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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
28 Nov 2024
Development and technical paper |
| 28 Nov 2024
| 28 Nov 2024
GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity
Ziyu Yin
School of Earth Sciences, Zhejiang University, Hangzhou, China
Jiale Ding
School of Earth Sciences, Zhejiang University, Hangzhou, China
School of Earth Sciences, Zhejiang University, Hangzhou, China
Ruoxu Wang
School of Earth Sciences, Zhejiang University, Hangzhou, China
Yige Wang
School of Earth Sciences, Zhejiang University, Hangzhou, China
Yijun Chen
School of Earth Sciences, Zhejiang University, Hangzhou, China
Jin Qi
School of Earth Sciences, Zhejiang University, Hangzhou, China
Sensen Wu
CORRESPONDING AUTHOR
School of Earth Sciences, Zhejiang University, Hangzhou, China
Zhenhong Du
School of Earth Sciences, Zhejiang University, Hangzhou, China
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Total article views: 4,361 (including HTML, PDF, and XML)
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Total article views: 1,804 (including HTML, PDF, and XML)
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Cited
14 citations as recorded by crossref.
- Similarity-weighted and geographically weighted regression using a dual-branch neural network architecture Z. Wang & Z. Hong https://doi.org/10.1080/13658816.2026.2714042
- The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure N. Kianfar et al. https://doi.org/10.4081/gh.2025.1386
- Global distribution and evolutionary trends of the PM2.5 health burden predicted with a Geographically Neural Network Weighted Regression model H. Wang et al. https://doi.org/10.1016/j.envpol.2025.127502
- Cross-sectional accuracy does not imply the reliability of population change in gridded population datasets of China L. Li et al. https://doi.org/10.1057/s41599-026-07688-w
- Seamless Daily XCO2 Mapping Across China From OCO-3 Observations via a Hybrid Framework of Transformer and Geo-Implicit Neural Representation J. Luo et al. https://doi.org/10.1109/TGRS.2026.3732466
- Overcoming the isotropic bias in mineral prospectivity mapping: a direction-aware neural network approach for sandstone-hosted uranium F. Diao et al. https://doi.org/10.1016/j.oregeorev.2026.107369
- Dual friction mapping of urban pedestrian spaces: Integrating street-view visual analytics and Geographically Neural Network Weighted Regression in Izmir, Türkiye Y. Eminoğlu & K. Çubukçu https://doi.org/10.1016/j.scs.2026.107913
- Predicting PM 2.5 concentrations with mobile graph convolution and frequency-window transformer using meteorological monitoring data K. Jia et al. https://doi.org/10.1088/1361-6501/ae80ec
- A Spatiotemporal Wildfire Risk Prediction Framework Integrating Density-Based Clustering and GTWR-RFR S. Xie et al. https://doi.org/10.3390/f16111632
- Unravelling intra-urban spatiotemporal disparities in housing price dynamics: An infrastructural approach using interpretable machine-learning methods in Shenzhen, China L. Hu et al. https://doi.org/10.1016/j.landusepol.2026.108314
- Context-sensitive analysis of disaster resilience and equity through geospatial explainable machine learning Y. Ding et al. https://doi.org/10.1016/j.scs.2026.107203
- A context entanglement-based geographically weighted regression method X. Hu et al. https://doi.org/10.1080/15481603.2026.2713313
- Integrating Geodetector and GTWR to Unveil Spatiotemporal Heterogeneity in China’s Agricultural Carbon Emissions Under the Dual Carbon Goals H. Dang et al. https://doi.org/10.3390/agriculture15121302
- Diag-STFN: A diagnostic spatiotemporal multimodal fusion network for global pre-harvest crop yield forecasting H. Zhuang et al. https://doi.org/10.1016/j.ecoinf.2026.103860
14 citations as recorded by crossref.
- Similarity-weighted and geographically weighted regression using a dual-branch neural network architecture Z. Wang & Z. Hong https://doi.org/10.1080/13658816.2026.2714042
- The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure N. Kianfar et al. https://doi.org/10.4081/gh.2025.1386
- Global distribution and evolutionary trends of the PM2.5 health burden predicted with a Geographically Neural Network Weighted Regression model H. Wang et al. https://doi.org/10.1016/j.envpol.2025.127502
- Cross-sectional accuracy does not imply the reliability of population change in gridded population datasets of China L. Li et al. https://doi.org/10.1057/s41599-026-07688-w
- Seamless Daily XCO2 Mapping Across China From OCO-3 Observations via a Hybrid Framework of Transformer and Geo-Implicit Neural Representation J. Luo et al. https://doi.org/10.1109/TGRS.2026.3732466
- Overcoming the isotropic bias in mineral prospectivity mapping: a direction-aware neural network approach for sandstone-hosted uranium F. Diao et al. https://doi.org/10.1016/j.oregeorev.2026.107369
- Dual friction mapping of urban pedestrian spaces: Integrating street-view visual analytics and Geographically Neural Network Weighted Regression in Izmir, Türkiye Y. Eminoğlu & K. Çubukçu https://doi.org/10.1016/j.scs.2026.107913
- Predicting PM 2.5 concentrations with mobile graph convolution and frequency-window transformer using meteorological monitoring data K. Jia et al. https://doi.org/10.1088/1361-6501/ae80ec
- A Spatiotemporal Wildfire Risk Prediction Framework Integrating Density-Based Clustering and GTWR-RFR S. Xie et al. https://doi.org/10.3390/f16111632
- Unravelling intra-urban spatiotemporal disparities in housing price dynamics: An infrastructural approach using interpretable machine-learning methods in Shenzhen, China L. Hu et al. https://doi.org/10.1016/j.landusepol.2026.108314
- Context-sensitive analysis of disaster resilience and equity through geospatial explainable machine learning Y. Ding et al. https://doi.org/10.1016/j.scs.2026.107203
- A context entanglement-based geographically weighted regression method X. Hu et al. https://doi.org/10.1080/15481603.2026.2713313
- Integrating Geodetector and GTWR to Unveil Spatiotemporal Heterogeneity in China’s Agricultural Carbon Emissions Under the Dual Carbon Goals H. Dang et al. https://doi.org/10.3390/agriculture15121302
- Diag-STFN: A diagnostic spatiotemporal multimodal fusion network for global pre-harvest crop yield forecasting H. Zhuang et al. https://doi.org/10.1016/j.ecoinf.2026.103860
Saved (final revised paper)
Latest update: 06 Oct 2026
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.
In geography, understanding how relationships between different factors change over time and...
