An application of principal component analysis on multivariate time-stationary spatio-temporal data

Stephan Stahlschmidt, Wolfgang Härdle and Helmut Thome

No 2014-016, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: PCA; spatio-temporal analysis; dimension reduction; factor extraction; economic deprivation; urbanism (search for similar items in EconPapers)
JEL-codes: C31 C33 R11 (search for similar items in EconPapers)
Date: 2014
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Related works:
Journal Article: An Application of Principal Component Analysis on Multivariate Time-stationary Spatio-temporal Data (2015)
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