A Principal Component Analysis (PCA)-based framework for automated variable selection in geodemographic classification

• Published in: Geo-spatial information science Vol. 22; no. 4; pp. 251 - 264
• Main Authors: Liu, Yunzhe, Singleton, Alex, Arribas-Bel, Daniel
• Format: Journal Article
• Language: English
• Published: Wuhan Taylor & Francis 02.10.2019; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Area classification; Automation; Classification; Clusters; Context; Geodemographics; Geography; Methodology; principal component analysis; Principal components analysis; spatial data mining; UK census; variable selection; Variables; United Kingdom > UK
• DOI: 10.1080/10095020.2019.1621549
• ISSN: 1009-5020

A geodemographic classification aims to describe the most salient characteristics of a small area zonal geography. However, such representations are influenced by the methodological choices made during their construction. Of particular debate are the choice and specification of input variables, with the objective of identifying inputs that add value but also aim for model parsimony. Within this context, our paper introduces a principal component analysis (PCA)-based automated variable selection methodology that has the objective of identifying candidate inputs to a geodemographic classification from a collection of variables. The proposed methodology is exemplified in the context of variables from the UK 2011 Census, and its output compared to the Office for National Statistics 2011 Output Area Classification (2011 OAC). Through the implementation of the proposed methodology, the quality of the cluster assignment was improved relative to 2011 OAC, manifested by a lower total within-cluster sum of square score. Across the UK, more than 70.2% of the Output Areas (OAs) occupied by the newly created classification (i.e. AVS-OAC) outperform the 2011 OAC, with particularly strong performance within Scotland and Wales.