SpatialPack: Tools for assessment the association between two spatial processes

This is the homepage for the R package SpatialPack developed in the Department of Mathematics at Universidad Técnica Federico Santa María, Chile by Felipe Osorio and Ronny Vallejos, with contributions of Francisco Cuevas, Diego Mancilla and Jonathan Acosta.

Resources and Downloads
Installation Instructions
How to cite the package
About the Authors

It provides tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. SpatialPack gives methods to complement methodologies that are available in geoR for one spatial process.

Tjostheim's Coefficient.
- Tjostheim, D., 1978. A measure of association for spatial variables. Biometrika 65, 109-114.
A Modified Correlation Coefficient Test.
- Clifford, P., Richardson, S., Hémon, D., 1989. Assessing the significance of the correlation between two spatial processes. Biometrics 45, 123-134.
The Codispersion Coefficient.
- Matheron, G., 1965. Les Variables Régionalisées et leur Estimation, Masson, Paris.
- Rukhin, A., Vallejos, R., 2008. Codispersion coefficient for spatial and temporal series. Statistics and Probability Letters 78, 1290-1300.
Modified F Test for assessing multiple correlation.
- Dutilleul, P., Pelletier, B., Alpargu, G., 2008. Modified F test for assessing the multiple correlation between one spatial process and several others. Journal of Statistical Planning and Inference 138, 1402-1415.

A detailed description of each of these techniques is presented in:

Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: with Applications in R.
Springer, Cham. DOI: 10.1007/978-3-030-56681-4

Features

Computes Tjostheim's coefficient and its asymptotic variance for two spatial sequences defined on the same locations on the plane.
Performs an hypothesis testing based on a modified version of the correlation coefficient. The test provides a decision rule to elucidate whether the two processes are spatially correlated or not. The spatial sequences need to be defined on the same locations on the plane.
Computes de codispersion coefficient for a specific direction h on the plane.
Provides a plot for the codispersion coefficient versus the lag distance h for isopropic processes.
Functions for image processing and computing the spatial association between images are also provided.

Providing Feedback

Please report any bugs/suggestions/improvements to Felipe Osorio, Universidad Técnica Federico Santa María. If you find these routines useful or not then please let me know. Also, acknowledgement of the use of the routines is appreciated.

Resources

Latest binaries and sources for SpatialPack are availables from CRAN package repository .

SpatialPack_0.4.tar.gz - Package sources
SpatialPack_0.4.zip - Windows binaries (R-release)
SpatialPack_0.4.tgz - MacOS binaries (R-release, arm64)
SpatialPack_0.4.tgz - MacOS binaries (R-release, x86_64)
SpatialPack.pdf - Reference Manual

Source code is also available at GitHub: github.com/faosorios/SpatialPack

Installation instructions

To install this package, start R and enter:

install.packages("SpatialPack")

Alternatively, you can download the source as a tarball or as a zip file. Unpack the tarball or zipfile (thereby creating a directory named, SpatialPack) and install the package source by executing (at the console prompt)

R CMD INSTALL SpatialPack

Next, you can load the package by using the command: library(SpatialPack)

Disclaimer

The package is provided under the GPL. SpatialPack is under active development: new features are being added and old features are being improved.

To cite the SpatialPack package in publications use:

Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: with Applications in R..
Springer, Cham. DOI: 10.1007/978-3-030-56681-4

References

Osorio, F., Vallejos, R., Barraza, W., Ojeda, S.M., Landi, M.A. (2022). Statistical estimation of the structural similarity index for image quality assessment. Signal, Image and Video Processing 16, 1035-1042.
Acosta, J., Vallejos, R. (2018). Effective sample size for spatial regression processes. Electronic Journal of Statistics 12, 3147-3180. Files: R Functions | Datasets
Vallejos, R., Osorio, F., Mancilla, D. (2015). The codispersion map: A graphical tool to visualize the association between two spatial variables. Statistica Neerlandica 69, 298-314.
Cuevas, F., Porcu, E., Vallejos, R. (2013). Study of spatial relationships between two sets of variables: A nonparametric approach.
Journal of Nonparametric Statistics 25, 695-714.
Ojeda, S., Vallejos, R., Lamberti, P. (2012). Measure of similarity between images based on the codispersion coefficient.
Journal of Electronic Imaging 21, 023019.

Some papers using SpatialPack:

Bernadou, A., Espadaler, X., Le Goff, A., Fourcassié, V. (2015). Ant community organization along elevational gradients in a temperate ecosystem. Insectes Sociaux 62, 59-71.
Koorem, K., Gazol, A., Öpik, M., Moora, M., Saks, Ü., Uibopuu, A., Sõber, V., Zobel, M. (2014). Soil nutrient content influences the abundance of soil microbes but not plant biomass at the small-scale. PLoS ONE 9(3): e91998.
Zhang, J., Nielsen, S.E., Mao, L., Chen, S., Svenning, J.-C. (2015). Regional and historical factors supplement current climate in shaping global forest canopy height. Journal of Ecology 104, 469-478.

See more papers using/citing SpatialPack...