A Principal Component Analysis (PCA)-based framework for automated variable selection in geodemographic classification-Reference-Cited by-同舟云学术

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

Published:2019-06-05 Issue:4 Volume:22 Page:251-264
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Liu Yunzhe¹^ORCID,Singleton Alex¹^ORCID,Arribas-Bel Daniel¹^ORCID

Affiliation:

1. Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool, UK

Publisher

Informa UK Limited

Subject

Computers in Earth Sciences,Geography, Planning and Development

Link

https://tandfonline.com/doi/pdf/10.1080/10095020.2019.1621549

Reference40 articles.

1. Principal component analysis

2. Adnan, M. 2011. “Towards Real-Time Geodemographic Information Systems: Design, Analysis and Evaluation.” PhD thesis, University College London.

3. Alexiou, A. 2016. “Putting “Geo” into Geodemographics: Evaluating the Performance of National Classification Systems within Regional Contexts.” PhD thesis, University of Liverpool.

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