Difference between revisions of "Lesson07"

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<font size="6">'''Geographical Segmentation with Spatially Constrained Cluster Analysis'''</font>
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<font size="6">'''Modelling Spatial Varying Relationship: Geographically Weighted Regression methods'''</font>
  
 
=Content=
 
=Content=
* Basic concepts of geographic segmentation
+
* Granddaddy of All Models: Multiple Regression
* Conventional cluster analysis techniques
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* Basic concepts of Spatial Non-stationary
* Approaches for clustering geographically referenced data
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* Geographically Weighted Regression (gwr) Methods
** Hierarchical clustering with spatial constraints
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** Basic principles and concepts
** Minimum spanning trees
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** Distance matrix, kernel and bandwidth
** Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap)
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** Basic grw
 +
** Beyond basic grw
 +
** GW regression and addressing local collinearity
  
  
=References=
+
=Must do=
  
==Methods==
+
Read:
 +
* Brunsdon, C., Fotheringham, A.S., and Charlton, M. (2002) “Geographically weighted regression: A method for exploring spatial nonstationarity”. Geographical Analysis, 28: 281-289.
  
Assuncao, R. M., Neves, M.C., Camara, G. and Costa Freitas, C.D. 2006. “Efficient Regionalization Techniques for Socio-Economic Geographical Units Using Minimum Spanning Trees.” International Journal of Geographical Information Science 20: 797–811.
+
* 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.  
  
Chavent, M., Kuentz-Simonet, V., Labenne,A. and Saracco, J. 2018. “ClustGeo: an R package for hierarchical clustering with spatial constraints” Computational Statistics. 33: 1799-1822.
 
  
Guo, D. 2008. “Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap).” International Journal of Geographical Information Science, 22(7): 801-823.
+
=In-Class Exercise=
  
==Applications==
+
* Hands-on Exercise 7. The handout and data sets are available at course eLearn.
  
  
 
=R Packages=
 
=R Packages=
  
 +
'''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==
  
 +
Brunsdon, C., Fotheringham, A.S., and Charlton, M. (2002) “Geographically weighted regression: A method for exploring spatial nonstationarity”. Geographical Analysis, 28: 281-289.
  
=Lesson competencies=
+
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.
  
=Technical References=
+
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==
=Application References=
 

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