GIS Programming Project 🗺️

A comprehensive geospatial analysis project structure for learning and implementing GIS programming with Python.

🚀 Project Overview

This project provides a professional structure for geospatial data analysis using Python, focusing on:

• Vector Data Analysis (Shapefiles, GeoJSON, etc.)
• Raster Data Processing (TIFF, Imagery, etc.)
• Spatial Analytics and Visualization
• Organized Workflow for reproducible geospatial research

📁 Project Structure

GisProgramming/
├── 📂 data/                    # Data storage (not tracked in Git)
│   ├── raw/                    # Original data files
│   │   ├── vector/            # Shapefiles, GeoJSON, etc.
│   │   └── raster/            # TIFF, imagery, etc.
│   ├── processed/             # Cleaned/processed data
│   └── external/              # External datasets
├── 📓 notebooks/              # Jupyter notebooks
│   ├── exploratory/           # Data exploration
│   ├── analysis/              # Main analysis notebooks
│   └── visualization/         # Visualization notebooks  
├── 📦 src/                    # Source code modules
│   ├── config.py             # Project configuration
│   ├── data_processing/       # Data utilities
│   ├── analysis/             # Analysis functions
│   └── visualization/        # Plotting utilities
├── 📊 output/                 # Results (sample outputs tracked)
│   ├── figures/              # Charts and maps
│   ├── tables/               # Analysis results
│   └── processed_data/       # Final datasets
├── 📚 docs/                   # Documentation
└── 🧪 tests/                  # Unit tests

🛠️ Setup Instructions

1. Clone the Repository

git clone https://github.com/YOUR_USERNAME/GisProgramming.git
cd GisProgramming

2. Install Dependencies

# Create virtual environment (recommended)
python -m venv gis_env
source gis_env/bin/activate  # On Windows: gis_env\Scripts\activate

# Install required packages
pip install -r requirements.txt

3. Verify Installation

python -c "import geopandas; print('✅ GeoPandas installed successfully!')"

📊 Quick Start

Load and Analyze Vector Data

# Import project modules
import sys
sys.path.append('src')

from config import Config
from data_processing.vector_utils import VectorDataProcessor

# Initialize
config = Config()
processor = VectorDataProcessor()

# Load your data (place files in data/raw/vector/)
gdf = processor.load_vector_data("your_file.shp")

# Basic analysis
print(f"Loaded {len(gdf)} features")
print(f"CRS: {gdf.crs}")

Run Analysis Notebooks

jupyter notebook notebooks/analysis/vector_data_analysis.ipynb

🔧 Core Dependencies

• GeoPandas - Vector data processing
• Rasterio - Raster data handling
• Shapely - Geometric operations
• Matplotlib/Seaborn - Visualization
• Pandas/NumPy - Data analysis
• Jupyter - Interactive analysis

📖 Learning Path

1. Start with: notebooks/analysis/vector_data_analysis.ipynb
2. Explore: Basic geospatial operations and visualizations
3. Advance to: Spatial analysis and custom workflows
4. Build: Your own geospatial analysis projects

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-analysis)
3. Commit your changes (git commit -m 'Add amazing analysis')
4. Push to branch (git push origin feature/amazing-analysis)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built for learning Geospatial Analytics with Python
• Structured for Supply Chain and Marketing Analytics integration
• Designed for Power BI and Excel workflow compatibility

---

Happy Mapping! 🗺️📊✨