Geometric Shadow Calculations

Functions for calculating: (1) shadow height, (2) logical shadow flag, (3) shadow footprint, (4) Sky View Factor and (5) radiation load. Basic required inputs include a polygonal layer of obstacle outlines along with their heights (i.e. "extruded polygons"), sun azimuth and sun elevation. The package also provides functions for related preliminary calculations: breaking polygons into line segments, determining azimuth of line segments, shifting segments by azimuth and distance, constructing the footprint of a line-of-sight between an observer and the sun, and creating a 3D grid covering the surface area of extruded polygons.

Antarctic Spatial Data Manipulation

Loads and creates spatial data, including layers and tools that are relevant to the activities of the Commission for the Conservation of Antarctic Marine Living Resources. Provides two categories of functions: load functions and create functions. Load functions are used to import existing spatial layers from the online CCAMLR GIS such as the ASD boundaries. Create functions are used to create layers from user data such as polygons and grids.

Plot Data on Oceanographic Maps using 'ggplot2'

Allows plotting data on bathymetric maps using 'ggplot2'. Plotting oceanographic spatial data is made as simple as feasible, but also flexible for custom modifications. Data that contain geographic information from anywhere around the globe can be plotted on maps generated by the basemap() or qmap() functions using 'ggplot2' layers separated by the '+' operator. The package uses spatial shape- ('sf') and raster ('stars') files, geospatial packages for R to manipulate, and the 'ggplot2' package to plot these files. The package ships with low-resolution spatial data files and higher resolution files for detailed maps are stored in the 'ggOceanMapsLargeData' repository on GitHub and downloaded automatically when needed.

Maps, Data and Methods Related to Guerry (1833) "Moral Statistics of France"

Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and statistical and graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and development of statistical and graphic methods for multivariate data in a geospatial context of historical interest.

Spatial Lag Model Trees

Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) .

Earth Observation Data Cubes from Satellite Image Collections

Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) for further details.

R Fortunes

A collection of fortunes from the R community.

Spatial Econometric Interaction Models

Efficient estimation of spatial econometric models of origin-destination flows, which may exhibit spatial autocorrelation in the dependent variable, the explanatory variables or both. The model is the one proposed by LeSage and Pace (2008) , who develop a matrix formulation that exploits the relational structure of flow data. The estimation procedures follow most closely those outlined by Dargel (2021) (preprint available at < https://www.tse-fr.eu/fr/publications/revisiting-estimation-methods-spatial-econometric-interaction-models>).

Entropy-Based Local Indicator of Spatial Association

A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) .

Sparse Linear Algebra

Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.