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.
Version: 0.2.1 Depends: R (≥ 4.1.0) Imports: dplyr, magrittr, purrr, sdsfun, sf, stats, terra, tibble LinkingTo: Rcpp, RcppArmadillo Suggests: ggplot2, knitr, Rcpp, RcppArmadillo, rmarkdown, viridis Published: 2024-11-11 DOI: 10.32614/CRAN.package.geocomplexity Author: Wenbo Lv [aut, cre, cph], Yongze Song [aut], Zehua Zhang [aut] Maintainer: Wenbo Lv <lyu.geosocial at gmail.com> BugReports: https://github.com/ausgis/geocomplexity/issues License: GPL-3 URL: https://ausgis.github.io/geocomplexity/, https://github.com/ausgis/geocomplexity NeedsCompilation: yes Citation: geocomplexity citation info Materials: README NEWS CRAN checks: geocomplexity results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=geocomplexity to link to this page.
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