This repository is a comprehensive toolkit for spatial economic analysis. It evolved from standalone Python visualization scripts into a full-stack Geospatial Economic Forecasting Dashboard.
The project is divided into two main components:
- Geospatial Economic Forecasting Dashboard (Django App):
- A web-based interface for state and district-level forecasting.
- Features interactive Leaflet maps, SQLite persistence, and plain-language economic interpretations.
- Allows manual entry of NDVI, Nightlight intensity, and Capital Formation proxies.
- Standalone Geospatial Visualizations (Python/Altair):
altair indian states.py: Scripts to generate high-fidelity, interactive choropleth maps.- Pre-rendered reports:
altair_bar_chart.htmlandaltair_scatter_plot.html.
Navigate to the application folder and initialize the Django server:
cd geospatial-economic-forecasting
python manage.py migrate
python manage.py runserver 127.0.0.1:8002
View the detailed Application README for usage instructions.
To run the analysis scripts directly:
python "altair indian states.py"
The forecasting logic (found in tasks.py) utilizes three primary pillars of regional economic health:
| Variable | Description | Proxy For |
|---|---|---|
| NDVI | Normalized Difference Vegetation Index | Agricultural health and ecological productivity. |
| Nightlight | Satellite-derived nighttime radiance | Urbanization, electrification, and industrial activity. |
| Capital Formation | Gross Fixed Capital Formation (GFCF) | Long-term investment and infrastructure growth. |
Note: The current forecasting model uses a weighted linear formula as a prototype methodology. It is designed to be replaced with trained Machine Learning models in future iterations.
- Web Framework: Django (Python)
- Frontend Maps: Leaflet.js (Dashboard) & Altair/Vega-Lite (Static)
- Geospatial Data: GeoJSON (India State/District boundaries)
- Data Handling: Pandas, Numpy
This project is licensed under the MIT License - see the LICENSE file for details.