Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau

Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau

This paper combines the use of principal component analysis (PCA) and the geographically weighted regression (GWR) model to predict the spatial distribution of frozen ground temperature. PCA is used to reduce the multicollinearity among covariates, while the GWR model is used to address the spatiall...

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Bibliographic Details
Main Authors: Rui Zhao, Mingxing Yao, Linchuan Yang, Hua Qi, Xianglian Meng, Fujun Zhou
Format: Article
Language:English
Published: IOP Publishing 2021-01-01
Series:Environmental Research Letters
Subjects:
geographically weighted regression
principal component analysis
frozen ground
ground temperature
permafrost
spatial heterogeneity
Online Access:https://doi.org/10.1088/1748-9326/abd431

Internet

https://doi.org/10.1088/1748-9326/abd431

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