Preprints
https://doi.org/10.5194/gmd-2024-62
https://doi.org/10.5194/gmd-2024-62
Submitted as: development and technical paper
 | 
29 May 2024
Submitted as: development and technical paper |  | 29 May 2024
Status: this preprint is currently under review for the journal GMD.

GNNWR: An Open-Source Package of Spatiotemporal Intelligent Regression Methods for Modeling Spatial and Temporal Non-Stationarity

Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du

Abstract. Spatiotemporal regression is a crucial method in geography for discerning spatiotemporal non-stationarity in geographical relationships, which has found widespread application across diverse research domains. This study implements two innovative spatiotemporal intelligent regression models, namely geographically neural network weighted regression (GNNWR) and geographically and temporally neural network weighted regression (GTNNWR), integrating the spatiotemporal weighted framework and neural networks. Demonstrating superior accuracy and generalization capabilities in large-scale data environments compared to traditional methods, these models have emerged as prominent tools. To facilitate the seamless application of GNNWR and GTNNWR in addressing spatiotemporal non-stationary processes, a Python-based package, GNNWR, has been developed. This article details the implementation of these models and introduces the GNNWR package, enabling users to efficiently apply these cutting-edge techniques. Validation of the package is conducted through two case studies. The first case involves the verification of GNNWR using air quality data from China, while the second employs offshore dissolved silicate concentration data from Zhejiang Province to validate GTNNWR. The results of the case studies underscore the effectiveness of the GNNWR package, yielding outcomes of notable accuracy. This contribution anticipates a significant role for the developed package in supporting future research that leverages big data and spatiotemporal regression techniques.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du

Status: open (until 24 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-62', Anonymous Referee #1, 05 Jun 2024 reply
  • RC2: 'Comment on gmd-2024-62', Anonymous Referee #2, 13 Jun 2024 reply
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du

Data sets

Replication package for GNNWR v0.1.11: A Python package for modeling spatial temporal non-stationary Ziyu Yin et al. https://doi.org/10.5281/zenodo.10890255

Model code and software

GNNWR v0.1.11: A Python package for modeling spatial temporal non-stationary Ziyu Yin et al. https://doi.org/10.5281/zenodo.10890176

Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du

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Short summary
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural network-based spatiotemporal regression models as well as an open-sourced Python package named GNNWR, to accurately capture the varying relationships between factors. This makes it a valuable tool for researchers in various fields, such as environmental science, urban planning, and public health.