R-Project spatial statistics software packages

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R-Project spatial statistics software packages

See: www.r-project.org/. R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files. Selected spatial analysis packages are listed in the Table below. Descriptions are those provided by the package authors. Many of the Spatial functions are described at: cran.r-project.org/web/views/Spatial.html

Package (links)




Spatial point patterns analysis

Perform first- and second-order multi-scale analyses derived from Ripley's K-function, for univariate, multivariate and marked mapped data in rectangular, circular or irregular shaped sampling windows, with test of statistical significance based on Monte Carlo simulations.


A collection of functions for estimating centrographic statistics and computational geometries from spatial point patterns

A collection of functions for computing centrographic statistics (e.g., standard distance, standard deviation ellipse), and minimum convex polygons (MCP) for observations taken at point locations. A tool is also provided for converting geometric objects associated with the centrographic statistics, and MCPs into ESRI Shapefiles


Functions for the detection of spatial clusters of diseases

A set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics


Tools for spatial data

Fields is for curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, robust, and thin plate splines, multivariate Kriging and Kriging for large data sets. One main feature is any covariance function implemented in R can be used for spatial prediction. There are also useful functions for plotting and working with spatial data as images


Package for generalized linear spatial models

Functions for inference in generalized linear spatial models. The posterior and predictive inference is based on Monte Carlo Markov chain methods. Package geoRglm is an extension to the package geoR, which must be installed first.


Generalized Regression Analysis and Spatial Prediction for R

GRASP is a general method for making spatial predictions of response variables (RV) using point surveys of the RV and spatial coverages of Predictor variables (PV)


Tools for reading and handling spatial objects

Set of tools for manipulating and reading geographic data, in particular ESRI shapefiles. Includes facilities for Google Earth grid and KML creation, ASCII grid reading, output to Mondrian, WinBUGS etc.


Functions for Kriging and Point Pattern Analysis



A package that provides classes and methods for spatial data. Note that sp has its own Sourceforge page. see:


The classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.


Univariate and Multivariate Spatial Modeling

spBayes fits Gaussian univariate and multivariate models with Monte Carlo Markov chain (MCMC)


Arbitrarily Shaped Multiple Spatial Cluster Detection for Case Event Data

Multiple cluster location and detection for 2D and 3D spatial point patterns (case event data). The methodology of this package is based on an original method that allows the detection of multiple clusters of any shape. A selection order and the distance from its nearest neighbor once pre-selected points have been taken into account are attributed at each point. This distance is weighted by the expected distance under the uniform distribution hypothesis. Potential clusters are located by modeling the multiple structural change of the distances on the selection order. Their presence is tested using the double maximum test and a Monte Carlo procedure


Graphs for spatial point patterns

Graphs, graph visualization and graph component calculations, meant to be used as a tool in spatial point pattern analysis



Spatial Point Pattern analysis, model-fitting, simulation, tests. See further


A package for analyzing spatial data, mainly Spatial Point Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Contains functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, and pixel images. Point process models can be fitted to point pattern data. Cluster type models are fitted by the method of minimum contrast. Very general Gibbs point process models can be fitted to point pattern data using a function ppm similar to lm or glm. Models may include dependence on covariates, interpoint interaction and dependence on marks. Fitted models can be simulated automatically. Also provides facilities for formal inference (such as chi-squared tests) and model diagnostics (including simulation envelopes, residuals, residual plots and Q-Q plots)


Spatial dependence: weighting schemes, statistics and models. See documentation for details:


A collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial autocorrelation, including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross product statistic, Empirical Bayes estimates and Assunção/Reis Index, Getis/Ord G and multicolored join count statistics, local Moran's I and Getis/Ord G, saddlepoint approximations and exact tests for global and local Moran's I; and functions for estimating spatial simultaneous autoregressive (SAR) lag and error models, weighted and unweighted SAR and CAR spatial regression models, semi-parametric and Moran eigenvector spatial filtering, GM SAR error models, and generalized spatial two stage least squares models


Geographically weighted regression


The function implements generalized geographically weighted regression approach to exploring spatial non-stationarity for given global bandwidth and chosen weighting scheme.


Spatial and Space-Time Point Pattern Analysis in R and S

Point pattern analysis, including kernel density, Ripley K, Ghat and Fhat functions


Trip pattern analysis

Analysis of spatial trip data, notably animal tracking information