Articles | Volume 11, issue 6
https://doi.org/10.5194/gmd-11-2525-2018
© Author(s) 2018. 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-11-2525-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
25 Jun 2018
Model description paper |
| 25 Jun 2018
| 25 Jun 2018
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
Daojun Zhang
CORRESPONDING AUTHOR
College of Economics and Management, Northwest A&F University,
Yangling 712100, China
Center for Resource Economics and Environment Management, Northwest
A&F University, Yangling 712100, China
Na Ren
College of Economics and Management, Northwest A&F University,
Yangling 712100, China
Xianhui Hou
CORRESPONDING AUTHOR
College of Economics and Management, Northwest A&F University,
Yangling 712100, China
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Cited
27 citations as recorded by crossref.
- Comparing the long‐term effects of artificial and natural vegetation restoration strategies: A case‐study of Wuqi and its adjacent counties in northern China X. Xu & D. Zhang 10.1002/ldr.4018
- Mineral prospectivity mapping using attention-based convolutional neural network Q. Li et al. 10.1016/j.oregeorev.2023.105381
- Evaluating the vegetation restoration potential achievement of ecological projects: A case study of Yan’an, China X. Xu et al. 10.1016/j.landusepol.2019.104293
- Contribution of ecological policies to vegetation restoration: A case study from Wuqi County in Shaanxi Province, China D. Zhang et al. 10.1016/j.landusepol.2018.02.020
- A novel framework for evaluating the effect of vegetation restoration via grazing exclusion by fencing: A case‐study from the Qinghai–Tibet Plateau W. Yang et al. 10.1002/ldr.4338
- Using spatial autologistic regression for predicting urban growth R. Crespo et al. 10.1080/14498596.2022.2127951
- Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach L. Wang et al. 10.1016/j.jag.2024.103746
- Assessing the coordination of ecological and agricultural goals during ecological restoration efforts: A case study of Wuqi County, Northwest China D. Zhang et al. 10.1016/j.landusepol.2019.01.001
- Trade-offs in land-use competition and sustainable land development in the North China Plain G. Jin et al. 10.1016/j.techfore.2019.01.004
- Evaluating the vegetation restoration sustainability of ecological projects: A case study of Wuqi County in China D. Zhang et al. 10.1016/j.jclepro.2020.121751
- Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space J. Huang et al. 10.1007/s11053-019-09560-y
- Spatially-Weighted Factor Analysis for Extraction of Source-Oriented Mineralization Feature in 3D Coordinates of Surface Geochemical Signal S. Esmaeiloghli et al. 10.1007/s11053-021-09933-2
- Efficiency Measurement and Influencing Factors of Ecological Compensation: A Case Study from Wuqi and Zhidan on the Loess Plateau Y. Zhang & D. Zhang 10.1007/s11053-021-09947-w
- Mapping Mineral Prospectivity via Semi-supervised Random Forest J. Wang et al. 10.1007/s11053-019-09510-8
- Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest N. Yang et al. 10.1007/s11053-022-10143-7
- Spatio-Geologically Informed Fuzzy Classification: An Innovative Method for Recognition of Mineralization-Related Patterns by Integration of Elemental, 3D Spatial, and Geological Information S. Esmaeiloghli et al. 10.1007/s11053-020-09798-x
- Modified Weights-of-Evidence Modeling with Example of Missing Geochemical Data D. Zhang & F. Agterberg 10.1155/2018/7945960
- Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping T. Li et al. 10.1007/s11053-020-09742-z
- An Improved GWR Approach for Exploring the Anisotropic Influence of Ore-Controlling Factors on Mineralization in 3D Space J. Huang et al. 10.1007/s11053-021-09954-x
- Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model X. Du et al. 10.3390/ijgi10110766
- Identifying the Association of Time-Averaged Serum Albumin Levels with Clinical Factors among Patients on Hemodialysis Using Whale Optimization Algorithm C. Yang et al. 10.3390/math10071030
- Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model N. Lin et al. 10.1007/s11053-019-09509-1
- Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran M. Hajihosseinlou et al. 10.1016/j.eswa.2023.121668
- Weighted Photolineaments Factor (WPF): An Enhanced Method to Generate a Predictive Structural Evidential Map with Low Uncertainty, a Case Study in Chahargonbad Area, Iran G. Elyasi et al. 10.1007/s11053-020-09658-8
- A novel similar habitat potential model based on sliding‐window technique for vegetation restoration potential mapping D. Zhang et al. 10.1002/ldr.3494
- Analysis of spatial variability in factors contributing to vegetation restoration in Yan'an, China D. Zhang et al. 10.1016/j.ecolind.2020.106278
- Optimization of urban land-use structure in China's rapidly developing regions with eco-environmental constraints X. Luo et al. 10.1016/j.pce.2019.03.001
27 citations as recorded by crossref.
- Comparing the long‐term effects of artificial and natural vegetation restoration strategies: A case‐study of Wuqi and its adjacent counties in northern China X. Xu & D. Zhang 10.1002/ldr.4018
- Mineral prospectivity mapping using attention-based convolutional neural network Q. Li et al. 10.1016/j.oregeorev.2023.105381
- Evaluating the vegetation restoration potential achievement of ecological projects: A case study of Yan’an, China X. Xu et al. 10.1016/j.landusepol.2019.104293
- Contribution of ecological policies to vegetation restoration: A case study from Wuqi County in Shaanxi Province, China D. Zhang et al. 10.1016/j.landusepol.2018.02.020
- A novel framework for evaluating the effect of vegetation restoration via grazing exclusion by fencing: A case‐study from the Qinghai–Tibet Plateau W. Yang et al. 10.1002/ldr.4338
- Using spatial autologistic regression for predicting urban growth R. Crespo et al. 10.1080/14498596.2022.2127951
- Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach L. Wang et al. 10.1016/j.jag.2024.103746
- Assessing the coordination of ecological and agricultural goals during ecological restoration efforts: A case study of Wuqi County, Northwest China D. Zhang et al. 10.1016/j.landusepol.2019.01.001
- Trade-offs in land-use competition and sustainable land development in the North China Plain G. Jin et al. 10.1016/j.techfore.2019.01.004
- Evaluating the vegetation restoration sustainability of ecological projects: A case study of Wuqi County in China D. Zhang et al. 10.1016/j.jclepro.2020.121751
- Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space J. Huang et al. 10.1007/s11053-019-09560-y
- Spatially-Weighted Factor Analysis for Extraction of Source-Oriented Mineralization Feature in 3D Coordinates of Surface Geochemical Signal S. Esmaeiloghli et al. 10.1007/s11053-021-09933-2
- Efficiency Measurement and Influencing Factors of Ecological Compensation: A Case Study from Wuqi and Zhidan on the Loess Plateau Y. Zhang & D. Zhang 10.1007/s11053-021-09947-w
- Mapping Mineral Prospectivity via Semi-supervised Random Forest J. Wang et al. 10.1007/s11053-019-09510-8
- Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest N. Yang et al. 10.1007/s11053-022-10143-7
- Spatio-Geologically Informed Fuzzy Classification: An Innovative Method for Recognition of Mineralization-Related Patterns by Integration of Elemental, 3D Spatial, and Geological Information S. Esmaeiloghli et al. 10.1007/s11053-020-09798-x
- Modified Weights-of-Evidence Modeling with Example of Missing Geochemical Data D. Zhang & F. Agterberg 10.1155/2018/7945960
- Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping T. Li et al. 10.1007/s11053-020-09742-z
- An Improved GWR Approach for Exploring the Anisotropic Influence of Ore-Controlling Factors on Mineralization in 3D Space J. Huang et al. 10.1007/s11053-021-09954-x
- Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model X. Du et al. 10.3390/ijgi10110766
- Identifying the Association of Time-Averaged Serum Albumin Levels with Clinical Factors among Patients on Hemodialysis Using Whale Optimization Algorithm C. Yang et al. 10.3390/math10071030
- Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model N. Lin et al. 10.1007/s11053-019-09509-1
- Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran M. Hajihosseinlou et al. 10.1016/j.eswa.2023.121668
- Weighted Photolineaments Factor (WPF): An Enhanced Method to Generate a Predictive Structural Evidential Map with Low Uncertainty, a Case Study in Chahargonbad Area, Iran G. Elyasi et al. 10.1007/s11053-020-09658-8
- A novel similar habitat potential model based on sliding‐window technique for vegetation restoration potential mapping D. Zhang et al. 10.1002/ldr.3494
- Analysis of spatial variability in factors contributing to vegetation restoration in Yan'an, China D. Zhang et al. 10.1016/j.ecolind.2020.106278
- Optimization of urban land-use structure in China's rapidly developing regions with eco-environmental constraints X. Luo et al. 10.1016/j.pce.2019.03.001
Latest update: 23 Apr 2024
Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Geographically weighted regression is a widely used method to deal with spatial heterogeneity,...
