Difference between revisions of "Lesson07"
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** Beyond basic grw | ** Beyond basic grw | ||
** GW regression and addressing local collinearity | ** GW regression and addressing local collinearity | ||
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| + | =Must do= | ||
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| + | * Read | ||
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| + | Brunsdon, C., Fotheringham, A.S., and Charlton, M. (2002) “Geographically weighted regression: A method for exploring spatial nonstationarity”. Geographical Analysis, 28: 281-289. | ||
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| + | =In-Class Exercise= | ||
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| + | * Hands-on Exercise 7. The handout and data sets are available at course eLearn. | ||
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=R Packages= | =R Packages= | ||
| − | '''GWmodel''': Geographically-Weighted Models [https://cran.r-project.org/web/packages/ | + | '''GWmodel''': Geographically-Weighted Models [https://cran.r-project.org/web/packages/GWmodel/index.html] |
Revision as of 20:49, 14 February 2019
IS415 GeoSpatial Analytics and Applications
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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.
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.
Applications
R Packages
GWmodel: Geographically-Weighted Models [1]
