Inspiration
The QRT challenge combined data engineering, software development, and algorithm design — a perfect mix that deeply excited our team. We wanted to tackle a problem that blended theory with practical impact.
What It Does
MeetSpot determines the most optimal location to host a company-wide event for QRT’s global offices.
It considers multiple factors such as:
- Number of attendees traveling from each office
- CO₂ emissions
- Total travel time
Using these, it finds a balanced and sustainable meeting point.
How We Built It
- Implemented the A* algorithm with a custom heuristic using NetworkX in Python to model global air travel as a graph
- Deployed the backend on an AWS EC2 instance for scalability and reliability
- Built a clean, interactive React frontend for user input and visualization
- Integrated airport IATA codes to map real-world data to the graph network
Challenges We Ran Into
- Handling cities with duplicate names (e.g., Paris, France vs. Paris, Texas)
- Resolving CORS issues during API communication
- Designing an algorithm focused on convergence to an optimal location rather than traditional pathfinding
Accomplishments We’re Proud Of
- Developed a custom heuristic that balances emissions, distance, and time
- Created an intuitive, responsive web interface
- Built a fully working end-to-end system that converges to an appropriate location
What We Learned
- In-depth understanding of graph theory and NetworkX
- Experience deploying applications with AWS EC2
- Improved frontend design and UI/UX principles using React
What’s Next for MeetSpot
We plan to extend MeetSpot by factoring in:
- Flight prices
- Political stability of host countries
- Visa accessibility for attendees
These additions will make the recommendation engine even more realistic and globally applicable.
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