Ising Model for Spatial Data

Performs simulations of binary spatial raster data using the Ising model (Ising (1925) ; Onsager (1944) ). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) ).

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) ).

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) ).

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)

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) ).

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 < https://www.datawrapper.de/blog/colorblind-check>.

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.

Datasets for Spatial Analysis

Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON and GeoPackage, but from version 2.3.4, no longer ESRI Shapefile - use GeoPackage instead. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.