spatialSpreadFunctions: Cost functions dependent only on sensor locations
In KristinaHelle/sensors4plumes: Test and Optimise Sampling Designs Based on Plume Simulations

Kriging variance computes the ordinary kriging variance at a given set of points for the case of certain sensor locations and variogram model.

minimalDistance computes for each given point the distance to the next sensor.

Both functions can be used as input to spatialSpread that runs them and turns the result into global cost.

1 2	krigingVariance(allLocations, locations, model) minimalDistance(allLocations, locations, algorithm = "kd_tree")

`allLocations`	`SpatialDataFrame` (not "SpatialIndexDataFrame"), for each of the locations a cost is computed
`locations`	indices of sensors in `allLocations`
`model`	`model` to be forwarded to `krige`
`algorithm`	`character` to be forwarded as `algorithm` to `get.knnx`

A vector of length allLocations with cost for these locations.

1	# see spatialSpread

KristinaHelle/sensors4plumes documentation built on May 7, 2019, 12:31 p.m.

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