Difference between revisions of "Lesson06"

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=Must do=
 
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* Complete:
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**  Chapter 2: Hierarchical clustering of Unsupervised Learning in DataCamp.
  
 
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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.
 
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.
 
* Complete:
 
**  Chapter 2: Hierarchical clustering of Unsupervised Learning in DataCamp.
 
  
  

Revision as of 09:41, 10 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 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

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]


Lesson competencies

Technical References

Application References