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rassta

rassta (raster-based spatial stratification algorithms) is a collection of algorithms for the spatial stratification of landscapes, sampling, and modeling of spatially-varying phenomena in R.

rassta offers a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena.

Installation

rassta is available from CRAN, so the current released version can be installed as follows:

install.packages("rassta")

To install the development version from github, please use:

remotes::install_github("bafuentes/rassta")

Cheat Sheet

Full documentation of rassta including some vignettes can be found here

Acknowledgments

rassta greatly benefits from past and current efforts to make spatial data analysis fully operational in R, which in turn have benefited from titans like GDAL, PROJ, GEOS, etc. Special thanks to the minds behind the terra, rgdal, rgeos, and sf packages, and those behind the other packages that rassta depends on as well.

Citation

A journal article describing the methods and theoretical background of rassta has been published by the R Journal and it is available here: https://doi.org/10.32614/RJ-2022-036. You can use citation("rassta") to get a BibTeX entry for LaTeX.

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Version

Install

install.packages('rassta')

Monthly Downloads

306

Version

1.0.6

License

AGPL (>= 3)

Maintainer

Bryan A. Fuentes

Last Published

August 19th, 2024

Functions in rassta (1.0.6)

plot3D

Interactive Maps of 3D surfaces
locations

Select Representative Sampling Locations for Stratification Units
select_functions

Select Constrained Univariate Distribution Functions
similarity

Calculate the Landscape Similarity to Stratification Units
engine

Predictive Modeling Engine
observation

Select the Representative Response Observation for Stratification Units
figure

Reproduce Figures from Fuentes et al. (n.d.)
predict_functions

Predict Distribution Functions Across Geographic Space
dummies

Create Dummy Layers from Categorical Raster Layers
signature

Calculate the Spatial Signature of Classification Units
som_pam

Rasterization of Self-Organizing Map and Partitioning Around Medoids
strata

Create Stratification Units
som_gap

Self-Organizing Map and Selection of k