Geographically Optimal Similarity
Overview
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) doi:10.1007/s11004-022-10036-8. GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) doi:10.1080/19475683.2018.1534890. GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.
- Correct maintainer surname spelling from
LvtoLyufor pinyin compliance.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 6 (1 internal)
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 75%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 33%
Downloads
Repository
- PRs merged (all time)
- 100%
- Median days to close, 50 most recently updated closed issues
- not measured
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
Releases over time
PRs over time
8 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/ausgis/geosimilarity on 2026-10-21.
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pkgdown site last built 2026-03-29
Dependencies
Code & Tests
Datasets
People & History
Maintainer since 3.0 (2024), previously Yongze Song (1.1 to 2.2)
Package metadata
- First published
- 2022-05-17
- Total releases
- 10 / 4 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 915 KB / 2 files
- Download size
- 1.4 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("geosimilarity")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1007/s11004-022-10036-8.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.