Difference between revisions of "Lesson06"
| Line 38: | Line 38: | ||
** Minimum spanning trees | ** Minimum spanning trees | ||
** Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap) | ** Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap) | ||
| + | |||
| + | |||
| + | =Must do= | ||
| + | |||
| + | * View | ||
| + | ** 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 | ||
| + | ** Anselin, L. (1995). "Local indicators of spatial association – LISA". ''Geographical Analysis'', 27(4): 93-115. | ||
| + | ** Brunsdon, C. & Comber, L. (2015) '''An Introduction to R for Spatial Analysis & Mapping''', SAGE Publication Ltd., London. Chapter 8: 8.1-8.6. | ||
| + | ** Bivand, R.S., Pebesma, E. & Gómez-Rubio, V. (2013) '''Applied Spatial Data Analysis with R''', 2nd Edition. Springer, New York. Chapter 9: Modeling Areal Data, Section 9.3.2 Local Tests. This is an e-book. | ||
| + | |||
| + | *Complete Hands-on Exercise 5. The handout and data sets are available at course eLearn. | ||
| + | |||
Revision as of 09:18, 10 February 2019
IS415 GeoSpatial Analytics and Applications
|
|
|
|
|
|
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
- View
- Lecture on “Spatially Constrained Clusters” by Luc Anselin (link to 1hr and 20mins video).
- Read
- Anselin, L. (1995). "Local indicators of spatial association – LISA". Geographical Analysis, 27(4): 93-115.
- Brunsdon, C. & Comber, L. (2015) An Introduction to R for Spatial Analysis & Mapping, SAGE Publication Ltd., London. Chapter 8: 8.1-8.6.
- Bivand, R.S., Pebesma, E. & Gómez-Rubio, V. (2013) Applied Spatial Data Analysis with R, 2nd Edition. Springer, New York. Chapter 9: Modeling Areal Data, Section 9.3.2 Local Tests. This is an e-book.
- Complete Hands-on Exercise 5. The handout and data sets are available at course eLearn.
References
Methods
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.
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]
