**NumberTheory » R stuff**, and kindly contributed to R-bloggers)

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 using`autofitVariogram`

, and creates an interpolated map.`autoKrige.cv`

, automatically fits the variogram model using`autofitVariogram`

, and performs cross-validation. Uses`krige.cv`

under the hood.`compare.cv`

, allows comparison of the output of`autoKrige.cv`

and`krige.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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