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Additional Functions for 'GeoPAT' 2
Supports analysis of spatial data processed with the 'GeoPAT' 2 software < https://github.com/Nowosad/geopat2>. Available features include creation of a grid based on the 'GeoPAT' 2 grid header file and reading a 'GeoPAT' 2 text outputs.
Local Pattern Analysis
Describes spatial patterns of categorical raster data for
any defined regular and irregular areas.
Patterns are described quantitatively using built-in signatures
based on co-occurrence matrices but also allows for
any user-defined functions.
It enables spatial analysis such as search, change detection,
and clustering to be performed on spatial patterns (Nowosad (2021)
Intra- and Inter-Regional Similarity
Calculates intra-regional and inter-regional similarities based on user-provided
spatial vector objects (regions) and spatial raster objects (cells with values).
Implemented metrics include inhomogeneity, isolation
(Haralick and Shapiro (1985)
Pattern-Based Zoneless Method for Analysis and Visualization of Racial Topography
Implements a computational framework for a pattern-based,
zoneless analysis, and visualization of (ethno)racial topography
(Dmowska, Stepinski, and Nowosad (2020)
Spatial Association Between Regionalizations
Calculates a degree of spatial association between regionalizations
or categorical maps using the information-theoretical V-measure
(Nowosad and Stepinski (2018)
Analysis of Aerobiological Data
Supports analysis of aerobiological data.
Available features include determination of pollen season limits,
replacement of outliers (Kasprzyk and Walanus (2014)
Check Color Palettes for Problems with Color Vision Deficiency
Compare color palettes with simulations of color vision deficiencies - deuteranopia, protanopia, and tritanopia. It includes calculation of distances between colors, and creating summaries of differences between a color palette and simulations of color vision deficiencies. This work was inspired by the blog post at < http://www.vis4.net/blog/2018/02/automate-colorblind-checking/>.
Boltzmann Entropy for Spatial Data
Calculates several entropy metrics for spatial data
inspired by Boltzmann's entropy formula.
It includes metrics introduced by Cushman for landscape mosaics
(Cushman (2015)
Boltzmann Entropy of a Landscape Gradient
Calculates the Boltzmann entropy of a landscape gradient.
This package uses the analytical method created by Gao, P., Zhang, H.
and Li, Z., 2018 (
'caret' Applications for Spatial-Temporal Models
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018)