Automatic spatial interpolation with R: the automap package
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In case of continuously collected data, e.g. observations from a monitoring network, spatial interpolation of this data cannot be done manually. Instead, the interpolation should be done automatically. To achieve this goal, I developed the automap
package. automap
builds on top of the excellent gstat
package, and provides automatic spatial interpolation, more specifically, automatic kriging. Kriging in its more simple form (Ordinary Kriging, Universal Kriging, aka Kriging with External Drift) is actually nothing more than linear regression with spatially correlated residuals.
automap
provides the following set of functions (for details I refer to the online manual):
autofitVariogram
, automatically fits the variogram model to the data.autoKrige
, automatically fits the variogram model usingautofitVariogram
, and creates an interpolated map.autoKrige.cv
, automatically fits the variogram model usingautofitVariogram
, and performs cross-validation. Useskrige.cv
under the hood.compare.cv
, allows comparison of the output ofautoKrige.cv
andkrige.cv
. This can be used to evaluate the performance of different interpolation algorithms.compare.cv
allows comparison using both summary statistics and spatial plots.
In general, the interface of automap
mimics that of gstat
. The following code snippets show some examples of creating interpolated maps using automap
:
library(automap) loadMeuse() # Ordinary kriging kriging_result = autoKrige(zinc~1, meuse, meuse.grid) plot(kriging_result) # Universal kriging kriging_result = autoKrige(zinc~soil+ffreq+dist, meuse, meuse.grid) plot(kriging_result)
You can get automap
from either CRAN:
install.packages("automap")
hg clone https://bitbucket.org/paulhiemstra/automap
PS: automap
was the first package I wrote, at the beginning of my PhD, so it is not the most beautiful code I ever wrote .
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