Peer-Reviewed Journal Details
Mandatory Fields
Demsar, U;Harris, P;Brunsdon, C;Fotheringham, AS;McLoone, S
2013
January
Annals of the Association of American Geographers
Principal Component Analysis on Spatial Data: An Overview
Published
18 ()
Optional Fields
FACTORIAL KRIGING ANALYSIS GEOGRAPHICALLY WEIGHTED REGRESSION EMPIRICAL ORTHOGONAL FUNCTIONS COMPOSITIONAL DATA-ANALYSIS CANCER-MORTALITY-RATES GEO-REFERENCED DATA GEOSTATISTICAL ANALYSIS OUTLIER DETECTION R-PACKAGE MULTIVARIATE-ANALYSIS
103
106
128
This article considers critically how one of the oldest and most widely applied statistical methods, principal components analysis (PCA), is employed with spatial data. We first provide a brief guide to how PCA works: This includes robust and compositional PCA variants, links to factor analysis, latent variable modeling, and multilevel PCA. We then present two different approaches to using PCA with spatial data. First we look at the nonspatial approach, which avoids challenges posed by spatial data by using a standard PCA on attribute space only. Within this approach we identify four main methodologies, which we define as (1) PCA applied to spatial objects, (2) PCA applied to raster data, (3) atmospheric science PCA, and (4) PCA on flows. In the second approach, we look at PCA adapted for effects in geographical space by looking at PCA methods adapted for first-order nonstationary effects (spatial heterogeneity) and second-order stationary effects (spatial autocorrelation). We also describe how PCA can be used to investigate multiple scales of spatial autocorrelation. Furthermore, we attempt to disambiguate a terminology confusion by clarifying which methods are specifically termed "spatial PCA" in the literature and how this term has different meanings in different areas. Finally, we look at a further three variations of PCA that have not been used in a spatial context but show considerable potential in this respect: simple PCA, sparse PCA, and multilinear PCA.
ABINGDON
0004-5608
10.1080/00045608.2012.689236
Grant Details