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geocomplexity  

Mitigating Spatial Bias Through Geographical Complexity
View on CRAN: Click here


Download and install geocomplexity package within the R console
Install from CRAN:
install.packages("geocomplexity")

Install from Github:
library("remotes")
install_github("cran/geocomplexity")

Install by package version:
library("remotes")
install_version("geocomplexity", "0.2.0")



Attach the package and use:
library("geocomplexity")
Maintained by
Wenbo Lv
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-24
Latest Update: 2024-09-24
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.
How to cite:
Wenbo Lv (2024). geocomplexity: Mitigating Spatial Bias Through Geographical Complexity. R package version 0.2.0, https://cran.r-project.org/web/packages/geocomplexity. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2024-09-24 21:10), 0.2.0 (2024-10-03 10:00)
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Complete documentation for geocomplexity
Functions, R codes and Examples using the geocomplexity R package
Full geocomplexity package functions and examples
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