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    Principal Component Analysis on Spatial Data: An Overview


    Demšar, Urška and Harris, Paul and Brunsdon, Chris and Fotheringham, Stewart and McLoone, Sean F. (2013) Principal Component Analysis on Spatial Data: An Overview. Annals of the Association of American Geographers, 103 (1). pp. 106-128. ISSN 1467-8306

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    Abstract

    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.

    Item Type: Article
    Keywords: dimensionality reduction; multivariate statistics; principal components analysis; spatial analysis; mathematical modeling; spatial data;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 5850
    Identification Number: https://doi.org/10.1080/00045608.2012.689236
    Depositing User: Prof. Chris Brunsdon
    Date Deposited: 17 Feb 2015 17:18
    Journal or Publication Title: Annals of the Association of American Geographers
    Publisher: Taylor & Francis
    Refereed: Yes
    URI:

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