A containerised platform for Geographic Data Science: gds_env

• Dani Arribas-Bel [@darribas]

The gds_env (short for "GDS environment") provides a modern platform for Geographic Data Science. The project is a Jupyter-based stack that includes state-of-the-art geospatial libraries for Python and R. The gds_env is based on container technology to make it a transferrable platform for reproducibility. The source code is released under an open source license and the build process is transparent.

The gds_env extends the official Jupyter Docker Stack to include geospatial functionality in both Python and R. To offer more flexibility, this extension is provided in three different flavours, or stacks (to ): gds_py, gds and gds_dev. Each of them builds on each other and adds further functionality. Please check the Stacks section for more information.

The goal of the gds_env is to make using Python and R for geospatial easy to set up in a large variety of contexts. The gds_env can support research and teaching activities, but is also suitable for data scientists using Python and R "in the field". The stacks can be used in a range of environments, including: Windows/Mac/Linux laptops and desktops, servers, compute clusters, supercomputers or in the cloud (e.g. you can deploy them on Binder). For more information on how to build or install any of the stacks, check the Guides section.

Building blocks

The gds_env stands on the shoulders of giants. Here are the core open technologies it is built with:

Community

The gds_env is an open-source project. To join the conversation, please read through its community guidelines.

Citation

@software{gds_env,
  author = { Dani Arribas-Bel },
  title = {\texttt{gds\_env}: A containerised platform for Geographic Data Science},
  url = {https://darribas.org/gds_env},
  version = {6.0},
  date = {2019-08-06},
}

License

The code to generate the gds_env stacks is released under a BSD License. More details available on the repository's license document.