A Python library for generating SVG maps from ESRI shapefiles.
Kartograph.py is a Python library that generates SVG maps from ESRI shapefiles. It allows developers to programmatically create beautiful, scalable vector maps for data visualization and geographic applications. The tool converts geographic data into SVG format, making it suitable for web and print use.
Developers and data visualization specialists working with geographic data who need to create custom SVG maps programmatically. It's particularly useful for those in journalism, research, or web development requiring high-quality map visualizations.
Kartograph.py provides a straightforward Python-based solution for converting shapefiles to SVG maps, offering programmatic control over map creation without requiring complex GIS software. Its focus on clean SVG output makes it ideal for web applications and publications needing scalable vector maps.
UNMAINTAINED! Renders beautiful SVG maps in Python.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Directly converts ESRI shapefiles to scalable vector graphics, enabling high-quality, resolution-independent maps for web and print applications, as per the core functionality described.
Provides a programmatic interface for map creation, allowing developers to customize and generate maps through Python code, ideal for automation in data visualization workflows.
Enables styling and configuration of map elements via code, offering design flexibility without external tools, as highlighted in the key features for tailored outputs.
Licensed under AGPL and supported by organizations like dpa-newslab and Open Knowledge Foundation, ensuring transparency and community access, as noted in the README.
The README explicitly states the project is no longer maintained, meaning bugs are not fixed and no new features are added, making it unreliable for production use.
Admitted to be still in beta with unresolved bugs, leading to potential instability and unexpected issues during map generation, as mentioned in the current status.
Requires installation via virtualenv and setup.py, which adds overhead compared to modern package managers, potentially hindering quick adoption and compatibility.