Package: geocomplexity 0.2.0
Wenbo Lv
geocomplexity: Mitigating Spatial Bias Through Geographical Complexity
The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.
Authors:
geocomplexity_0.2.0.tar.gz
geocomplexity_0.2.0.tar.gz(r-4.5-noble)geocomplexity_0.2.0.tar.gz(r-4.4-noble)
geocomplexity_0.2.0.tgz(r-4.4-emscripten)geocomplexity_0.2.0.tgz(r-4.3-emscripten)
geocomplexity.pdf |geocomplexity.html✨
geocomplexity/json (API)
NEWS
| # Install 'geocomplexity' in R: |
| install.packages('geocomplexity', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ausgis/geocomplexity/issues
Last updated 22 days agofrom:9bfe040b18. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 04 2024 |
| R-4.5-linux-x86_64 | OK | Oct 04 2024 |
Exports:%>%geocd_rastergeocd_swmgeocd_vectorgeocs_rastergeocs_swmgeocs_vectorgwr_geoc
Dependencies:bootclassclassIntcliDBIdeldirdplyre1071fansigenericsgeosphereglueKernSmoothlatticelifecyclemagrittrMASSpillarpkgconfigproxypurrrR6RcppRcppArmadillorlangs2sdsfunsfspspDataspdepterratibbletidyselectunitsutf8vctrswithrwk
