[AM9-4] Spatial expansion and Geographically Weighted Regression GWR

Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.

External resources

• Páez, A., Wheeler, D.C, (2009) Geographically Weighted Regression, Editor(s): Rob Kitchin, Nigel Thrift, International Encyclopedia of Human Geography, Elsevier, 2009, Pages 407-414, ISBN 9780080449104

Skills

• 299 - Describe the characteristics of the spatial expansion method
  Describe the characteristics of the spatial expansion method
• 300 - Discuss the appropriateness of GWR under various conditions
  Discuss the appropriateness of GWR under various conditions
• 301 - Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity
  Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity
• 302 - Explain the principles of geographically weighted regression
  Explain the principles of geographically weighted regression
• 303 - Compare and contrast GWR with universal kriging using moving neighborhoods
  Compare and contrast GWR with universal kriging using moving neighborhoods
• 304 - Perform an analysis using the geographically weighted regression technique
  Perform an analysis using the geographically weighted regression technique
• 305 - Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results
  Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results

Status

Outgoing relations

• [AM9-4] Spatial expansion and Geographically Weighted Regression GWR is subconcept of [AM9] Spatial regression and econometrics

Contributors

• Rob Lemmens
  University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
  https://research.utwente.nl/en/persons/rob-lemmens