UrbanVitals

🎖️ Honorable Mentions at HackMIT 2025 (Tandemn: Best Use of AI) & 🏆 Best Student Hack at StarHacks 2025. UrbanVitals is an AI-powered sustainability platform that evaluates neighborhoods using comprehensive environmental, infrastructure, and livability metrics.

Everyone talks about building greener cities, but if you actually ask how sustainable is my neighborhood, there is rarely a straight answer. The data exists, scattered across air-quality feeds, energy grids, transit maps, and government reports, yet it is far too fragmented for a regular person, or even a city planner, to make sense of. UrbanVitals pulls all of that together into a single, honest number. It is an AI-powered sustainability platform that scores the livability and environmental health of urban neighborhoods, and it picked up two awards along the way: an Honorable Mention for Best Use of AI (Tandemn) at HackMIT 2025 and Best Student Hack at StarHacks 2025. ### One number for a neighborhood's health At the center of UrbanVitals is the Green Score, a rating from 1 to 10 built from more than 13 indicators spanning the environmental, infrastructural, and social sides of a place: air and water quality, renewable energy use, grid reliability, transit access, walkability, and more. Under the hood, a weighted model groups those signals into categories like Environmental Quality, Infrastructure, Livability, Sustainability, and Disaster Risk, then blends them into one figure you can compare across neighborhoods at a glance. ### A 3D map you can actually read The frontend, built with React and Mapbox GL, turns all of that into an interactive 3D map. Neighborhoods are color-coded by their Green Score, and you can toggle layers to surface specific concerns like greenery density, pollution hotspots, or disaster vulnerability. Smooth transitions and hover details make the whole thing feel exploratory rather than clinical, so someone with no technical background can dig into their own street as easily as an urban planner can. ### Grounded in real data, explained in plain language A score is only as trustworthy as what feeds it. On the backend, a FastAPI and Python service, accelerated by the Cerebras SDK for the heavy AI work, runs a pipeline that consolidates open data from OpenStreetMap, Open-Meteo, and government environmental APIs, so every Green Score is grounded in verifiable, real-world data rather than guesswork. To explain the results, the AI uses Retrieval-Augmented Generation with LangChain, pulling in relevant sustainability context before it answers, which keeps its explanations accurate and traceable. A built-in conversational assistant lets you ask why a neighborhood scored the way it did, compare places, and get concrete improvement ideas, and every score stays explainable: a low mark might trace straight back to poor recycling rates or an unreliable power grid, shown right there in the dashboard. ### AI that practices what it preaches The part I am proudest of is that UrbanVitals does not just measure sustainability, it lives it. The AI runs on Tandemn, a sustainable computing platform that uses refurbished hardware powered by renewable energy, which saves roughly 1.7 mg of CO2 per token the model generates. Every query in the app tracks and displays its own real-time carbon savings, so the tool is honest about its own footprint even while it analyzes everyone else's. ### Built for a city, designed to scale For the hackathon, the team, Divyam Kataria, Vivien Lim, and I, built the MVP around Tempe, Arizona, aggregating live environmental and infrastructure data and scoring it in real time. The architecture is deliberately modular: it supports Modal cloud deployment so large citywide analyses can run in parallel, with 60fps Mapbox rendering and Redis caching keeping it fast even across thousands of data points. From here, the vision reaches toward multi-city and international coverage, predictive climate modeling, urban-planning simulations, and policy recommendation tools, turning UrbanVitals from a scorecard into a genuine decision-making aid for building better cities.

At a glance

Duration: 48 hours
Type: Hackathon Project
Event: StarHacks 2025 & HackMIT 2025
Award: Best Student Hack & Honorable Mentions - Tandemn: Best Use of AI
Other Contributors: Divyam Kataria, Vivien Lim

Technologies

React.js
Vite
FastAPI
Python
Mapbox GL JS
Cerebras SDK
Tandemn AI
LangChain
Retrieval-Augmented Generation (RAG)
OpenStreetMap & Open-Meteo APIs
Modal Cloud
Redis (Caching)
Data Visualization (3D Mapping)

Photos

Demoing UrbanVitals with the team, the neighborhood map live on the laptops
With the team and Ali Dasdan, CTO of Dropbox, at HackMIT 2025
In front of MIT's Great Dome at HackMIT

Links

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