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, ESRI Shapefile and GeoPackage. 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.

Geographic Data Analysis and Modeling

Reading, writing, manipulating, analyzing and modeling of spatial data. The package implements basic and high-level functions for raster data and for vector data operations such as intersections. See the manual and tutorials on < https://rspatial.org/> to get started.

Thematic Maps

Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.

Similarity and Distance Quantification Between Probability Functions

Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) ). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

'CARTOColors' Palettes

Provides color schemes for maps and other graphics designed by 'CARTO' as described at < https://carto.com/carto-colors/>. It includes four types of palettes: aggregation, diverging, qualitative, and quantitative.

Creates Co-Occurrence Matrices of Spatial Data

Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) ).

Color Palettes Inspired by Works at the Metropolitan Museum of Art

Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains over 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using the '{colorblindcheck} package by Jakub Nowosad'< https://jakubnowosad.com/colorblindcheck/>.

Superpixels of Spatial Data

Creates superpixels based on input spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) ), and readapts it to work with arbitrary dissimilarity measures.

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) ).

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) ).