A few weeks back I saw an article on HackerNews about how a person was able to classify neighbourhoods of London as up and coming by using non traditional parameters - the density of coffee shops and fried chicken stalls - in addition to the more traditional factors such as property prices and ease of access. We were inspired to do something similar to determine what factors we could find to help a fledgling business decide where to setup it's workspace.

What it does

Launchpad is designed to help 2 types of entrepreneurs: those setting up a restaurant and those who are establishing a tech start-up. The entrepreneur uses the web-app to select what type of business he/she is starting and picks which of the available parameters that we selected he/she would like to use. We then generate heat maps illustrating the data sets and a combined result. The user can then visually identify which area looks promising.

How I built it

We built it using the Django framework with a python back-end. Used YELP, and Google API . BrainTree's API also

Challenges I ran into

The main difficulty we had was locating appropriate data sets. While some were readily available and easy to use such as the Yelp API they had limitations such as a cap on query results meaning it was difficult to get city wide data. Others were more tricky to find and it was difficult to determine the accuracy of the data.

Accomplishments that I'm proud of

I'm proud of the fact that the end product more or less matched my initial vision of the project.

What I learned

Plan ahead, so that you are not rushing into things and panicking. On a technical level we got to learn about how API calls work.

What's next for Launchpad

Let's see. It all depends on what sort of data sets we can find.

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