Gridlock

Inspiration

GridLock was inspired by a simple problem: neighboring utilities plan future transmission projects independently, but they may not have an easy way to see where those plans come close enough to coordinate.

What it does

GridLock maps planned transmission projects from multiple utilities, identifies nearby cross-utility projects, and ranks potential coordination opportunities by distance.

How I built it

I used Python and Pandas to clean and structure public planning data, geolocated projects from available location information, used Folium for mapping, and built the final interface in Streamlit.

Challenges I ran into

The biggest challenge was route creation. Planning documents often describe projects in text without providing exact GIS routes. With limited hackathon time, I could preserve some known routes, but many projects had to rely on representative locations instead of strong route reconstruction.

Accomplishments that I'm proud of

I built an end-to-end prototype that turns messy planning data into an interactive map and ranked list of potential coordination opportunities. I also made the limitations visible instead of presenting approximate locations as exact.

What I learned

I learned a lot about infrastructure data, geolocation, and the importance of uncertainty in geographic analysis.

The project also made me rethink how I use AI. I was uncomfortable using AI to move faster when it introduced code or tools I did not fully understand. By the end, I saw a better balance: AI can accelerate development, but I still want to understand the important logic, assumptions, and decisions behind what I build.

What's next for GridLock

Next, I would improve route reconstruction using stronger GIS data, add more utilities, and improve confidence scoring.

Built With

• folium
• python