A Data-Driven Quality of Life Index for Smarter Home Decisions
Livabl is an open-source platform that aggregates public urban datasets to generate a 0–100 Quality of Life score for every ward in Delhi NCR. It helps renters, homebuyers, researchers, and urban planners make informed decisions through comparable locality insights and an interactive map.
Housing decisions are often made with incomplete or biased information.
- Property listings only emphasize positives
- Short visits don't reveal long-term livability issues
- Public data on air quality, infrastructure, and civic issues is scattered across multiple portals with no unified view
As a result, people rely on price, intuition, and word-of-mouth rather than measurable quality-of-life indicators. Delhi NCR alone has 11 million+ residents making housing decisions without reliable livability data.
Livabl unifies multiple public datasets into a standardized locality score (0–100) and visualizes it on an interactive OpenStreetMap-powered dashboard.
The platform transforms complex urban data into simple, actionable insights through:
- Interactive ward-level map with color-coded livability zones
- Per-ward score breakdown for key metrics (hospital, school, pollution)
- Ranked neighborhood list with real-time filtering
- Side-by-side locality comparison in the sidebar
Dashboard running locally — see Getting Started below.
Livabl computes a 0–100 livability score with metric breakdowns currently exposed as:
| Metric | Description | Data Source |
|---|---|---|
| 🏥 Hospital Score | Healthcare accessibility proxy | OpenStreetMap + processed ward data |
| 🏫 School Score | Education accessibility proxy | OpenStreetMap + processed ward data |
| 🌫️ Pollution Score | Environmental pressure proxy | AQI/processed ward data |
The frontend shows the combined livability score and these per-ward metric components.
Raw public datasets
↓
Data ingestion & cleaning (Python)
↓
Normalize metrics → 0-100 scale
↓
Weighted aggregation per ward
↓
Quality Score generated for all 290 Delhi wards
↓
Served via FastAPI → React dashboard
- React + TypeScript — component-based UI
- Vite — fast dev server and build tool
- Leaflet.js — interactive map rendering
- OpenStreetMap — free, open-source map tiles and ward boundary data
- Python + FastAPI — REST API and data processing
- GeoJSON — ward boundary and score data format
- uv — fast Python package manager
- OpenStreetMap — ward boundaries via Overpass API
- 290 Delhi NCR wards with real livability scores
- Open environmental datasets (AQI, hospitals, schools)
Livabl/
├── frontend/ # React + TypeScript dashboard
│ ├── src/
│ │ ├── api/ # Backend API layer
│ │ │ ├── wards.ts # Real ward data loading
│ │ │ └── overpass.ts # OSM boundary parser
│ │ ├── components/ # UI components
│ │ │ ├── Header.tsx # Search + filter bar
│ │ │ ├── LiveMap.tsx # Leaflet OSM map
│ │ │ ├── Sidebar.tsx # Score cards + ward list
│ │ │ └── ScoreBar.tsx # Animated score bars
│ │ ├── types/ # TypeScript definitions
│ │ └── utils.ts # Score color helpers
│ └── public/
│ └── data/
│ └── wards_score.geojson # 290 Delhi ward scores
│
├── backend/ # Python scoring pipeline
│ ├── app/
│ │ ├── api/routes.py # FastAPI endpoints
│ │ ├── data/
│ │ │ ├── ingestion.py # GeoJSON loader
│ │ │ ├── processing.py # Ward data transformer
│ │ │ └── schemas.py # Pydantic data models
│ │ └── scoring/ # Score computation engine
| | ├── engine.py
| | └── metrics.py
│ └── data/
│ ├── raw/ # Source GeoJSON files
│ └── processed/ # Scored ward data
│
└── docs/ # Architecture and methodology
- Python 3.10+
- Node.js 18+
- npm
- uv (Python package manager)
pip install uv# Clone the repository
git clone https://github.com/WalkingDead1407/Livabl.git
cd Livablcd backend
# Install/lock dependencies from pyproject.toml + uv.lock
uv sync --dev
# Run the API server
uv run python run.pyThe API will be available at http://127.0.0.1:8000.
Quick health check:
curl http://127.0.0.1:8000/healthcd frontend
npm install
npm run devOpen http://localhost:5173 in your browser.
Create a .env file inside the frontend/ folder:
VITE_API_URL=http://127.0.0.1:8000
The frontend will use the live backend when available. If backend is down/unreachable, it automatically falls back to local GeoJSON data.
If the UI is stuck on Loading neighborhoods…:
- Confirm backend is running:
cd backend uv run python run.py - Check API health:
curl http://127.0.0.1:8000/health
- Ensure frontend
.envpoints to the same backend URL:VITE_API_URL=http://127.0.0.1:8000
Contributions are welcome!
- Fork the repo
- Create a feature branch (
git checkout -b feat/your-feature) - Commit your changes (
git commit -m "feat: add your feature") - Push and open a pull request
Please post any issues on discussions and open an issue before starting work on a feature so we can coordinate. You can leave a comment below the issue so that we can assign it to you.
MIT License — see LICENSE for details.
Livabl — Turning urban data into clear decisions.
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