The globe is now digital. Everything from monitoring deforestation, predicting wildfires, to training autonomous vehicles and tracking uprisings on social media requires you to understand how to leverage location data. This class will introduce you to the methods required for spatial programming. We focus on building your core programming techniques while helping you: leverage spatial data from OSM and the US Census, use satellite imagery, track land-use change, and track social distance during a pandemic, amongst others. We will leverage open source Python packages such as GeoPandas, Rasterio, Sklearn, and Geowombat to better understand our world and help predict its future. Some Python programming experience is required, however the material will be presented in a student-friendly manner and will focus on real-world application.
Under Development - Please Contribute See the current book here
If you'd like to develop on and build the PyGIS - Open Source Spatial Programming & Remote Sensing book, you should:
- Clone this repository and run
- Run
pip install -r requirements.txt(it is recommended you do this within a virtual environment) - Navigate to
./pyGIS/ - (Recommended) Remove the existing
/pyGIS/pygis/_build/directory - Run
jupyter-book build pygis
A fully-rendered HTML version of the book will be built in /pyGIS/pygis/_build/html/index.html .
We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.
This project is created using the excellent open source Jupyter Book project and the executablebooks/cookiecutter-jupyter-book template.
Michael Mann, Steven Chao, Christophe Van Neste, Roger Lew, & Mark Isken. (2023). mmann1123/pyGIS: Clearer Satellites (v1.2.1). Zenodo. https://doi.org/10.5281/zenodo.8215141
