A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Que, Xiang; Ma, Xiaogang; Ma, Chao; Chen, Qiyu

doi:https://doi.org/10.5194/gmd-13-6149-2020

Articles | Volume 13, issue 12

https://doi.org/10.5194/gmd-13-6149-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-6149-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 12

Model description paper

|

03 Dec 2020

Model description paper |

| 03 Dec 2020

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen

Data sets

quexiang/STWR: STWR v1.0 (Version v1.0) X. Que https://doi.org/10.5281/zenodo.3637689

Waterisotopes Database G. Bowen https://wateriso.utah.edu/waterisotopes/pages/spatial_db/SPATIAL_DB.html

PRISM Climate Data PRISM Climate Group https://prism.oregonstate.edu

GMTED2010 Viewer USGS https://topotools.cr.usgs.gov/gmted_viewer/viewer.htm

Short summary

This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.