GeoHackWeek Tutorial: Vector Data Processing using Python Tools

GeoHackWeek tutorial is found at https://geohackweek.github.io/vector/

The vector files at this repository (tutorial_contents) contain the Jupyter notebooks and associated data files and conda environment file used in the vector tutorial.

Conda environment and Jupyter

First make sure the miniconda or anaconda conda version is installed. See instructions below for miniconda. See below for installation instructions.

To install the conda environment used for running the Jupyter notebooks in this tutorial, change to the directory where the environment.yml file is found, downloaded from this GitHub repository (or based on a git clone).

To clone the GeoHackWeek tutorial_contents GitHub repository (which, BTW, will also install all other GeoHackWeek tutorials):

git clone https://github.com/geohackweek/tutorial_contents.git ghw_tutorial_contents

Then, at the terminal (after changing directories to ghw_tutorial_contents/vector), run:

conda env create -f environment.yml

An environment called vectorenv19 will be created. Note that this environment doesn't include JupyterLab, though it does include Jupyter notebook. It assumes you are running JupyterLab using a different conda environment where JupyterLab is installed.

Install miniconda and setup JupyterLab

Steps taken from https://geohackweek.github.io/preliminary/01-conda-tutorial/ Instructions for MacOSX and Windows are also available there.

On linux:

# Install miniconda
url=https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
wget $url -O miniconda.sh
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
conda update conda --yes

# Create a conda environment with jupyterlab
conda create -n jupyterlab -c conda-forge python=3.7 jupyterlab nb_conda_kernels nodejs

# Starting jupyter lab
source activate jupyterlab
# Then run jupyter lab
jupyter lab