Local, national and international news typically exist in different spheres with little continuity between them. The world around us does not happen in clearly defined sections though. This project aims to put those buckets of news onto a single continuum by applying some geography theory (distance decay) to news.

One of the basic principles of geography is that a place is more connected to places that are physically closer. People's behaviors change based on the distances between places. A typical example is that a person will be willing to travel down the street to buy a jug of milk, but not much further. But for rare and extremely important needs, such as specialized medical care, people are willing to travel across the globe.

If you think of news as having interest instead of a trip, the same holds true for news. A pot hole or a minor crime on your street, will be of high interest, but that falls of pretty quickly as you get away from a location. However, a cataclysmic event on the other side of the world would still be of interest despite the distance.

By mining existing feeds of news that travel with location information -- such as those form Everyblock, Placeblogger, Outside.in -- we can find and organize the information that is likely to be of interest to somebody from a specific location, be that item on your block, the next town, or another state.

We'll produce a website and API, where you can see the news that's important to any point. It will be built on top of GeoDjango, and along the way we'll spin parts out as open source projects. While the scope of the technology is pan-geographic, Brooklyn, N.Y. would make an ideal starting point because of an abundance of existing geographically tagged news, and a consistent density.