A generalized additive model (GAM) approach to principal component analysis of geographic data-Reference-Cited by-同舟云学术

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A generalized additive model (GAM) approach to principal component analysis of geographic data

Published:2024-03 Issue: Volume:59 Page:100806
ISSN:2211-6753
Container-title:Spatial Statistics
language:en
Short-container-title:Spatial Statistics

Author:

de Asís López Francisco^ORCID,Ordóñez Celestino,Roca-Pardiñas Javier

Funder

España Ministerio de Ciencia Innovación y Universidades

Universidad de Oviedo

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Statistics and Probability

Reference30 articles.

1. Geospatial data science in R;Ahmed,2022

2. Soil fertility quality assessment based on geographically weighted principal component analysis (GWPCA) in large-scale areas;Chen;Catena,2021

3. Principal component analysis on spatial data: An overview;Demšar;Ann. Am. Assoc. Geogr.,2013

4. Geographically weighted principal components analysis to assess diffuse pollution sources of soil heavy metal: application to rough mountain areas in Northwest Spain;Fernandez;Geoderma,2018

5. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships;Fotheringham,2002

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Creating area level indices of behaviours impacting cancer in Australia with a Bayesian generalised shared component model;Health & Place;2024-09

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