This repository contains the materials for D-Lab Python Geospatial Fundamental workshop.
We recommend attending Python Fundamentals and Python Data Wrangling prior to this workshop.
Check D-Lab's Learning Pathways to figure out which of our workshops to take!
After this workshop, you will be able to:
- Recognize different forms of geospatial data and coordinate reference system (CRS).
- Use
GeoPandasandmatplotliblibraries to map and analyze spatial data. - Apply more advanced Python libraries for interactive visualization.
- Choose domain-specific spatial datasets to create your own maps.
Anaconda is a useful package management software that allows you to run Python and Jupyter notebooks very easily. Installing Anaconda is the easiest way to make sure you have all the necessary software to run the materials for this workshop. Complete the following steps:
- Download and install Anaconda. Click "Download" and then click 64-bit "Graphical Installer" for your current operating system.
- Download these workshop materials:
- Click the green "Code" button in the top right of the repository information.
- Click "Download Zip".
- Extract this file to a folder on your computer where you can easily access it (we recommend Desktop).
- Optional: if you’re familiar with git, you can instead clone this repository by opening a terminal and entering [GitCloneCommand].
If you do not have Python installed and the materials loaded on your workshop by the time it starts, we strongly recommend using the UC Berkeley Datahub to run the materials for these lessons. You can access the DataHub by clicking the following button:
The DataHub downloads this repository, along with any necessary packages, and
allows you to run the materials in an RStudio instance on UC Berkeley's servers.
No installation is necessary from your end - you only need an internet browser
and a CalNet ID to log in. By using the DataHub, you can save your work and come
back to it at any time. When you want to return to your saved work, just go
straight to the D-Lab DataHub, sign in, and
you click on the Python-Geospatial-Fundamentals-Pilot folder.
If you don't have a Berkeley CalNet ID, you can still run these lessons in the cloud, by clicking this button:
By using this button, however, you cannot save your work.
Now that you have all the required software and materials, you need to run the code:
Provide instructions on running the code, including how to load relevant software (RStudio, Jupyter Notebooks, etc.) and which file to open up. See other repositories for examples.
Additionally, provide instructions on how to run code once it’s open (running Jupyter cells, RMarkdown cells, etc.).
Check out the following resources to learn more about Geospatial programming and analysis:
D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.
Visit the D-Lab homepage to learn more about us. You can view our calendar for upcoming events, learn about how to utilize our consulting and data services, and check out upcoming workshops.
Here are other Python workshops offered by the D-Lab: