Difference between revisions of "Lesson06"
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* Hands-on Exercise 5. The handout and data sets are available at course eLearn.. | * Hands-on Exercise 5. The handout and data sets are available at course eLearn.. | ||
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=References= | =References= | ||
==Methods== | ==Methods== | ||
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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. | Guo, D. 2008. “Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap).” International Journal of Geographical Information Science, 22(7): 801-823. | ||
==Applications== | ==Applications== | ||
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| + | 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] | ||
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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] | ||
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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] | ||
Revision as of 20:39, 10 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 5. The handout and data sets are available at course eLearn..
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. [1]
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. [2]
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. [3]
R Packages
AMOEBA: A Multidirectional Optimum Ecotope-Based Algorithm [4]
ClustGeo: Hierarchical Clustering with Spatial Constraints [5] and Introduction to Clustgeo [6]
skater: A function from spdep package that implements a SKATER procedure for spatial clustering analysis.[7]
spatialcluster: An R package for spatially-constrained clustering using either distance or covariance matrices. [8]
