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

Citation for package GD | Authors’ affiliations | 1. Introduction to GD package | 1.1 The model can be used to address following issues: | 1.2 The GD package makes following steps fast and easy: | 1.3 Start from the one-step function: gdm (Highly Recommended) | 1.4 Advanced models | 2. Geographical detector model | 3. Spatial data discretization | 3.1 Discretization with given parameters: disc | 3.2 Optimal discretization: optidisc | 4. Geographical detectors | 4.1 Factor detector: gd | 4.2 Risk detector: riskmean and gdrisk | 4.3 Interaction detector: gdinteract | 4.4 Ecological detector: gdeco | 5. Comparison of size effects of spatial unit | 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

Citation for package geosimilarity | 1. Introduction to geosimilarity package | 2. Spatial prediction using GOS model | 2.1 Characterizing geographical configurations | 2.2 Determining the optimal similarity | 2.3 Spatial prediction | 2.4 Model evaluation

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

Introduction to the localsp package | 1.1 This package can be used to address following issues: | 1.2 How to install this package: | 1.4 Example data in this package: | Case study: identifying local stratified determinants of Mountain glacier thickness | 2.1 Local extent exploration | 2.2 Local power of determinants of individual variables | Reference

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

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

An application of HSAR for asking prices in the municipality of Athens | Libraries | Reading the datasets | Create matrices used in the hsar function | Run the models

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

Improving Model Fitting Utilizing Geographical Complexity in Spatial Regression Tasks | Incorporating the geographical complexity of each independent variable into the model's explanatory variables | GCMLR 0.6215 0.6024 | Using spatial weight matrices that account for geographical complexity in spatial regression | GCSEM -1364 -1322 693.1 | geographical complexity-geographically weighted regression | GeoCGWR 0.836 0.8319 -1445 0.01923

Calculation methods of geographical complexity | Considering the geographical complexity with spatial local dependencies | Considering the geographical complexity with geographical configurations similarities | Firstly, calculate global similarity: | The geographic complexity is then calculated: | Cases for computing geographical complexity | Geographical Complexity of Individual Variables | Geographical Complexity of Multiple Variables

Introduction to SecDim package | Cross validation

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

Load package and data | visualize the mean and standard deviation of the sample in each stratum | Run sandwich model for the sim data use area weight | Run sandwich model for the sim data use population weight

Load data and package | Data pre-processing and variable selection | Determining the optimal similarity | Spatial prediction use GOS model

Load data and package. | Local Moran | Local Geary | Local Getis-Ord Statistics | Local Join Count Statistic

Load spatial data and nessary r package. | Contiguity Weights | Distance Based Weights | K-Nearest Neighbor Weights | Kernel Weights | Use st_kernel_weights() for Kernel Weights with afixedbandwidth | Use st_kernel_knn_weights() for Kernel Weights with adaptive bandwidth | Create Spatial Weights object by st_weights()