How it fits in the criteria
- Social Good: Our software is free to use and provides valuable information that individuals and small businesses can use.
- Lifestyle: We help tenants, and other individuals see if the place they plan to move to is a good fit for them.
How we built it
- We built a FastAPI backend that aggregates data from multiple external providers into a unified API optimized for our frontend experience.
- On the frontend, we used Mapbox GL JS to render an interactive globe and map interface on the web, enabling real-time geographic visualization of data.
- React Query handled data fetching, caching, and synchronization between services, allowing the UI to stay responsive and performant.
Challenges we ran into
- Identifying reliable data sources that provided relevant and consistent geographic data
- Normalizing different APIs and data formats into a single backend schema
- Learning and integrating unfamiliar tools such as Mapbox GL JS and React Query under time constraints
- Balancing performance with rendering large amounts of map data in real time
Accomplishments that we're proud of
- Designing and shipping a clean, intuitive geospatial UI powered by a 3D globe and interactive map
- Successfully aggregating and unifying multiple external datasets into one coherent experience
- Building a full end-to-end system (data ingestion → backend aggregation → interactive visualization) within hackathon time limits
- Delivering a polished product that turns complex data into something visually understandable and actionable
What we learned
- How to design a clean backend aggregation layer that abstracts away inconsistencies across multiple third-party APIs
- The importance of normalizing and validating data early to simplify frontend rendering and avoid downstream bugs
- How to work effectively with geospatial visualization tools like Mapbox GL JS, including performance considerations for large datasets
- Practical tradeoffs between speed of development and architectural cleanliness in a hackathon setting
- How to leverage AI coding assistants strategically — using them to accelerate scaffolding and debugging while still making key design decisions ourselves
- That UI clarity matters just as much as data depth — powerful data is useless if users can’t quickly understand it
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