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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Dec 2020
Model description paper |
| 03 Dec 2020
| 03 Dec 2020
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
Xiang Que
Computer and Information College, Fujian Agriculture and Forestry
University, Fuzhou, Fujian, China
Department of Computer Science, University of Idaho, 875 Perimeter
Drive MS 1010, Moscow, ID 83844-1010, USA
Department of Computer Science, University of Idaho, 875 Perimeter
Drive MS 1010, Moscow, ID 83844-1010, USA
Department of Computer Science, University of Idaho, 875 Perimeter
Drive MS 1010, Moscow, ID 83844-1010, USA
Qiyu Chen
School of Computer Science, China University of Geosciences
(Wuhan), 388 Lumo Road, Wuhan 430074, China
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Total article views: 4,626 (including HTML, PDF, and XML)
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Total article views: 1,978 (including HTML, PDF, and XML)
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Cited
18 citations as recorded by crossref.
- Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020 R. Ji et al. 10.1016/j.jenvman.2024.122464
- Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns Z. Wang et al. 10.1016/j.ecoinf.2024.102870
- A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas J. Qi et al. 10.1016/j.scitotenv.2023.163981
- Exploring Urban Heat Distribution and Thermal Comfort Exposure Using Spatiotemporal Weighted Regression (STWR) R. Chen et al. 10.3390/buildings14061883
- A SPATIOTEMPORAL-AWARE WEIGHTING SCHEME FOR IMPROVING CLIMATE MODEL ENSEMBLE PREDICTIONS M. Fan et al. 10.1615/JMachLearnModelComput.2022046715
- Energy Carbon Emission Reduction Based on Spatiotemporal Heterogeneity: A County-Level Empirical Analysis in Guangdong, Fujian, and Zhejiang Y. Lai et al. 10.3390/su17073218
- How does cultivated land fragmentation affect soil erosion: Evidence from the Yangtze River Basin in China J. Zeng et al. 10.1016/j.jenvman.2024.121020
- How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China C. Wei et al. 10.3390/land14010059
- Analysis of wildfires and their extremes via spatial quantile autoregressive model J. Lee et al. 10.1007/s10687-023-00462-0
- Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model Q. Chen et al. 10.1038/s41598-024-54630-8
- Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas X. Zou et al. 10.3390/su16083260
- A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors J. Qi et al. 10.1016/j.envsoft.2024.106289
- Modeling the spatiotemporal heterogeneity and changes of slope stability in rainfall-induced landslide areas X. Que et al. 10.1007/s12145-023-01165-7
- Multi-scale analysis of urban forests and socioeconomic patterns in a desert city, Phoenix, Arizona Z. Wang et al. 10.1038/s41598-024-74208-8
- Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment L. Chen et al. 10.3390/rs16101678
- Land Cover Impacts on Surface Temperatures: Evaluation and Application of a Novel Spatiotemporal Weighted Regression Approach C. Fan et al. 10.3390/ijgi12040151
- Trends and drivers of pedestrian mobility in Barcelona: A fine-grained study across its commercial tissue C. Rames et al. 10.1016/j.cities.2024.105655
- Parallel computing for Fast Spatiotemporal Weighted Regression X. Que et al. 10.1016/j.cageo.2021.104723
18 citations as recorded by crossref.
- Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020 R. Ji et al. 10.1016/j.jenvman.2024.122464
- Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns Z. Wang et al. 10.1016/j.ecoinf.2024.102870
- A spatiotemporally weighted intelligent method for exploring fine-scale distributions of surface dissolved silicate in coastal seas J. Qi et al. 10.1016/j.scitotenv.2023.163981
- Exploring Urban Heat Distribution and Thermal Comfort Exposure Using Spatiotemporal Weighted Regression (STWR) R. Chen et al. 10.3390/buildings14061883
- A SPATIOTEMPORAL-AWARE WEIGHTING SCHEME FOR IMPROVING CLIMATE MODEL ENSEMBLE PREDICTIONS M. Fan et al. 10.1615/JMachLearnModelComput.2022046715
- Energy Carbon Emission Reduction Based on Spatiotemporal Heterogeneity: A County-Level Empirical Analysis in Guangdong, Fujian, and Zhejiang Y. Lai et al. 10.3390/su17073218
- How does cultivated land fragmentation affect soil erosion: Evidence from the Yangtze River Basin in China J. Zeng et al. 10.1016/j.jenvman.2024.121020
- How Do Temporal and Geographical Kernels Differ in Reflecting Regional Disparities? Insights from a Case Study in China C. Wei et al. 10.3390/land14010059
- Analysis of wildfires and their extremes via spatial quantile autoregressive model J. Lee et al. 10.1007/s10687-023-00462-0
- Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model Q. Chen et al. 10.1038/s41598-024-54630-8
- Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas X. Zou et al. 10.3390/su16083260
- A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors J. Qi et al. 10.1016/j.envsoft.2024.106289
- Modeling the spatiotemporal heterogeneity and changes of slope stability in rainfall-induced landslide areas X. Que et al. 10.1007/s12145-023-01165-7
- Multi-scale analysis of urban forests and socioeconomic patterns in a desert city, Phoenix, Arizona Z. Wang et al. 10.1038/s41598-024-74208-8
- Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment L. Chen et al. 10.3390/rs16101678
- Land Cover Impacts on Surface Temperatures: Evaluation and Application of a Novel Spatiotemporal Weighted Regression Approach C. Fan et al. 10.3390/ijgi12040151
- Trends and drivers of pedestrian mobility in Barcelona: A fine-grained study across its commercial tissue C. Rames et al. 10.1016/j.cities.2024.105655
- Parallel computing for Fast Spatiotemporal Weighted Regression X. Que et al. 10.1016/j.cageo.2021.104723
Latest update: 19 May 2025
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.
This paper presents a spatiotemporal weighted regression (STWR) model for exploring...
