We wanted to use Facebook's API to extract data on nearby places (restaurants/breweries/coffee shops etc.). This includes weighing them by ratings and satisfaction of previous customers. Doing this allows us to prioritize what could be considered the higher quality establishments (and more popular) that the user may be more inclined to visit.

What it does

Our app will query the Facebook API to return nicely formatted JSON data to us. This is based on location and business type. We then prioritize businesses with higher ratings and traffic, and show those results to the user.

How we built it

We built it using node, and entirely using Javascript and function calls to MongoDB to pull our data back. We also plan to use Javascript to generate HTML for the front-end interface.

Challenges we ran into

Properly using MongoDB's Stitch back-end service was our first hurdle. It was difficult at first to figure out how to properly store the data, and then retrieve it as needed. The next challenge was user input and a GUI.

Accomplishments that we're proud of

We are proud to have successfully connected and used MongoDB's Stitch service. During the development of this project, we recognized the high utility of their service, and plan on using it in the future.

What we learned

We learned how to use Facebook's API to extract information on places. The way their API works for extracting information on people is also similar. So, this knowledge could be used in the development of future web apps. The other important knowledge we take away from this Hackathon is MongoDB's Stitch service.

What's next for Nearby Restaurant Analytics

For further development of this project, our first important goal is to properly flesh out the front-end interface for the user to customize the use of the API. This includes a location selector using (possibly) google maps integration, a list box for the user to enter multiple categories, grouping rating prioritization by category, and more.

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