Difference between revisions of "Lesson07"

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<font size="6">'''Modelling Spatial Varying Relationship: Geographically Weighted Regression methods'''</font>
  
 
=Content=
 
=Content=
* What is geospatial analytics?
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* Granddaddy of All Models: Multiple Regression
* Motivation of promoting geospatial analytics
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* Basic concepts of Spatial Non-stationary
* Case study
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* Geographically Weighted Regression (gwr) Methods
* An overview of Geographic Information Systems (GIS)
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** Basic principles and concepts
* GIS versus Geospatial Analytics
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** Distance matrix, kernel and bandwidth
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** Basic grw
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** Beyond basic grw
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** 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.
 +
 
 +
* 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, [http://www.jstatsoft.org/v63/i17/] Section 1, 2, 3, 6 and 8.
 +
 
 +
 
 +
=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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 +
 
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=R Packages=
 +
 
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'''GWmodel''': Geographically-Weighted Models [https://cran.r-project.org/web/packages/GWmodel/index.html]
 +
 
 +
* 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, [http://www.jstatsoft.org/v63/i17/] 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, [https://www.tandfonline.com/doi/full/10.1080/10095020.2014.917453]
 +
 
 +
=References=
  
 +
==Methods==
  
=Lesson competencies=
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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.
  
=Technical References=
+
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.
  
=Application References=
+
==Applications==

Latest revision as of 22:24, 14 February 2019

Claraview.png 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] 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.

Applications