Lesson07
Jump to navigation
Jump to search
IS415 GeoSpatial Analytics and Applications
|
|
|
|
|
|
Modelling Spatial Varying Relationship: Geographically Weighted Regression methods
Contents
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] Section 1, 2, 3, 6 and 8.
In-Class Exercise
- Hands-on Exercise 7. The handout and data sets are available at course eLearn.
R Packages
GWmodel: Geographically-Weighted Models [2]
- 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, [3] Section 1, 2, 3, 6 and 8.
- 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, [4]
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.
