Lesson 6: Geographic Segmentation with Spatially Constrained Cluster Analysis

Published

July 21, 2024

Modified

August 15, 2025

Overview

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

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)