Difference between revisions of "Lesson07"
Jump to navigation
Jump to search
| Line 29: | Line 29: | ||
<br/> | <br/> | ||
| − | <font size="6">'''Modelling Spatial Varying Relationship | + | <font size="6">'''Modelling Spatial Varying Relationship: Geographically Weighted Regression methods'''</font> |
=Content= | =Content= | ||
Revision as of 20:41, 14 February 2019
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
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.
Applications
R Packages
GWmodel: Geographically-Weighted Models [1]
