Difference between revisions of "Lesson07"

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==Methods==
 
==Methods==
  
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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.
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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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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.
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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.
  
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Guo, D. 2008. “Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap).” International Journal of Geographical Information Science, 22(7): 801-823.  
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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==
 
==Applications==
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=R Packages=
 
=R Packages=
  
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'''AMOEBA''': A Multidirectional Optimum Ecotope-Based Algorithm [https://cran.r-project.org/web/packages/AMOEBA/index.html]
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'''GWmodel''': Geographically-Weighted Models [https://cran.r-project.org/web/packages/AMOEBA/index.html]
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'''ClustGeo''': Hierarchical Clustering with Spatial Constraints [https://cran.r-project.org/web/packages/ClustGeo/index.html] and Introduction to Clustgeo [https://cran.r-project.org/web/packages/ClustGeo/vignettes/intro_ClustGeo.html]
 
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'''skater''': A function from spdep package that implements a SKATER procedure for spatial clustering analysis.[https://www.rdocumentation.org/packages/spdep/versions/0.8-1/topics/skater]
 
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'''spatialcluster''': An R package for spatially-constrained clustering using either distance or covariance matrices. [https://github.com/mpadge/spatialcluster]
 
  
  

Revision as of 16:54, 6 February 2019

Claraview.png IS415 GeoSpatial Analytics and Applications

About

Weekly Session

Take-home Exercises

Geospatial Analytics Project

Course Resources

 


Modelling Spatial Varying Relationship with Geographically Weighted Regression

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]


Lesson competencies

Technical References

Application References

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