FlockFindr

SLC Tim Hortons
RCH
Graph

Inspiration

As Waterloo students, it feels as though anytime that we want to go get a coffee or donut at Tims, there’s an enormous line that we failed to avoid. Flipping the narrative, we couldn’t imagine how difficult the rush hours were for Tim Horton’s itself.

What it does

FlockerFindr uses Bluetooth to retrieve the estimated real-time location of everyone in a set area. It then sends the information to a database which is processed by our website to display the estimated number and location of all the people in locations in the proximity of our Bluetooth devices.

How we built it

The main map interface in the web-app was built using the MappedIn React SDK, think Google Maps but indoors. Our frontend UI was built in React.js and Next.js, and our backend was made of a Python Bluetooth detection script connected to a Supabase dataframe.

What we’re proud of

3D Geese! 🪿 🪿 🪿
Integrating 3D UI aspects into our website
Real-time data collection and visualization

Challenges

The MappedIn React SDK is not fully developed as of yet, so we had to often keep multiple other documentations open and at times guess the properties we were allowed to call.

What’s next

With more time and investment, more features would be added to the program. Things such as developing a more powerful, customized Bluetooth device, device authorization, and integrating AI for data analysis and prediction of future trends. With such a versatile product like FlockFindr, the possibilities are endless!

Made with love by UW students. 🪿💛🖤