Hexiang Bai
Biographic Data
| ID | 3635625 |
|---|---|
| NAME | Hexiang Bai |
| GIVEN NAMES | Hexiang |
| FAMILY NAME | Bai |
| SIGNATURE | BAI H |
| AFFILIATIONS | Shanxi University |
| ORCID | 0000-0003-3098-9609 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 5 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Exploring Spatial Stratified Heterogeneity for Local Geometry Structures
Geometry structure modeling plays an increasingly important role in the era of artificial-intelligence-guided geoscience discovery. It poses challenges in detecting and quantifying spatial stratified heterogeneity, which is critical in capturing causal association and developing stratum-specific models in geoscience, as up-to-date methods mainly focus on value disparities and leave the corresponding spatial geometry structure aside. This article …
Spatial Association from the Perspective of Mutual Information
Measures of spatial association are important to reveal the spatial structures and patterns in geographical phenomena. They have utility for spatial interpolation, stochastic simulation, and causal inference, among others. Such measures are abundantly available for continuous spatial variables, whereas for categorical spatial variables they are less well developed. In this research, we developed a measure of spatial association for categorical sp…
Information Consistency-Based Measures for Spatial Stratified Heterogeneity
As a typical form of spatial heterogeneity, spatial stratified heterogeneity is widely observed in geographical phenomena. Although the q statistic provides a measure of spatial stratified heterogeneity using variance differences, it is not suitable for nominal target variables and neglects information differences between strata at higher order moments. Based on the mutual information and relative entropy between variables, two spatial stratified…
Spatial Association from the Perspective of Mutual Information
Measures of spatial association are important to reveal the spatial structures and patterns in geographical phenomena. They have utility for spatial interpolation, stochastic simulation, and causal inference, among others. Such measures are abundantly available for continuous spatial variables, whereas for categorical spatial variables they are less well developed. In this research, we developed a measure of spatial association for categorical sp…
Information Consistency-Based Measures for Spatial Stratified Heterogeneity
As a typical form of spatial heterogeneity, spatial stratified heterogeneity is widely observed in geographical phenomena. Although the q statistic provides a measure of spatial stratified heterogeneity using variance differences, it is not suitable for nominal target variables and neglects information differences between strata at higher order moments. Based on the mutual information and relative entropy between variables, two spatial stratified…
Spatial Association from the Perspective of Mutual Information
Measures of spatial association are important to reveal the spatial structures and patterns in geographical phenomena. They have utility for spatial interpolation, stochastic simulation, and causal inference, among others. Such measures are abundantly available for continuous spatial variables, whereas for categorical spatial variables they are less well developed. In this research, we developed a measure of spatial association for categorical sp…
Information Consistency-Based Measures for Spatial Stratified Heterogeneity
As a typical form of spatial heterogeneity, spatial stratified heterogeneity is widely observed in geographical phenomena. Although the q statistic provides a measure of spatial stratified heterogeneity using variance differences, it is not suitable for nominal target variables and neglects information differences between strata at higher order moments. Based on the mutual information and relative entropy between variables, two spatial stratified…
Exploring Spatial Stratified Heterogeneity for Local Geometry Structures
Geometry structure modeling plays an increasingly important role in the era of artificial-intelligence-guided geoscience discovery. It poses challenges in detecting and quantifying spatial stratified heterogeneity, which is critical in capturing causal association and developing stratum-specific models in geoscience, as up-to-date methods mainly focus on value disparities and leave the corresponding spatial geometry structure aside. This article …
Spatial and Panel Data Analysis (3 works) · Econometrics (2 works) · Land Use and Ecosystem Services (2 works) · Mathematics (2 works) · Spatial analysis (2 works) · Spatial ecology (2 works) · Spatial variability (2 works) · Statistics (2 works) · 3D Modeling in Geospatial Applications (1 work) · Artificial Intelligence (1 work)