Difference between revisions of "Lesson06"

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<font size="6">'''Geographical Segmentation with Spatially Constrained Cluster Analysis'''</font>
  
 
=Content=
 
=Content=
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* Basic concepts of Principal Component Analysis
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* Basic concepts of geographic segmentation
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* Standard Nonspatial PCA on Spatial Data
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* Conventional cluster analysis techniques
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* Locally Weighted PCA
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* Approaches for clustering geographically referenced data
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* Geographically Weighted PCA
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** Hierarchical clustering with spatial constraints
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** Minimum spanning trees
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** Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap)
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 +
 
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=Must do=
 +
 
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* Complete:
 +
**  Chapter 2: Hierarchical clustering of Unsupervised Learning in DataCamp.
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* View
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** Lecture on “[https://www.youtube.com/watch?v=LCf_7o7teuw&list=PLzREt6r1Nenlu-MBaxCRL2KZNk62n7o1g&index=8 Spatially Constrained Clusters]” by Luc Anselin (link to 1hr and 20mins video).
 +
 
 +
* Read
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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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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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=References=
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=In-Class Exercise=
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* Hands-on Exercise 6. The handout and data sets are available at course eLearn.
  
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==Methods==
 
  
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Urška Demšar, Paul Harris, Chris Brunsdon, A. Stewart Fotheringham & Sean McLoone (2012): “Principal Component Analysis on Spatial Data: An Overview”. Annals of the Association of American Geographers, 103:1, 106-128.
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=R Packages=
  
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Harris, P., Brunsdon, C., and Charlton, M. (2011) “Geographically weighted principal component analysis”. International Journal of Geographical Information Science, 25(10): 1717-1736.
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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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'''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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==Applications==
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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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Harris, P., et. al. (2015) “Enhancements to a geographically weighted principal components analysis in the context of an application to an environmental data set”. ''Geographical Analysis'', 47: 146-172.
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'''spatialcluster''': An R package for spatially-constrained clustering using either distance or covariance matrices. [https://github.com/mpadge/spatialcluster]
  
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Comber, A., Harris, P., and Tsutsumida, N. (2016) “Improving land cover classification using texture variables outputted from a geographically weighted principal components analysis”. ''ISPRS Journal of Photogrammetry and Remote Sensing'', 119: 347-360.
 
  
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Tsutsumida, N., Harris, P., Comber, A. (2017) “The application of a geographically weighted principal components analysis for exploring 23 years of goat population change across Mongolia”. ''Annals of the Association of American Geographers'', 107(5): 1060-1074.
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=References=
  
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Li, Z., Cheng, J. & Wu, Q. (2016)”Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis”, ''Letters in Spatial and Resource Sciences'', 9(3), 233-245.
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==Methods==
  
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=The R Packages=
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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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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.
+
 
 +
==Applications==
  
 +
Rovan, J. and Sambt, J. (2003) “Socio-economic Differences Among Slovenian Municipalities: A Cluster Analysis Approach”, ''Developments in Applied Statistics'', pp. 265-278. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.126.4636&rep=rep1&type=pdf]
 +
 +
Demeter, T.  and Bratucu, G. (2013) “Statistical Analysis Of The EU Countries from A Touristic Point of View”, ''Bulletin of the Transilvania University of Braşov'', 6(55): 121-130. [https://search-proquest-com.libproxy.smu.edu.sg/docview/1510289237?rfr_id=info%3Axri%2Fsid%3Aprimo]
  
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=Lesson competencies=
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Brown, N.S. & Watson, P. (2012) “What can a comprehensive plan really tell us about a region?: A cluster analysis of county comprehensive plans in Idaho”, ''Western Economics Forum''. pp.22-37. [https://ageconsearch.umn.edu/record/176591/files/WEFFall2012v11n2_Brown.pdf]

Latest revision as of 22:20, 14 February 2019

Claraview.png IS415 GeoSpatial Analytics and Applications

About

Weekly Session

Take-home Exercises

Geospatial Analytics Project

Course Resources

 


Geographical Segmentation with Spatially Constrained Cluster Analysis

Content

  • Basic concepts of geographic segmentation
  • Conventional cluster analysis techniques
  • Approaches for clustering geographically referenced data
    • Hierarchical clustering with spatial constraints
    • Minimum spanning trees
    • Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap)


Must do

  • Complete:
    • Chapter 2: Hierarchical clustering of Unsupervised Learning in DataCamp.
  • Read

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.

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.


In-Class Exercise

  • Hands-on Exercise 6. The handout and data sets are available at course eLearn.


R Packages

AMOEBA: A Multidirectional Optimum Ecotope-Based Algorithm [1]

ClustGeo: Hierarchical Clustering with Spatial Constraints [2] and Introduction to Clustgeo [3]

skater: A function from spdep package that implements a SKATER procedure for spatial clustering analysis.[4]

spatialcluster: An R package for spatially-constrained clustering using either distance or covariance matrices. [5]


References

Methods

Guo, D. 2008. “Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap).” International Journal of Geographical Information Science, 22(7): 801-823.

Applications

Rovan, J. and Sambt, J. (2003) “Socio-economic Differences Among Slovenian Municipalities: A Cluster Analysis Approach”, Developments in Applied Statistics, pp. 265-278. [6]

Demeter, T. and Bratucu, G. (2013) “Statistical Analysis Of The EU Countries from A Touristic Point of View”, Bulletin of the Transilvania University of Braşov, 6(55): 121-130. [7]

Brown, N.S. & Watson, P. (2012) “What can a comprehensive plan really tell us about a region?: A cluster analysis of county comprehensive plans in Idaho”, Western Economics Forum. pp.22-37. [8]

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