Geospatial Vector Data Processing in Python

This repository contains an introduction to geospatial vector data processing in Python. This is part of the course on Advanced Geospatial Analytics with Python taught since Fall 2023 at Clark University.

Requirements

You need to have pixi installed on your machine. Pixi manages the Python environment and all the dependencies for this tutorial (pinned in pixi.toml and locked in pixi.lock).

To install pixi on macOS, Linux or Windows Subsystem for Linux (WSL):

curl -fsSL https://pixi.sh/install.sh | sh

On Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm -useb https://pixi.sh/install.ps1 | iex"

Restart your terminal after the installation, and check that it works by running pixi --version. See the pixi installation docs for other options.

Instructions

Clone this repository to your local machine:

git clone git@github.com:HamedAlemo/vector-data-tutorial.git

Change your directory to the cloned repository:

cd vector-data-tutorial

Install the environment (this only needs to be done once; it creates a local .pixi folder inside the repository):

pixi install

Launch Jupyter Lab:

pixi run lab

• Jupyter Lab opens in your browser (if it doesn't, copy the URL printed in the terminal and paste it in your browser).
• Open vector_analysis.ipynb, dask_geopandas_intro.ipynb, and scalable_vector_analysis.ipynb and follow the instructions.
• The Dask dashboard is available at http://localhost:8787/status once you start a Dask cluster in scalable_vector_analysis.ipynb.

Alternatively, you can activate the environment in your terminal with pixi shell and run any command (e.g. jupyter lab or python) from there.

Using VS Code

If you prefer to run the notebooks in VS Code, run pixi install first, then open a notebook and select the kernel from .pixi/envs/default/bin/python (Windows: .pixi\envs\default\python.exe).