized problems (like the flap about the non-existent congressional districts) it seems like there may be some kind of error. My father called up the Port wheer the dredging is being done to investigate. Sure enough, they said there is a two decimal place error in the reported contract amount. It was actually $470,150. So I guess the message is take this data (which is self reported by recipients as I understand it) with a grain of salt, there is plenty of room for two-orders-magnitude errors to sneak in.
Phil Howard has built another corporate consolidation dataset, this one on ownership networks of seed distributors.
[click for pdf version of network]
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Just in case you think that entity resolution problems (matching up names appearing in multiple data sources, while not falsely assuming that everyone named “John Smith” is the same person) are purely an academic concern, I recently got an email from an airline announcing TSA’s new Secure Flight program and asking me to provide them with birth date and gender information when making a reservation:
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Valdis Krebs recently posted a nice viz of the health care lobbying network surrounding Max Baucus using data from LittleSis.org and the Center for Responsive Politics.
Gives an interesting perspective on who might stand to benefit from the current proposals: probably not those of us who are uninsured. Starting to seem pretty unlikely that we will get a reasonable single-payer plan :-(
… from the perspective of its SEC filings
Click here for a movie of the changes in corporate structure at Lehman Brothers from 2003-2008. Each dot is a subsidiary corporation and each line is a declared ownership relation.
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View as: QuickTime | YouTube | other formats.
A movie premier! Yup, this week we are releasing a scare-thriller by the name of Concurrency and Reachability: transmission in a dynamic network. Don’t let the title fool you, the topic is a bit sexier than it sounds, as the underlying network model used to simulate disease transmission was derived from data on real-world sex contact networks.
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For the past several months, Greg and I have been working on project to scrape corporate subsidiary ownership relations from Securities Exchange Commission filings. The first part of the project launched today! So now you can pull down company names and relationships for more than 200,000 publicly traded U.S. corporations and their subsidiaries from http://api.corpwatch.org. If writing code is not your thing, we also built an interactive browser for the data at http://croctail.corpwatch.org.
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The main conference on Social Network Analysis was is in San Diego this year, so I decided to make a trip down. Was nice to step away from the screen and see old and new faces from the far-flung research community. Amusingly, the conference landed in the middle of spring break celebrations, so there were bearded academics wandering geekily around in crowds of drunken sunburnt 20-something revelers.
I gave a presentation at the very tail end of the conference to demonstrate some features of the oilmoney website—including a presidential contribution movie, and bit of analysis on the data. Much of this will be familiar to anyone who has read these earlier posts, but the stat stuff is new.
Warning: the rest of this post is pretty geeky, read at your own risk ;-)
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Dan Newman, director of the money in politics watchdog/transparency site MAPLight.org kindly shared some of their bill endorsement data for me to explore. In addition to providing an elegant interface for accessing California and U.S. Federal campaign contribution data and voting records, MAPLight’s interns do extensive research to determine various organizations positions on bills that are being voted on in Congress. These endorsement and opposition relationships can be thought of as ties linking the organizations to the various bills they take a position on. The ties can then be assembled to form—yup, you guessed it—networks of bills and their supporters. My hope is that giving the bill data a relational treatment might reveal some of the coalitions and give additional context for each organization’s position.
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I’m very interested in trying to figure out ways to map the political landscapes and power structures that are operating around us. I’d like to be able to see various organizations and political actors in the context of their allies, enemies, and supporters in order to understand where the political boundaries are between various factions.
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