MEGA-Equigrid

🏙️ Urban Equity Analyzer

🌍 Inspiration

We were inspired by a simple but powerful question:

Do all communities have equal access to essential services like hospitals?

In many cities, access to healthcare and public services is unevenly distributed. We wanted to build a project that could use data and algorithms to measure accessibility in a clear, quantitative way.

The idea was to combine urban data with algorithmic thinking to evaluate how fairly resources are distributed across a city.

🧠 What We Learned

This was our first-ever hackathon — and our first real coding collaboration as a team.

We learned:

How to divide tasks effectively under time pressure
How to structure code so multiple people can work on it
How to simplify problems when performance becomes a bottleneck
That clean, efficient algorithms matter more than “cool” algorithms

Most importantly, we learned that building something together is very different from coding alone.

🏗️ How We Built It

Our goal was to measure how accessible hospitals are from different points in a city.

Initially, we modeled the city as a graph:

Nodes represented locations
Edges represented possible paths
We attempted to compute the shortest path from every node to nearby hospitals using Dijkstra’s algorithm

Formally, for each node $v$, we attempted to compute:

$$ \min_{h \in H} ; d(v, h) $$

Where:

$H$ is the set of hospitals
$d(v, h)$ is the shortest path distance

However, this approach had significant performance issues.

🚧 Time Complexity Challenge

Running Dijkstra’s algorithm for many nodes was computationally expensive:

$$ O((V + E) \log V) $$

When repeated across many starting points, execution time exceeded 20 minutes, which was not practical within a hackathon setting.

To optimize, we switched to using Euclidean distance as an approximation:

$$ d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2} $$

This dramatically reduced runtime while still providing a meaningful measure of accessibility.

⚙️ Technical Stack

Python for data processing
Graph modeling for spatial representation
Mathematical distance calculations
Data visualization to communicate results

We structured the project into:

Data preprocessing
Distance computation
Accessibility scoring
Output visualization

🎯 Challenges We Faced

1️⃣ Algorithmic Performance

Our original path-based solution was too slow. Learning to balance theoretical correctness with practical efficiency was a major lesson.

2️⃣ First-Time Collaboration

This was our first hackathon and our first time building something collaboratively under pressure. Coordinating code, debugging together, and merging ideas was both challenging and fun.