optim.spatial: Optimization of spatial sampling.
In BAT: Biodiversity Assessment Tools
optim.spatial R Documentation
Optimization of spatial sampling.
Description
Optimization of sampling site distribution in space based on environmental (or other) variables.
Usage
optim.spatial(layers, n, latlong = TRUE, clusterMap = TRUE)
Arguments
layers
A SpatRaster object from package terra.
n
The number of intended sampling sites (clusters).
latlong
Boolean indicating whether latitude and longitude should be taken into account when clustering.
clusterMap
Boolean indicating whether to build a new raster with clusters.
Details
Optimizing the selection of sampling sites often requires maximizing the environmental diversity covered by them.
One possible solution to this problem, here adopted, is performing a k-means clustering using environmental data and choosing the sites closest to the multidimensional environmental centroid of each cluster for sampling (Jimenez-Valverde & Lobo 2004)
Value
Either a matrix of cells x clusters (also indicating distance to centroid, longitude and latitude of each cell) or a list with such matrix plus the clusterMap.
References
Jimenez-Valverde, A., & Lobo, J. M. (2004) Un metodo sencillo para seleccionar puntos de muestreo con el objetivo de inventariar taxones hiperdiversos: el caso practico de las familias Araneidae y Thomisidae (Araneae) en la comunidad de Madrid, Espana. Ecologia, 18: 297-305.
BAT documentation built on May 29, 2024, 7:35 a.m.
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| optim.spatial | R Documentation |
Optimization of spatial sampling.
Description
Optimization of sampling site distribution in space based on environmental (or other) variables.
Usage
optim.spatial(layers, n, latlong = TRUE, clusterMap = TRUE)
Arguments
layers |
A SpatRaster object from package terra. |
n |
The number of intended sampling sites (clusters). |
latlong |
Boolean indicating whether latitude and longitude should be taken into account when clustering. |
clusterMap |
Boolean indicating whether to build a new raster with clusters. |
Details
Optimizing the selection of sampling sites often requires maximizing the environmental diversity covered by them. One possible solution to this problem, here adopted, is performing a k-means clustering using environmental data and choosing the sites closest to the multidimensional environmental centroid of each cluster for sampling (Jimenez-Valverde & Lobo 2004)
Value
Either a matrix of cells x clusters (also indicating distance to centroid, longitude and latitude of each cell) or a list with such matrix plus the clusterMap.
References
Jimenez-Valverde, A., & Lobo, J. M. (2004) Un metodo sencillo para seleccionar puntos de muestreo con el objetivo de inventariar taxones hiperdiversos: el caso practico de las familias Araneidae y Thomisidae (Araneae) en la comunidad de Madrid, Espana. Ecologia, 18: 297-305.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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