Lesson 3: Geographic Segmentation with Spatially Constrained Cluster Analysis

Published

December 4, 2022

Overview

Basic concepts of geographic segmentation
Conventional cluster analysis techniques
Approaches for clustering geographically referenced data
- Hierarchical clustering with spatial constraints
- Minimum spanning trees

To read before class:

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. (Available in SMU digital library)
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. (Available in SMU digital library)

Rovan, J. and Sambt, J. (2003) “Socio-economic Differences Among Slovenian Municipalities: A Cluster Analysis Approach”, Developments in Applied Statistics, pp. 265-278.
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.
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.
Jaya, I.G.M., Ruchjana, B.N., Andriyana, Y. & Agata, R (2019) “Clustering with spatial constraints: The case of diarrhea in Bandung city, Indonesia”
de Souza, D. C. & Taconeli, C. A. (2022) “Spatial and non-spatial clustering algorithm in the analysis of Brazilian educational data”, Communications in Statistics: Case Studies, Data Analysis, and Applications. Vol. 8, No. 4, 588-606. (Available in SMU digital library)