Location & Details
This course is presented by GEMS Learning.
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.
Spatial Regression in R
Is accounting for spatial dependency in your analyses critical to your work? This course is designed for those who want to learn spatial regression techniques and model spatial dependency explicitly. Through this course, you will learn about spatial dependency and spatial autocorrelation, the construction of spatial weight matrices, testing for spatial association and correlation and building generalized spatial regression models. You will have the opportunity to immediately practice your new skills via hands-on exercises focused on agri-food applications throughout the 2.5-hour workshop.
Fee: $175 (no charge for U of M affiliated)
The other courses in the Accounting for Location in Agriculture in R series are:
Scholarships are available (see the link on the registration page). See the full line-up of courses and register.