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.
This workshop is part of a series on working with and analyzing spatial agricultural data in R.
Prerequisites: GEMSx003.00 GEMSx003.01 or equivalent
- Modifiable areal unit problem
- Compute spatial autocorrelation
- Account for spatial lags in regressions
- Application: Agricultural input usage