Carbon at scale

From the making the intangible tangible dept. Some nice work from CarbonVisuals. I’m not usually that excited about 3D geo rendering stuff, but this is elegant, simple, and compelling.

A single hour of NYC carbon emissions:

Carbon emissions associated with NYC public buildings:

broken link :-(

I think I need this one attached to my car keys. One Gallon of Gas:

From the methodology document:

Take a typical room 20 feet x 20 feet x 9 feet. Only 400 parts per million of the air in the room is carbon dioxide, which doesn’t sound like a lot. However, that is nearly 11 US gallons of carbon dioxide, which sounds a lot more significant. 3.2 US gallons of that came from burning fossil fuels. If you show people that in every small room there is 3 US gallons of greenhouse gas that we have put there if begins to seem like a big deal.

I’m thinking maybe I should label three empty milk jugs and leave ’em in my living room to trip over, just so I don’t forget.

via infosthetics

Pleasantly Perusing the Paper Pile with Pivots

A nice UI for browsing a database of academic papers: Marian Dörk’s PivotPaths. It arranges papers matching a search term along with a sort of linked tag-cloud of authors and other keywords for easy navigation. All the interaction is really nice (try dragging a line between two authors to compare them!). Papers queried from Microsoft Academic Search. Find it interesting how looking at a different database for my standard search terms reveals unfamiliar sets of papers (my vanity queries turned up nothing in the viz, tho they do have hits in Microsoft Academic Search). Would be really cool if you could point it to google scholar also to compare coverage. I like that, even ‘tho it was built at Microsoft Research, it uses open web standards! (SVG, oog). Have we reached the point where the best way to publicize a new data service is to get someone to build a viz with it? Via infosthetics.

The Other Kind of Network Marketing

Private Equity firms and shady deals from Darwin BondGraham

Oakland political economy journalist/blogger Darwin BondGraham has an interesting article about a court case that appears to reveal massive collusion between private equity firms to manipulate markets and cheat investors when doing leveraged buyouts (I think thats what “LBO” stands for?) of public companies. Darwin includes some network diagrams, apparently bi-partite networks of the major firms and their shady deals extracted from Dahl v. Bain court documents unsealed by the NYT. The document is a surprisingly riveting read, kind of like techno-thriller-horror script.

50 nets of Grey

Just saw this really nice blog post at explaining how to do Exponential Random Graph Modeling (ERGM) using a sexual-hookup network from the TV show Grey’s Anatomy.

Grey's Anatomy sexual-hookup network image

I do some work on some of the dynamics packages in statnet (‘tho not the amazing stats part demoed in the post) so its great to have something to point to explain what the project can do. Now we just need to go back and add the timing information to the edges (who was partnered in which episodes) to be able to estimate the number of concurrent partnerships and look at the epidemic-spreading potential of the network…
(via Brian Keegan)

Open Government Data

…the book

Josh Tauber (the guy behind GovTrack) recently wrote a book on Open Government Data. I think the chapter where he walks through the process of scraping and building a visualization is great, this kind of thing should be required reading for all non-coders who are interested in technology and transparency issues. It walks through really practical examples of why formats matter. Also liked the figure on the right for locating the strengths of various types of data formats with respect to intended use. Makes the great point that a “high-quality” electronic document isn’t always better for some purposes.
Continue reading Open Government Data

Plane Old Networks

This is a catchall post to collect together a number of interesting network images I’ve run across in the last few years. The common feature is that they are all networks that are based in or arise from geography or spatial processes. Unlike most of the networks we often have to work with, these are mostly “planar” (or nearly so) meaning that they can usually be drawn in two dimensions with minimal crossing and distortion.

The map itself

In The Network Analysis of Urban Streets: a Dual Approach Sergio Porta, Paolo Crucitti and Vito Latora convert city streets to networks and examine some of their properties. Figure 2 of the paper below

The six 1-square mile samples of urban patterns (above) and their primal graphs (below): 1. Ahmedabad; 2. Barcelona; 3. San Francisco; 4. Venezia; 5. Wien; 6. Walnut Creek. Cities are so diverse that, at a first sight, it seems hard to imagine that they share any common — though hidden — pattern, which is what they actually do.

Continue reading Plane Old Networks

Adventures with J. P. Morgan and Form 13F

Image of J.P. Morgan Chase & Co's Q42010 security ownership network

Inspired in part by recent work for a client, I finally got around to pulling a long night to play with some Form 13F data from the SEC. This pdf image shows significant ownership relations for J. P. Morgan at the end of 2010. Or at least I think it does. I’d love to hear from anyone who knows more about these types of financial filings.
Continue reading Adventures with J. P. Morgan and Form 13F

Are Your Cookies Colluding?

I recently installed Mozilla’s Collusion add-on for Firefox. It maps out the set of information-sharing relationships between sites as you visit them and they install various 3rd-party-cookies to track your browsing history. Has a neat interactive network viz as a browser plugin where so you can highlight sites you’ve visit and who they reported to.

This image was after a week of low-intensity computer use. Not that surprising to see Google, Facebook and Twitter as very central nodes, tho still impressive to have an image of how many sites report to them. Some sites like I’d at least heard of before, but not some of the other central stats players like or or that appeared lurking in the network.

The visualization seems to be built in SVG using the D3 library’s network code. The interactivity and design are quite lovely, at least for small networks. However, now that I’ve been running Collusion for a month, the graph is quite large and the animation is getting painfully slow and eating my CPU.