Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 22
Articles | Volume 17, issue 22
https://doi.org/10.5194/gmd-17-8455-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8455-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 22
Development and technical paper
 | 
28 Nov 2024
Development and technical paper |  | 28 Nov 2024

GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity

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

Viewed

Total article views: 3,595 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,169 1,354 72 3,595 69 121
  • HTML: 2,169
  • PDF: 1,354
  • XML: 72
  • Total: 3,595
  • BibTeX: 69
  • EndNote: 121
Views and downloads (calculated since 29 May 2024)
Cumulative views and downloads (calculated since 29 May 2024)

Viewed (geographical distribution)

Total article views: 3,595 (including HTML, PDF, and XML) Thereof 3,510 with geography defined and 85 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Dec 2025
Download
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 and an open-source Python package named Geographically Neural Network Weighted Regression to capture relationships between factors. This makes it a valuable tool for researchers in fields such as environmental science, urban planning, and public health.
Read more
Share