gdverse | Analysis of Spatial Stratified Heterogeneity • gdverse

Analysis of Spatial Stratified Heterogeneity

Overview

gdverse consolidates cutting-edge SSH methodologies into a unified toolkit, redefining spatial association measurement as the evolutionary successor to geodetector and GD in the R ecosystem.

Current models and functions provided by gdverse are:

Model	Function	Support
GD	`gd()`	✔️
OPGD	`opgd()`	✔️
GOZH	`gozh()`	✔️
LESH	`lesh()`	✔️
SPADE	`spade()`	✔️
IDSA	`idsa()`	✔️
RGD	`rgd()`	✔️
RID	`rid()`	✔️
SRSGD	`srsgd()`	✔️

Installation

Install from CRAN with:

install.packages("gdverse", dep = TRUE)

Install development binary version from R-universe with:

install.packages('gdverse',
                 repos = c("https://stscl.r-universe.dev",
                           "https://cloud.r-project.org"),
                 dep = TRUE)

Install development source version from GitHub with:

# install.packages("devtools")
devtools::install_github("stscl/gdverse",
                         build_vignettes = TRUE,
                         dep = TRUE)

✨ Please ensure that Rcpp is properly installed and the appropriate C++ compilation environment is configured in advance if you want to install gdverse from github.

✨ The gdverse package supports the use of robust discretization for the robust geographical detector and robust interaction detector. For details on using them, please refer to https://stscl.github.io/gdverse/articles/rgdrid.html.

Example

library(gdverse)
data("ndvi")
ndvi
## # A tibble: 713 × 7
##    NDVIchange Climatezone Mining Tempchange Precipitation    GDP Popdensity
##         <dbl> <chr>       <fct>       <dbl>         <dbl>  <dbl>      <dbl>
##  1    0.116   Bwk         low         0.256          237.  12.6      1.45  
##  2    0.0178  Bwk         low         0.273          214.   2.69     0.801 
##  3    0.138   Bsk         low         0.302          449.  20.1     11.5   
##  4    0.00439 Bwk         low         0.383          213.   0        0.0462
##  5    0.00316 Bwk         low         0.357          205.   0        0.0748
##  6    0.00838 Bwk         low         0.338          201.   0        0.549 
##  7    0.0335  Bwk         low         0.296          210.  11.9      1.63  
##  8    0.0387  Bwk         low         0.230          236.  30.2      4.99  
##  9    0.0882  Bsk         low         0.214          342. 241       20.0   
## 10    0.0690  Bsk         low         0.245          379.  42.0      7.50  
## # ℹ 703 more rows

OPGD model

discvar = names(ndvi)[-1:-3]
discvar
## [1] "Tempchange"    "Precipitation" "GDP"           "Popdensity"
ndvi_opgd = opgd(NDVIchange ~ ., data = ndvi, 
                 discvar = discvar, cores = 6)
ndvi_opgd
## ***   Optimal Parameters-based Geographical Detector     
##                 Factor Detector            
## 
## |   variable    | Q-statistic | P-value  |
## |:-------------:|:-----------:|:--------:|
## | Precipitation |  0.8693505  | 2.58e-10 |
## |  Climatezone  |  0.8218335  | 7.34e-10 |
## |  Tempchange   |  0.3330256  | 1.89e-10 |
## |  Popdensity   |  0.1990773  | 6.60e-11 |
## |    Mining     |  0.1411154  | 6.73e-10 |
## |      GDP      |  0.1004568  | 3.07e-10 |

GOZH model

g = gozh(NDVIchange ~ ., data = ndvi)
g
## ***   Geographically Optimal Zones-based Heterogeneity Model       
##                 Factor Detector            
## 
## |   variable    | Q-statistic | P-value  |
## |:-------------:|:-----------:|:--------:|
## | Precipitation | 0.87255056  | 4.52e-10 |
## |  Climatezone  | 0.82129550  | 2.50e-10 |
## |  Tempchange   | 0.33324945  | 1.12e-10 |
## |  Popdensity   | 0.22321863  | 3.00e-10 |
## |    Mining     | 0.13982859  | 6.00e-11 |
## |      GDP      | 0.09170153  | 3.96e-10 |

CITATION

Please cite gdverse as:

Lv, W., Lei, Y., Liu, F., Yan, J., Song, Y., Zhao, W., 2025. gdverse: An R Package for Spatial Stratified Heterogeneity Family. Transactions in GIS 29. https://doi.org/10.1111/tgis.70032

A BibTeX entry for LaTeX users is:

@article{lyu2025gdverse, 
    title={{gdverse}: An {R} Package for Spatial Stratified Heterogeneity Family}, 
    volume={29}, 
    ISSN={1467-9671},
    DOI={10.1111/tgis.70032},
    number={2}, 
    journal={Transactions in GIS}, 
    publisher={Wiley}, 
    author={Lv, Wenbo and Lei, Yangyang and Liu, Fangmei and Yan, Jianwu and Song, Yongze and Zhao, Wufan},
    year={2025}, 
    month={mar}
}

Reference

Lv, W., Lei, Y., Liu, F., Yan, J., Song, Y., Zhao, W., 2025. gdverse: An R Package for Spatial Stratified Heterogeneity Family. Transactions in GIS 29. https://doi.org/10.1111/tgis.70032.

Wang, J., Li, X., Christakos, G., Liao, Y., Zhang, T., Gu, X., Zheng, X., 2010. Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. International Journal of Geographical Information Science 24, 107–127. https://doi.org/10.1080/13658810802443457.

Song, Y., Wang, J., Ge, Y., Xu, C., 2020. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GIScience & Remote Sensing 57, 593–610. https://doi.org/10.1080/15481603.2020.1760434.

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., Meng, L., 2022. Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing 185, 111–128. https://doi.org/10.1016/j.isprsjprs.2022.01.009.

Li, Y., Luo, P., Song, Y., Zhang, L., Qu, Y., Hou, Z., 2023. A locally explained heterogeneity model for examining wetland disparity. International Journal of Digital Earth 16, 4533–4552. https://doi.org/10.1080/17538947.2023.2271883.

Cang, X., Luo, W., 2018. Spatial association detector (SPADE). International Journal of Geographical Information Science 32, 2055–2075. https://doi.org/10.1080/13658816.2018.1476693.

Song, Y., Wu, P., 2021. An interactive detector for spatial associations. International Journal of Geographical Information Science 35, 1676–1701. https://doi.org/10.1080/13658816.2021.1882680.

Zhang, Z., Song, Y., Wu, P., 2022. Robust geographical detector. International Journal of Applied Earth Observation and Geoinformation 109, 102782. https://doi.org/10.1016/j.jag.2022.102782.

Zhang, Z., Song, Y., Karunaratne, L., Wu, P., 2024. Robust interaction detector: A case of road life expectancy analysis. Spatial Statistics 59, 100814. https://doi.org/10.1016/j.spasta.2024.100814.

Bai, H., Li, D., Ge, Y., Wang, J., Cao, F., 2022. Spatial rough set-based geographical detectors for nominal target variables. Information Sciences 586, 525–539. https://doi.org/10.1016/j.ins.2021.12.019.

Wang, J., Zhang, T., Fu, B., 2016. A measure of spatial stratified heterogeneity. Ecological Indicators 67, 250–256. https://doi.org/10.1016/j.ecolind.2016.02.052.

Wang, J., Haining, R., Zhang, T., Xu, C., Hu, M., Yin, Q., Li, L., Zhou, C., Li, G., Chen, H., 2024. Statistical Modeling of Spatially Stratified Heterogeneous Data. Annals of the American Association of Geographers 114, 499–519. https://doi.org/10.1080/24694452.2023.2289982.