Is accounting for spatial dependency in your analyses critical to your work? This course is designed for those who want to learn spatial regression and spatial interpolation techniques and model spatial dependency explicitly. Through this course, you will learn about  spatial dependency and spatial autocorrelation. The regression portion covers the construction of spatial weight matrices, testing for spatial association and correlation, and building generalized spatial regression models. The interpolation section covers variogram analysis and kriging methods. 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 course.

Prerequisites: GEMSx008.1


Week 1 : Spatial regression in Python
  • Modifiable areal unit problem (MAUP)
  • Spatial autocorrelation 
  • Spatial contiguity, distance, and weighting
  • Methods for determining spatial autocorrelation 
  • Spatial regression
Week 2: Spatial interpolation in Python
  • Spatial autocorrelation
  • Global & local, exact & approximate interpolation
  • Deterministic & stochastic processes and methods
  • Delaunay Triangulation & Voronoi Polygons
  • Inverse Distance Weighting
  • Kriging 
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