Difference between revisions of "Lesson06"
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* Hands-on Exercise 6. The handout and data sets are available at course eLearn. | * Hands-on Exercise 6. The handout and data sets are available at course eLearn. | ||
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| + | =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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| + | '''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] | ||
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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] | 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] | ||
| − | 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 | + | 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] |
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Latest revision as of 22:20, 14 February 2019
IS415 GeoSpatial Analytics and Applications
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Geographical Segmentation with Spatially Constrained Cluster Analysis
Contents
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.
- View
- Lecture on “Spatially Constrained Clusters” by Luc Anselin (link to 1hr and 20mins video).
- 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]
