Lesson06
IS415 GeoSpatial Analytics and Applications
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Content
- Basic concepts of Principal Component Analysis
- Standard Nonspatial PCA on Spatial Data
- Locally Weighted PCA
- Geographically Weighted PCA
References
Methods
Urška Demšar, Paul Harris, Chris Brunsdon, A. Stewart Fotheringham & Sean McLoone (2012): “Principal Component Analysis on Spatial Data: An Overview”. Annals of the Association of American Geographers, 103:1, 106-128.
Harris, P., Brunsdon, C., and Charlton, M. (2011) “Geographically weighted principal component analysis”. International Journal of Geographical Information Science, 25(10): 1717-1736.
Applications
Harris, P., et. al. (2015) “Enhancements to a geographically weighted principal components analysis in the context of an application to an environmental data set”. Geographical Analysis, 47: 146-172.
Comber, A., Harris, P., and Tsutsumida, N. (2016) “Improving land cover classification using texture variables outputted from a geographically weighted principal components analysis”. ISPRS Journal of Photogrammetry and Remote Sensing, 119: 347-360.
Tsutsumida, N., Harris, P., Comber, A. (2017) “The application of a geographically weighted principal components analysis for exploring 23 years of goat population change across Mongolia”. Annals of the Association of American Geographers, 107(5): 1060-1074.
Li, Z., Cheng, J. & Wu, Q. (2016)”Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis”, Letters in Spatial and Resource Sciences, 9(3), 233-245.
Kallio, M., Guillaume, J.H.A., Kummu, M. et al. (2018) “Spatial Variation in Seasonal Water Poverty Index for Laos: An Application of Geographically Weighted Principal Component Analysis”, Social Indicators Research, 140(3), 1131–1157.
The R Packages
Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2015) GWmodel: an R Package for exploring Spatial Heterogeneity using Geographically Weighted Models. Journal of Statistical Software 63(17): 1-50.
