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A containerised platform for Geographic Data Science
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README.md

gds_env: A containerised platform for Geographic Data Science

This repository contains a docker container that includes:

  • A full Python stack ready for geospatial analysis (see gds_stack.yml for a detailed list).
  • A full R stack ready for geospatial analysis (see install.R for a detailed list).
  • Both the IRkernel and rpy2 channels to interact with R through Python.
  • A full LaTeX distribution.
  • Additional development utilities (e.g. pandoc, git, decktape, etc.).

It is rather heavy (around 10GB) but it is meant to provide a fully isolated environment that can be deployed in a wide array of contexts and encompass several situations.

Requirements

You will need Docker to be able to install the GDS environment.

Installing

You can install this container by simply running:

docker pull darribas/gds:3.0

[Note that you'll need Docker installed on your machine]

Building

If, instead, you want to build from source, the Docker image can be built by running:

docker build -t darribas/gds:3.0 .

You can check it has been built correctly by:

docker image ls

And you should see one image with the name gds.

Running

The container can be run as:

> docker run --rm -ti -p 8888:8888 -v ${pwd}:/home/jovyan/host darribas/gds:3.0

A couple of notes on the command above:

  • This opens the 8888 port of the container, so to access the Lab instance, you will have to point your browser to localhost:8888 and insert the token printed on the terminal
  • The command also mounts the current folder (pwd) to the container, but you can replace that with the path to any folder on your local machine (in fact, that will only work on host machines with the pwd command installed)

Citing

DOI

@software{gds_env,
  author = {{Dani Arribas-Bel}},
  title = {\texttt{gds\_env}: A containerised platform for Geographic Data Science},
  url = {https://github.com/darribas/gds_env},
  version = {3.0},
  date = {2019-08-06},
}

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