I had a startup that built almost exactly the same thing in 2009 (at the peak of 'hyperlocal' hype), so I have some advice. We ended up creating branded widgets for partners and attempted to monetize with advertising.
1. Your biggest problem is going to be acquiring audience; 'if you build it, they will come' is probably not going to play out unless you offer something above and beyond a typical aggregation web site.
2. Consider the utility of granular location-specific news. For many users what you will be showing is either very little content (because there isn't any) or content from a larger geographic region. At that point you have to ask who your audience is and what you provide to them over a bigger regional news source.
3. Keeping up with available sources will be a big challenge. Remember, if you're just aggregating from the big sources, you're not providing much; the appeal to an aggregator is that it gets and filters everything. If you have to go to another source as a user, there's no point in using this site.
4. The site looks pretty bare-bones. It could use some design help across the board.
5. The 'secret sauce' of a site like this is actually not aggregation and automation ... it's curation. If you read 100,000 RSS feeds (if and where you can even find them these days!) you still need to separate the wheat from the chaff. That's why Reddit works - it's effectively a user-curated aggregate site that leverages a scoring algorithm to (ostensibly) bring the best stuff to the top.
Edit: Forgot one of the biggest challenges. I don't know how you're doing your address extraction, but one of the toughest things we ran into were false positives. Articles that reference a location which we geocode but really weren't about that location. So you'll analyze a whole story (which can get very computationally expensive) and find 4 locations:
1600 Pennsylvania Avenue
Washington, D.C.
Washington
Zimbabwe
Now this story is about the Zimbabwe ambassador having a meeting with the president. Two of those addresses are where the story is, two probably aren't (Washington would probably not be geocoded to D.C.). So you file the story in:
Washington, D.C.
Washington, USA
Zimbabwe
Now you've just got noise. NLP can help but it's not enough - you will mis-geolocate this story. When that happens a lot the results are not reliable and, again, your potential audience does not see the utility.
Anyway, best of luck.