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Description

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. 

Prerequisites: GEMSx008.1

Objectives

Topic 1 : Spatial regression in Python
  • Modifiable areal unit problem (MAUP)
  • Spatial autocorrelation 
  • Spatial contiguity, distance, and weighting
  • Methods for determining spatial autocorrelation 
  • Spatial regression
Topic 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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Enroll Now - Select a section to enroll in
Section Title
Explicitly Accounting for Location in Agriculture: Spatial regression in Python
Type
--
Dates
Sep 01, 2024 to Aug 31, 2025
Delivery Options
Self Paced Online  
Course Fee(s)
Course Fee $175.00
Instructors
Section Details

Special Information about this Course:

  • Cancellations are subject to a $25.00 processing fee. 

Contact Information:

  • For questions regarding course registration, please email Kris Junker at junk0011@umn.edu or call 612-624-7253
  • For questions regarding course materials and contact, please email Jeff Thompson at jathomps@umn.edu
  • For questions regarding system access, log-in, username, or password, please email help@umn.edu or call 612-301-4357
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