One of the most interesting aspects of working on this project was the strategy we used for refining our primitive city detection. We gathered pairs of article titles & bodies (hoping we'd later accurately scrape them with the readability.js library...) and set them up as test fixtures. While TDD seems to go out the window during hackathons, it was crucial for us to quickly and efficiently iterate while making sure to catch as many edge cases as possible.

In typical hackathon fashion, the code got mangled when I had to switch the data store from leveldb (node) to flat json files (chrome extension) so the latest version in the repo isn't the most graceful. But here's a hacky node app for processing cities1000 data and then running mocha tests against a set of articles:

https://github.com/dzhang50/rlt/tree/master/node

Hope someone finds the technique and code useful, interesting, or at least amusing.

Extracting what location a news article is about is fascinating work. Unlike geocoding you don't get a fixed query.

The only commercial solution I'm aware of is Yahoo!s PlaceSpotter (middle of the page https://developer.yahoo.com/boss/geo/). Yahoo uses it to better target ads against content, e.g. news articles. I'm sure Google has something similar in-house.

Looks great but you guys have a name conflict with a major bus provider up and down the east cost. http://www.vamoosebus.com/

Ha yes realized as much a few weeks after the hackathon. We're not planning to develop the idea further so not really an issue...

That's 100 times better than anything else I've seen produced at a weekend hackathon. You guys are amazing!

Aw thanks Bill!

Dunno why but you seem to have a really bad Web of Trust score :( Results of a bad former owner of the domain?

Really good hack guys. Keep building it! (I do agree the name is not great)