AM9-4 - Spatial expansion and geographically weighted regression (GWR)
You are currently viewing an archived version of Topic The Geographically Weighted Regression Framework.
If updates or revisions have been published you can find them at The Geographically Weighted Regression Framework.
Author and Citation Info:
DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., and Wentz, E. (2006). Spatial expansion and geographically weighted regression (GWR). The Geographic Information Science & Technology Body of Knowledge. Washington, DC: Association of American Geographers. (2nd Quarter 2016, first digital).
Learning Objectives:
Perform an analysis using the geographically weighted regression technique
Discuss the appropriateness of GWR under various conditions
Describe the characteristics of the spatial expansion method
Explain the principles of geographically weighted regression
Compare and contrast GWR with universal kriging using moving neighborhoods
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
Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results
You are currently viewing an archived version of Topic The Geographically Weighted Regression Framework. If updates or revisions have been published you can find them at The Geographically Weighted Regression Framework.
DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., and Wentz, E. (2006). Spatial expansion and geographically weighted regression (GWR). The Geographic Information Science & Technology Body of Knowledge. Washington, DC: Association of American Geographers. (2nd Quarter 2016, first digital).