A containerised platform for Geographic Data Science: gds_env
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 = {10.0},
date = {2023-04-11},
doi = {10.5281/zenodo.4642516},
}
License
The code to generate the gds_env
stacks is released under a BSD License. More details available on the repository’s license document.