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geocomplexity

Mitigating Spatial Bias Through Geographical Complexity

v0.2.1 · Nov 11, 2024 · GPL-3

Description

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.

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NOTE r-oldrel-macos-arm64

installed package size

  installed size is  7.4Mb
  sub-directories of 1Mb or more:
    libs   6.1Mb
NOTE r-oldrel-macos-x86_64

installed package size

  installed size is  7.2Mb
  sub-directories of 1Mb or more:
    libs   6.0Mb
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Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-oldrel-macos-arm64

installed package size

  installed size is  7.4Mb
  sub-directories of 1Mb or more:
    libs   6.1Mb
NOTE r-oldrel-macos-x86_64

installed package size

  installed size is  7.2Mb
  sub-directories of 1Mb or more:
    libs   6.0Mb

Dependency Network

Dependencies Reverse dependencies dplyr magrittr purrr sdsfun sf terra tibble geocomplexity

Version History

new 0.2.1 Mar 9, 2026