This is a set of Jupyter Notebooks for workshop at NC GIS 2025 conference.
Presenters: Vaclav (Vashek) Petras, Corey White
The workshop can be run online in MyBinder and Google Colab. This is a convenient way, since no installation is needed.
To run the notebook in MyBinder, click the launch button above.
The software and dataset is downloaded and installed into the environment. Other notebooks and files can be accessed through the JupyterLab interface on the left.
To run the notebook in Google Colab, click the open button above.
In Google Colab, we don't have pre-installed the software needed, so go ahead and execute the first cell to install it. Also, in order to avoid the need to repeat the installation, we merged all notebooks into one long notebook.
Please, don't do a local setup for this short workshop so that we can focus on analysis. However, if you want to explore local setup, it might be advantageous to explore the notebooks for Windows and macOS here. For Linux, the setup will be relatively straightforward even if you have to use a virtual environment. Having GRASS on a non-standard path (which is not on system path), will require:
import os
os.environ["PATH"] += ":/path/to/directory/where/grass/executable/is/"This hands-on NCGIS 2025 pre-conference workshop introduces participants to GRASS as a powerful geospatial processing engine integrated with Python for advanced data science workflows.
Using online Jupyter notebooks, participants will explore how GRASS tools can handle large datasets, automate spatial analysis using Python, and perform complex computations.
Participants will work through examples of raster and vector processing, spatial statistics, and image classification. No previous experience with GRASS is required, but either basic Python or advanced geospatial experience is recommended.
Duration: 4 hours
Vaclav Petras, NC State University, Center for Geospatial Analytics
Corey White, NC State University, Center for Geospatial Analytics
This material is dual licensed under GNU FDL 1.3 and CC BY-SA 4.0.
This workshop was developed and delivered with the support of the U.S. National Science Foundation, award 2303651.
