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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 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.
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)
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)
Additional Functions for 'GeoPAT' 2
Supports analysis of spatial data processed with the 'GeoPAT' 2 software < http://sil.uc.edu/cms/index.php?id=geopat2>. Available features include creation of a grid based on the 'GeoPAT' 2 grid header file and reading a 'GeoPAT' 2 text outputs.
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)
Analysis of Aerobiological Data
Supports analysis of aerobiological data.
Available features include determination of pollen season limits,
replacement of outliers (Kasprzyk and Walanus (2014)
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)