I just spent some time comparing various tools for publishing web visualizations of region-coded data. I looked at some Google tools, ManyEyes, and Tableau Public. This is what I learned using my demo example. Please let me know of any other tools I should test…
Guess who is pumping in the money to support CA Proposition 23 to roll back California’s global warming legislation?
Yup, mostly its the companies that would be regulated when the law goes into effect. I’ve been working for the past few weeks to build prop23.dirtyenergymoney.com, an interactive network chart of the funding flows. Its an adaptation of our previous dirty energy money site, but using campaign finance data from California, and from OpenSecrets.org for the Federal PACs that are contributing in California. I think it is interesting to see the multiple layers, how the various group funnel in money and influence. I hope to be able to do an expanded version which would also show some of the other strings that Koch is pulling, why Chevron isn’t investing in Prop 23 (it seems they are backing 25 & 26 instead), and the the backers of the various No on 23 campaigns.
Also, can anyone help me figure out what happened to the $5000 contribution from Western Petroleum Marketer’s Association? It was in an earlier version of the data, but now seems to be missing from the filings. It seems that Tesoro may have solicited support for Prop 23 in their meetings, and I’d like to be able to include them.
Update: We’ve added panning and zooming features on the graph. We’ve also done a second chart for CA Proposition 26: prop26.dirtyenergymoney.com
I’m interested in making political structures visible. Trying to put those half-realized connections and linkages into a tangible form–a map that we can point to. I recently located an online database of conservative funding relationships created by Media Matters Action Network. I was able to scrape the site and (with their permission) experiment with some network diagrams in pdf form to visualize the funding relationships among “angel investor” foundations and right-wing organizations. Continue reading →
… but only a few owning companies. Another great study and diagram of brand ownership relations by Philip Howard.
…To visualize the extent of pseudovariety in this industry we developed a cluster diagram to represent the number of soft drink brands and varieties found in the refrigerator cases of 94 Michigan retailers, along with their ownership connections.
Although, according to the Soda vs. Pop map, since the study was in Michigan, maybe it should be labeled “Pop” not “soft drinks”? ;-)
I thought that this map of overlapping topics between media outlets was a cool idea. The resulting network seems like a fairly undifferentiated core-periphery structure, which seems typical of a lot of topic-maps-as-networks I’ve seen. Does this reflect a property of media networks, or is this what topic maps look like? Maybe a threshold filter on the edges to then out weak (or strong) ties would reveal more subtle structure? I thought the blockmodel reduction of the network was a helpful summary. The model does seem to be backed by some substantial statistical work and
… “Results of the estimation indicate that both production volume and common ownership affect the topic overlap of news outlets.”…
Michael Heaney and Fabio Rojas just released another great network map in a blog post. This one shows the co-mentions of topics (as coded by the researchers) appearing in the descriptions of panel discussions at the recently concluded 2010 US Social Forum in Detroit. The map functions as a coarse-grained representation of interconnectedness of the various topics, and presumably how important and relatively central they are to the activists and organizers participating in the forum.
A lovely organization co-membership network graph of the delegates to the 2008 party conventions. From Networking the Parties: A Comparative Study of Democratic and Republican Convention Delegates in 2008 (Pre-print. Seth Masket, Michael Heaney, Joanne Miller, and Dara Z. Strolovich). The authors surveyed the delegates at the major party conventions about which groups they were members of. Nodes are groups, links indicate delegates who named both groups. Red only nominated by Republican delegates, Blue only Democratic, Purple = Both. The image gives a nice quick overview of the relative positions of groups, the paper gives more detailed analysis.
Interesting but not surprising to see the Sierra Club / NRA relationship, and NAACP / Young Republicans membership as well. I’m curious what the single red node is between Amnesty International and Sierra Club… Would like to see a version with all the organization names. Does this image match your intuitive sense of the relative political positions of the organizations named?
The Sunlight Foundation recently brought all of its grantees together so that each organization could learn more about what the others were working on. Since they funded the work on the CorpWatch API, I got to attend. They also invited folks to stay over the weekend and attend the TransparencyCamp, a 2-day “un-conference” in DC for folks interested in getting the government to be more open an responsive with its data.
I gave a presentation on the work we did on the CorpWatch API, and why I think it would be a good idea to develop a common standard id system for company and organization names. The talk was streamed live, and archived as well. I sound a bit jet-lagged ;-)
I really enjoyed the un-conference format: participants basically shout out what they want to present or discuss and convince folks to come to their sessions. Got to finally meet face to face with the people who have been doing all the amazing work to provide the data we use in so many projects. Had some great discussions about trying to build some kind of larger project to create a common id system that various organizations could link to so that companies can be correctly matched and aggregated across datasets. Learned a lot. Was especially interested in some of the work being done internationally, seemed at time more pragmatic, less obsessed with the latest shinny new tech toys.
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.