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.

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>).

R Fortunes

A collection of fortunes from the R community.

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) .

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.

Calculate Sun Position, Sunrise, Sunset, Solar Noon and Twilight

Provides a set of convenient functions for calculating sun-related information, including the sun's position (elevation and azimuth), and the times of sunrise, sunset, solar noon, and twilight for any given geographical location on Earth. These calculations are based on equations provided by the National Oceanic & Atmospheric Administration (NOAA) < https://gml.noaa.gov/grad/solcalc/calcdetails.html> as described in "Astronomical Algorithms" by Jean Meeus (1991, ISBN: 978-0-943396-35-4).

Spatial Data Analysis

Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on < https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).

Quantile Regression

Estimation and inference methods for models for conditional quantile functions: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, .