Congrats to the NY Senate for beginning to open more data at http://www.nysenate.gov/opendata!
Here is the network of Senator Allocations of Funding to Community Projects (CPFs): 2009-2010 by Senator or group and zipcode. Line width is proportional to funding allocation.
[click for full-size image]
Related: how do we define what’s public data? Some transit agencies are claiming copyright over transit performance.
Thanks to ProPublica and Sunlight Foundation:
…for the first time digitized one year’s worth of FARA records, making them accessible in a searchable database that allows users to easily follow the money and connect the dots. With the Foreign Lobbying Influence Tracker , anyone can quickly learn what governments are lobbying whom, how often and about what. [source @ ProPublica]
Here are the firms the Congressmen and -women from my home state have been meeting with:
and the countries of the governmental department or foreign firm paying the lobbyists:
Thanks everyone for showing the strong interest in the Lobbyist map. I got a couple nice mentions at Mother Jones and LittleSis.org, but more importantly, I’ve added in all of the other names in the map.
Circles are people, squares are organizations, and white circles are the lobbyists in question.
If you’d rather the image than the flash bits, here you go, all 2.5MB of it.
A zoomable version of the earlier map is here:
[Thanks to Drew Conway for the Sea Dragon zoomable suggestion]
The Huffington Post, along with public contributors, has been collecting a list of former Congressional staffers turned healthcare lobbyists. LittleSis.org has been keeping track of these former staffers, and thanks to their API, we now have a social graph of their relationships.
Former staffers in white (with names), and the rest of the visual field to show that some are MUCH better networked than others.
If there’s interest, I can add the names of the people they are networked with and start some analysis of the group.
As always, click for a larger image.
Update: network map with all names, and in a zoomable widget here.
Late last month, the NY Times had an article about the debate over healthcare legislation taking place in the Senate Finance Committee. Coincidentally, around that time, the folks over at LittleSis, the “free database detailing the connections between powerful people and organizations,” were kind enough to give me early access to their API (thanks Kevin and Matthew!).
So from NY Times:
To LittleSis:
Of the named members in the photo, neither Tom Barthold nor Phil Ellis existed at the time in the LittleSis database, but it’s still showing a pretty networked bunch.
I’d like to see someone do this one better, and include donors.
I occasionally get Twitter replies to random questions I put out there from people I’m not following, but this one had a surprise:
Not only did @answers respond, but also included more info. Intriguing. I followed the link. That’s when I furrowed my brow.
The page it took me to demonstrated what I perceive to be a grave violation of network trust: impersonation.
The website has a post, in their forum, ostensibly from me — and not just in name only. They refer back to my post where I asked the question.
I don’t want anyone to act in my name without my permission. Creating an account with my name, and referring back to a legitimate source with the same name, sure sounds like impersonation to me. Am I over reacting? Or is Mahalo violating network trust?
Idiots of Ants [via Tomorrow Museum].
While not networking exactly, this does touch on our predilection to believe others we do not know, if there are enough of them.
New York Attorney General Andrew Cuomo has settled with a plastic surgery company, alleging the firm published phony positive testimonials and Web sites. Lifestyle Lift, associated with Michigan-based Scientific Image Center Management, agreed to pay $300,000 to New York State and stop posting false endorsements of its facial cosmetic surgery services.
I wonder how long until marketers create entire networks of fake people to promote products. Anyone seen this on Facebook, Myspace, or anywhere?
Creating a network from a sample of communications from approximately 900,000 people on Twitter, the distribution of distinct communication partners result fits the definition of a scale-free network. The power is a little higher than scale-free networks usually described for social networks (2<k<3), but not much.
In honor of the Iranian election, here’s the 2001 social network analysis of the Iranian government by Renfro and Deckro.
