Overview | Installation | Data | Columbus OH spatial analysis dataset | County-level population density in mainland China | Farmland NPP and related variables in mainland China | Usage | References

Methodological Background | Usage examples | Example of spatial vector data | Example of spatial raster data

Introduction | Utility Functions | Example Cases | Air Pollution and Cardiovascular Health in Hong Kong | Population Density and Its Drivers in Mainland China | Influence of Climatic and Topographic Factors on Net Primary Productivity (NPP) in Mainland China

Introduction | Coupling Degree | Modified Coupling Degree | Modification by $\text{wang}^{1}$. | Modification by $\text{fan}^{2}$. | Comprehensive Development Index | Coordination Degree | Metacoupling Perspective | Example Cases | Install necessary packages and load data | Analysis of coupling coordination degree | A Meta-Coupling Analysis | References

Model principles | Usage examples

Methodological Background | Usage examples | Example of spatial vector data | Example of spatial raster data

Methodological Background | Usage examples

Methodological Background | Usage examples | Example of spatial vector data | Example of spatial raster data

Methodological Background | Usage examples | Example of spatial vector data | Example of spatial raster data

Methodological Background | Usage examples | Example of spatial vector data | Example of spatial raster data

Model principles | Usage examples | Example of spatial vector data | Example of spatial raster data

1. Introduction to the tEDM package | 2. Example data in the tEDM package | Hong Kong Air Pollution and Cardiovascular Admissions | US County-Level Carbon Emissions Dataset | COVID-19 Infection Counts in Japan | 3. Case studies of the tEDM package | Air Pollution and Cardiovascular Health in Hong Kong | US County Carbon Emissions and Temperature Dynamics | COVID-19 Spread Across Japanese Prefectures | Reference

1. Introduction to sshicm package | 1.1 The sshicm package can be used to address following issues: | 1.2 Example data in the sshicm package | baltim data | cinc data | 1.3 Functions in the sshicm package | Two functions for vector-type inputs of dependent and independent variables. | Regression-style data frame modeling function | 2. The principle of measuring spatial stratified heterogeneity based on information consistency | 2.1 When the dependent variable is a continuous variable: | 2.2 When the dependent variable is a nominal variable: | 3. Examples of the sshicm package | Reference

Load data and package | Construct spatial Weight | IDSA modeling

Load data and package | Determine optimal discretization parameters | Run geodetector | You can also use opgd() in one time to get result above.

Load data and package | Run RGD | Run RID

Processing data | Comparison of Size Effect of Spatial Units based on OPGD model | Comparison of Size Effect of Spatial Units based on GOZH model

Load data and package | Univariate power of determinants detection | Variable interaction detection | Variable interaction contribution

Install and load R packages | Spatial pattern correlation

Introduction | Utility Functions | Example Cases | Air Pollution and Cardiovascular Health in Hong Kong | Population Density and Its Drivers in Mainland China | Influence of Climatic and Topographic Factors on Net Primary Productivity (NPP) in Mainland China

Load data and package | Spatial Autocorrelation of Depression Prevelence | OPGD modeling | Spatial Weight Matrix | SPADE modeling

Load package and pre-processing data. | Factor detector | Interaction detector | Risk detector | Ecological detector | Running four basic geodetectors simultaneously

1. The principle of the information consistency-based measures | 2. Example

1. The principle of the information-theoretical V-measure | 2. Example

Install and load R packages | SESP With Linear Regression | SESP With Spatial Lag Regression | SESP With Spatial Error Regression | SESP With Spatial Durbin Regression | SESP With Spatial Autoregressive Combined | SESP With Geographically Weighted Regression | Results of optimal spatial discretization

Install necessary R packages | The spatial autocorrelation of the example data | Q values corresponding to spatial implicit discretization | A Monte Carlo simulation experiment demonstrating the necessity of spatial explicit discretization

1. Q Values | 2. Linear Regression Model with Dummy Variables | 3. Behavior of Dummy Variables | 4. Predicted Value for a Category | 5. Ordinary Least Squares (OLS) Estimation | 6. Conclusion | 7. Real-world example | Factor detector | Interaction detector

Available data sets | Commonly Used CRS for Drawing Maps of China | Region-specific CRS Transformation in China