Found 23 packages in 0.01 seconds
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 gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported. There is a also support for vector data operations such as intersections. See the manual and tutorials on < https://rspatial.org/> to get started.
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)
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.
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.
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 (
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. 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.
Creates Co-Occurrence Matrices for 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)