Enhancements to a Geographically Weighted Principal Component Analysis in the Context of an Application to an Environmental Data Set


Harris, Paul and Clarke, Annemarie and Juggins, Steve and Brunsdon, Chris and Charlton, Martin (2015) Enhancements to a Geographically Weighted Principal Component Analysis in the Context of an Application to an Environmental Data Set. Geographical Analysis, 47 (2). pp. 146-172. ISSN 1538-4632

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Abstract

In many physical geography settings, principal component analysis (PCA) is applied without consideration for important spatial effects, and in doing so, tends to provide an incomplete understanding of a given process. In such circumstances, a spatial adaptation of PCA can be adopted, and to this end, this study focuses on the use of geographically weighted principal component analysis (GWPCA). GWPCA is a localized version of PCA that is an appropriate exploratory tool when a need exists to investigate for a certain spatial heterogeneity in the structure of a multivariate data set. This study provides enhancements to GWPCA with respect to: (i) finding the scale at which each localized PCA should operate; and (ii) visualizing the copious amounts of output that result from its application. An extension of GWPCA is also proposed, where it is used to detect multivariate spatial outliers. These advancements in GWPCA are demonstrated using an environmental freshwater chemistry data set, where a commentary on the use of preprocessed (transformed and standardized) data is also presented. The study is structured as follows: (1) the GWPCA methodology; (2) a description of the case study data; (3) the GWPCA application, demonstrating the value of the proposed advancements; and (4) conclusions. Most GWPCA functions have been incorporated within the GWmodel R package.

Item Type: Article
Keywords: Enhancements; Geographically Weighted Principal Component Analysis; Application; Environmental Data Set;
Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID: 8051
Identification Number: 10.1111/gean.12048
Depositing User: Prof. Chris Brunsdon
Date Deposited: 23 Mar 2017 12:06
Journal or Publication Title: Geographical Analysis
Publisher: Wiley
Refereed: Yes
URI:

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