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