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) , Jasiewicz et al. (2018) ), and distinction (Nowosad (2021) ).

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) ). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of 'landscape’ used in the domain of landscape ecology.

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) ). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) ).

Analysis of Aerobiological Data

Supports analysis of aerobiological data. Available features include determination of pollen season limits, replacement of outliers (Kasprzyk and Walanus (2014) ), calculation of growing degree days (Baskerville and Emin (1969) ), and determination of the base temperature for growing degree days (Yang et al. (1995)

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) ), and landscape gradients and point patterns (Cushman (2021) ); by Zhao and Zhang for landscape mosaics (Zhao and Zhang (2019) ); and by Gao et al. for landscape gradients (Gao et al. (2018) ; Gao and Li (2019) ).

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 () and by Gao, P. and Li, Z., 2019 (). It also extend the original ideas by allowing calculations on data with missing values.

'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) ; Meyer et al. (2019) ; Meyer and Pebesma (2021) ; Milà et al. (2022) ; Meyer and Pebesma (2022) ; Linnenbrink et al. (2023) ; Schumacher et al. (2024) . The package is described in detail in Meyer et al. (2024) .