Lesson07

IS415 GeoSpatial Analytics and Applications

About

Weekly Session

Take-home Exercises

Geospatial Analytics Project

Course Resources

Modelling Spatial Varying Relationship: Geographically Weighted Regression methods

Content

• Granddaddy of All Models: Multiple Regression
• Basic concepts of Spatial Non-stationary
• Geographically Weighted Regression (gwr) Methods
  • Basic principles and concepts
  • Distance matrix, kernel and bandwidth
  • Basic grw
  • Beyond basic grw
  • GW regression and addressing local collinearity

Must do

• Read

Brunsdon, C., Fotheringham, A.S., and Charlton, M. (2002) “Geographically weighted regression: A method for exploring spatial nonstationarity”. Geographical Analysis, 28: 281-289.

Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2015) "GWmodel: an R Package for exploring Spatial Heterogeneity using Geographically Weighted Models". *Journal of Statistical Software*, 63(17):1-50, [1]

Lu B, Harris P, Charlton M, Brunsdon C (2014) "The GWmodel R Package: further topics for exploring Spatial Heterogeneity using Geographically Weighted Models". *Geo-spatial Information Science* 17(2): 85-101, [2]

In-Class Exercise

• Hands-on Exercise 7. The handout and data sets are available at course eLearn.

References

Methods

Brunsdon, C., Fotheringham, A.S., and Charlton, M. (2002) “Geographically weighted regression: A method for exploring spatial nonstationarity”. Geographical Analysis, 28: 281-289.

Brunsdon, C., Fotheringham, A.S. and Charlton, M., (1999) “Some Notes on Parametric Significance Tests for Geographically Weighted Regression”. Journal of Regional Science, 39(3), 497-524.

Harris, P. et al., (2010) “The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets”. Mathematical Geosciences, 42(6), 657-680.

R Packages

GWmodel: Geographically-Weighted Models [3]