Geospatial Data Visualization & Prediction Web App

📌 Project Overview

This interactive web application allows users to upload geospatial data, visualize it on a web map, and run machine learning-based spatial analysis (e.g., land suitability, flood risk, or energy consumption prediction).

💡 Features

• Upload and visualize geospatial data (GeoJSON, CSV, shapefiles)
• Perform basic spatial analysis (buffer, clip, intersection)
• Apply ML models for spatial pattern predictions (e.g., land suitability, flood risk)
• Display ML results as interactive maps
• Fully deployed web app with cloud hosting

🛠️ Tech Stack

• Frontend: React (for UI)
• Backend: Django (API & ML processing)
• Database: PostgreSQL/PostGIS (store spatial data)
• ML Libraries: Scikit-learn, TensorFlow/PyTorch, GeoPandas, Rasterio
• GIS Tools: Leaflet, GDAL, Shapely
• Containerization: Docker
• Deployment: GeoNode for spatial data hosting + Vercel for deployment

🚀 Installation & Setup

Prerequisites

• Node.js & npm
• Python & pip
• PostgreSQL with PostGIS extension
• Docker (optional, for containerization)

Backend Setup

1. Clone the repository:

   git clone https://github.com/Issimonmutunga/geospatial-web-app.git
   cd geospatial-web-app

2. Set up a virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate

3. Install dependencies:

   pip install -r backend/requirements.txt

4. Set up the database:

   createdb geospatial_db
   psql -d geospatial_db -c "CREATE EXTENSION postgis;"

5. Run migrations:

   python manage.py migrate

6. Start the backend server:

   python manage.py runserver

Frontend Setup

1. Navigate to the frontend folder:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm start

📅 Development Plan

1.Setup & GIS Backend

• Research & project planning
• Set up PostgreSQL/PostGIS and sample datasets
• Build Django API for spatial data operations
• Implement CRUD endpoints and refine schema

2.Frontend & Map Integration

• Set up React and integrate Leaflet for map visualization
• Implement file upload feature
• Display uploaded data interactively
• Add spatial querying and filtering

3: Machine Learning Integration

• Identify ML use case (e.g., suitability analysis, energy prediction)
• Train a basic ML model with Scikit-learn
• Integrate ML model with backend API
• Visualize ML predictions on interactive maps

4: Optimization & Deployment

• Optimize backend & ML performance
• Containerize with Docker
• Deploy on Heroku/DigitalOcean with GeoNode
• Add SEO & analytics for better visibility
• Publish project blog on portfolio

📜 License

📞 Contact

For any inquiries, reach out via email at besimonmutunga@gmail.com.