Spatial Data Analysis in R

GEMS Learning provides modular non-credit digital and data science education for working professionals and students in food, agriculture, and natural resource application areas. Across the curriculum, instructors have built their course content from their own work executing large-scale data science projects to solve agricultural problems. 

Series: Accounting for Location in Agriculture in R

Would you like to leverage spatial data to start exploring the relationships of agricultural processes across geographies? Is accounting for spatial dependency in your analyses critical to your work? Or do you need to create a continuous surface of data (i.e., raster) based on a sample point date taken at selected locations? Learn how to work with spatial data in R, starting from importing different spatial datasets and creating simple maps, to conducting basic geocomputation on vector and raster data. Each module includes the opportunity to practice your new skills via hands-on exercises focused on agri-food applications.

An Introduction to Spatial Data Analysis in R

Would you like to leverage spatial data to start exploring the relationships of agricultural processes across geographies? This course is designed for those who are interested in explicitly accounting for location in their analyses. Through this 3-week introductory course, you will learn how to work with spatial data in R, starting from importing different spatial datasets and creating simple maps, to conducting basic geocomputation on vector and raster data. In each 2.5 hour lecture, you will have the opportunity to immediately practice your new skills via hands-on exercises focused on agri-food applications.

Week 1: Introduction to spatial data and mapping in R
Week 2: Basic geocomputation with vector data in R
Week 3: Basic geocomputation with raster data in R 

The course will be delivered via a Jupyter Notebook hosted on the GEMS Informatics Platform. You do not need to have R or RStudio installed on your machine to participate.

Fees

Fee: $525 (no charge for U of M affiliated)

This is a three-part course that will take place at the same times on dates between February 9 and March 1

The other courses in the Accounting for Location in Agriculture in R series are:

• Spatial Regression in R
• Geostatistics and Interpolation in R
• Scholarships are available (see the link on the registration page). See the full line-up of courses and register.

Date, time and location:

• Feb. 9, 2024 to Mar. 1, 2024
• 10:00am to 12:30pm
• Online

Click here to Register