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

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Cited articles

Ahadnejad Reveshty, M., Heydari, M. T., and Tahmasebimoghaddam, H.: Spatial Analysis of the Factors Impacting on the Spread of Covid-19 in the Neighborhoods of Zanjan, Iran, Spatial Information Research, 32, 151–164, https://doi.org/10.1007/s41324-023-00550-0, 2023. a
Bivand, R. and Yu, D.: spgwr: Geographically Weighted Regression, CRAN [code], https://cran.r-project.org/package=spgwr (last access: 26 November 2024), 2023. a
Brunsdon, C., Fotheringham, A. S., and Charlton, M. E.: Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity, Geogr. Anal., 28, 281–298, https://doi.org/10.1111/j.1538-4632.1996.tb00936.x, 1996. a
Brunsdon, C., Fotheringham, A. S., and Charlton, M.: Some Notes on Parametric Significance Tests for Geographically Weighted Regression, J. Regional Sci., 39, 497–524, https://doi.org/10.1111/0022-4146.00146, 1999. a
Chen, Y., Wu, S., Wang, Y., Zhang, F., Liu, R., and Du, Z.: Satellite-Based Mapping of High-Resolution Ground-Level PM2.5 with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression, Remote Sens., 13, 1979, https://doi.org/10.3390/rs13101979, 2021. a
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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.
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