Welcome to GEOG 441, Advanced Geospatial Methods
This course is designed to allow students with varying degrees of GIS and remote sensing experience to increase their knowledge. The class is mainly focussed on Python, R and other open source geospatial tools. Students are free to choose the exercises that are most relevant to their goals.
That said there are a few skills and tools that students need to grasp by the end of the class.
- Use of GIT for project management and code sharing
- Basic Understanding of GDAL
- Ability to use either Python or R to perform geospatial tasks
| Component | Fraction of grade |
|---|---|
| Exercises | 0.3 |
| Independent project | 0.3 |
| Group Project | 0.3 |
| Participation (coming to class, being engaged) | 0.1 |
There are two exercises that are required, one introducing GIT, and another introducing GDAL. Students may choose other exercises based on their interests or project needs. We will be using GIT to manage projects in this class, and GIT is a skill that many employers are interested in. GIT is also a great way to manage and share your own projects if you are a researcher.
I am forcing you to learn a little about GDAL for a couple of reasons. The first is historical/contextual. Most other geospatial applications rely, at least partially, on GDAL. It has been in use since 2000. Secondly, it is extremely useful. It is great for writing scripts for processing files in batch. If you ever want to process a bunch of files in a container on the cloud it is a good choice because it does not have complicated dependencies.
The rest is up to you as far as exercises go. You do however need to make substantial gains in you ability to use either R or Python (or both!) to solve geospatial problems. Ideally, by the end of the quarter you will be familiar with the content of the suggested tutorials for your chosen language and have completed at least on of the synthesizing exercises. If you are working primarily with your programming language for your personal project, or group project you can skip the synthesizing exercise if you would like.
At the end of the quarter you will submit a brief self-evaluation, with a summary of the exercises you have completed and a paragraph or two describing the things you have learned. You will give yourself a grade for the exercises portion on the class, and explain why you deserve that grade.
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GIT exercise (required, complete by 2025-04-09)
- Create a github (or similar) account (if you don't have one)
- Read and experiment with the steps in this Github tutorial
- Clone this github repo
- Create your own repo for your individual project
- With your group, create a repo for your group project
- Before you start working on group project, mess around with commits, branching and merging to get some practice.
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GDAL tutorials (required)
- In the Spatial Thoughts GDAL tutorial do sections 1.1–1.4 and 2.1–2.3
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Python tutorials
- Pandas:
- GeoPandas: +
- rioXarray: +
- Synthesizing exercises: +
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R tutorials
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QGIS tutorials
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GRASS tutorials
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Other stuff you want to learn tutorials
| Week | Date | Lecture | Preparation |
|---|---|---|---|
| 1 | 2025-04-02 | Introduction
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| 2 | 2025-04-07 | Group Project Introductions
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| 2 | 2025-04-09 | Python and R:
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Complete the GIT exercise before class |
| 3 | 2025-04-14 | Working with Vectors in Python | |
| 3 | 2025-04-16 | Working with Vectors in R
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| 4 | 2025-04-21 | Working with Rasters in Python
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| 4 | 2025-04-23 | Working with Rasters in R Or Drone flyin' |
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| 5 | 2025-04-28 | Working with Rasters in GRASS
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| 5 | 2025-04-30 | Lidar
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| 6 | 2025-05-05 | Catch up day | |
| 6 | 2025-05-07 | Lidar continued
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| 7 | 2025-05-12 | Group Project Check-In | |
| 7 | 2025-05-14 | Individual Project Check-In | |
| 8 | 2025-05-19 | Leaflet, quarto, reveal | |
| 8 | 2025-05-21 | Subject By Popular Demand | |
| 9 | 2025-05-26 | Memorial Day | |
| 9 | 2025-05-27 | Subject By Popular Demand (A Tuesday following Monday schedule) | |
| 9 | 2025-05-28 | Subject By Popular Demand | |
| 10 | 2025-06-02 | Individual Presentations | |
| 10 | 2025-06-04 | Group Presentations |
Lecture Slides found here.
Difficulty Ratings:
Easy
Hard
Project Ideas:
- Get a part 107 Commercial UAV operators license. Study for the test, then take the exam (costs $175).
- Here are some ideas in the realm of urban planning. You would need to go above and beyond just following the steps presented in this article, but they could be a good starting point. This article is Python focused, but you could redo them using other tolls if desired.
- Do something cool with Carbon Mapper data
- Train an ML model to identify abandoned cannabis grow operations in the Klamath Mountains.
- Build an application or module/library for accessing the ESA Climate Data API