Geographically Optimal Similarity

Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) . GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) . GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.

geosimilarity

Geographically Optimal Similarity

Please cite geosimilarity as:

Song, Y. (2022). Geographically Optimal Similarity. Mathematical Geosciences,55(3), 295–320. https://doi.org/10.1007/s11004-022-10036-8.

A BibTeX entry for LaTeX users is:

@article{song2022gos,
  title = {Geographically Optimal Similarity},
  author = {Song, Yongze},
  year = {2022},
  month = {nov},
  volume = {55},
  number = {3},
  pages = {295–320},
  journal = {Mathematical Geosciences},
  publisher = {Springer Science and Business Media LLC},
  doi = {10.1007/s11004-022-10036-8},
}

Installation

• Install from CRAN with:

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

• Install development binary version from R-universe with:

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

• Install development source version from GitHub with:

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