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.
Geostatistics and Interpolation in R
Do you need to create a continuous surface of data (i.e., raster) based on sample point data taken at selected locations? This course is designed for those who are interested in collecting, creating, processing, and interpolating geostatistical data. Through this course, you will learn variogram analysis and kriging methods to model and analyze spatial data based on information collected from sampled locations. 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.