Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/93227
Authors: 
Stahlschmidt, Stephan
Härdle, Wolfgang Karl
Thome, Helmut
Year of Publication: 
2014
Series/Report no.: 
SFB 649 Discussion Paper 2014-016
Abstract: 
Principal component analysis denotes a popular algorithmic technique to dimension reduction and factor extraction. Spatial variants have been proposed to account for the particularities of spatial data, namely spatial heterogeneity and spatial autocorrelation, and we present a novel approach which transfers principal component analysis into the spatio-temporal realm. Our approach, named stPCA, allows for dimension reduction in the attribute space while striving to preserve much of the data's variance and maintaining the data's original structure in the spatio-temporal domain. Additionally to spatial autocorrelation stPCA exploits any serial correlation present in the data and consequently takes advantage of all particular features of spatial-temporal data. A simulation study underlines the superior performance of stPCA if compared to the original PCA or its spatial variants and an application on indicators of economic deprivation and urbanism demonstrates its suitability for practical use.
Subjects: 
PCA
spatio-temporal analysis
dimension reduction
factor extraction
economic deprivation
urbanism
JEL: 
C31
C33
R11
Document Type: 
Working Paper

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