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https://github.com/raythurman2386/gis-playground
GIS Playground is a comprehensive web-based GIS application that combines multiple data sources and provides advanced spatial data visualization and analysis capabilities. The application features real-time wildfire data integration, intelligent spatial data processing, and interactive mapping functionality.
https://github.com/raythurman2386/gis-playground
flask gdal geopandas leaflet nltk scikit-learn
Last synced: 1 day ago
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GIS Playground is a comprehensive web-based GIS application that combines multiple data sources and provides advanced spatial data visualization and analysis capabilities. The application features real-time wildfire data integration, intelligent spatial data processing, and interactive mapping functionality.
- Host: GitHub
- URL: https://github.com/raythurman2386/gis-playground
- Owner: raythurman2386
- Created: 2025-01-18T03:48:29.000Z (10 days ago)
- Default Branch: main
- Last Pushed: 2025-01-26T16:08:15.000Z (1 day ago)
- Last Synced: 2025-01-26T17:21:33.632Z (1 day ago)
- Topics: flask, gdal, geopandas, leaflet, nltk, scikit-learn
- Language: Python
- Homepage:
- Size: 1.59 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# GIS Playground
GIS Playground is a comprehensive web-based GIS application that combines multiple data sources and provides advanced spatial data visualization and analysis capabilities. The application features real-time wildfire data integration, intelligent spatial data processing, and interactive mapping functionality, making it a versatile tool for geographic data exploration and analysis.
## Features
### Data Integration
- Real-time wildfire data from ESRI's feature services
- Support for multiple spatial file formats:
- Shapefiles
- GeoJSON
- CSV (with spatial coordinates)
- GeoPackage
- Custom Layer visualization
- Multiple base map options
### Intelligent Processing
- AI-assisted layer naming and description generation
- Automated data quality analysis
- Spatial clustering for large datasets
- Smart attribute analysis and validation
### Data Management
- Automated CRS standardization
- Geometry validation and repair
- Multi-layer GeoPackage support
- Comprehensive error handling and validation
### Visualization
- Interactive map interface
- Dynamic layer toggling
- Feature property inspection
- Custom styling options
## Prerequisites
- Python 3.8+
- PostgreSQL with PostGIS extension
- GDAL/OGR libraries
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/gis-playground.git
cd gis-playground
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install required packages:
```bash
pip install -r requirements.txt
```
## Project Structure
- `app/`: Source code directory
- `database/`: Data processing and management
- `models/`: API interaction
- `routes/`: API interaction
- `static/`: Static files (CSS, JS, Images)
- `templates/`: HTML templates
- __init__.py: Package initialization
- `config/`: Configuration files
- `data/`: Raw and processed data storage
- `logs/`: Log files
- `processors/`: Data processing scripts
- File type-specific processors (Shapefile, CSV, GeoJSON, GeoPackage)
- `tools/`: Geoprocessing tools
- `validation/`: Geometry and attribute validation
- `conversion/`: CRS and format conversion
- `analysis/`: Spatial analysis tools
- `ai/`: AI-assisted processing tools
- `utils/`: Utility functions
- `requirements.txt`: List of dependencies
- `README.md`: Project documentation
- `manage.py`: Database management commands
- `run.py`: Main Flask application
## Key Components
### Data Processors
- Shapefile Processor: Handles ESRI Shapefile processing
- CSV Processor: Processes CSV files with spatial coordinates
- GeoJSON Processor: Manages GeoJSON file processing
- GeoPackage Processor: Handles multi-layer GeoPackage files
### Tools
- Geometry Validation: Ensures spatial data integrity
- CRS Conversion: Standardizes coordinate reference systems
- Smart Processing: AI-assisted data analysis and naming
## Usage
1. Start the Flask development server:
```bash
python run.py
```
2. Open your web browser and navigate to `http://localhost:5000`
3. Upload spatial data through the interface:
- Supports multiple file formats
- Automatic processing and validation
- AI-assisted naming and description
- Quality analysis reporting
## Future Enhancements
- Census data integration
- Advanced spatial analysis tools
- Custom styling interface
- Batch processing capabilities
- Enhanced AI features
## Acknowledgments
- ESRI for real-time wildfire data
- OpenStreetMap contributors
- Leaflet.js community
- GDAL/OGR project
## Additional Resources
- [Leaflet Documentation](https://leafletjs.com/reference.html)
- [PostGIS Documentation](https://postgis.net/documentation/)
- [GDAL/OGR Documentation](https://gdal.org/api/index.html)
- [Leaflet Providers](https://leaflet-extras.github.io/leaflet-providers/preview/)