Package: geocomplexity 0.3.0

Wenbo Lyu

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:Wenbo Lyu [aut, cre, cph], Yongze Song [aut], Zehua Zhang [aut]

geocomplexity_0.3.0.tar.gz
geocomplexity_0.3.0.zip(r-4.7-x86_64)geocomplexity_0.3.0.zip(r-4.7-arm64)geocomplexity_0.3.0.zip(r-4.6-x86_64)geocomplexity_0.3.0.zip(r-4.6-arm64)
geocomplexity_0.3.0.tar.gz(r-4.7-arm64)geocomplexity_0.3.0.tar.gz(r-4.7-x86_64)geocomplexity_0.3.0.tar.gz(r-4.6-arm64)geocomplexity_0.3.0.tar.gz(r-4.6-x86_64)
geocomplexity_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
geocomplexity/json (API)

# 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

Pkgdown/docs site:https://ausgis.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.65 score 1 packages 15 scripts 462 downloads 8 exports 39 dependencies

Last updated from:15ac1d1a44. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK157
linux-devel-x86_64OK181
source / vignettesOK306
linux-release-arm64OK149
linux-release-x86_64OK170
windows-devel-arm64OK276
windows-devel-x86_64OK229
windows-release-arm64OK256
windows-release-x86_64OK197
wasm-releaseOK196

Exports:%>%geocd_rastergeocd_swmgeocd_vectorgeocs_rastergeocs_swmgeocs_vectorgwr_geoc

Dependencies:bootclassclassIntcliDBIdeldirdigestdplyre1071genericsgeosphereglueKernSmoothlatticelifecyclemagrittrpanderpillarpkgconfigproxypurrrR6RcppRcppArmadillorlangs2sdsfunsfspspDataspdepterratibbletidyselectunitsutf8vctrswithrwk

GeoComplexity Calculation
Calculation methods of geographical complexity | Considering the geographical complexity with spatial local dependencies | Considering the geographical complexity with geographical configurations similarities | Firstly, calculate global similarity: | The geographic complexity is then calculated: | Cases for computing geographical complexity | Geographical Complexity of Individual Variables | Geographical Complexity of Multiple Variables
Rendered fromgcc.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2024-09-25

GeoComplexity Application
Improving Model Fitting Utilizing Geographical Complexity in Spatial Regression Tasks | Incorporating the geographical complexity of each independent variable into the model's explanatory variables | GCMLR 0.6215 0.6024 | Using spatial weight matrices that account for geographical complexity in spatial regression | GCSEM -1364 -1322 693.1 | geographical complexity-geographically weighted regression | GeoCGWR 0.836 0.8319 -1445 0.01923
Rendered fromgca.Rmdusingknitr::rmarkdown

Last update: 2024-09-25
Started: 2024-09-25