buffer.dist-methods: Derive buffer distances for a list of points

In Envirometrix/landmap: Automated Spatial Prediction using Ensemble Machine Learning

Derive buffer distances for a list of points

Description

Derive buffer distances using the raster::distance function, so that these can be used as predictors for spatial prediction i.e. to account for spatial proximity to low, medium and high values.

Usage

## S4 method for signature 'SpatialPointsDataFrame,SpatialPixelsDataFrame'
buffer.dist(
  observations,
  predictionDomain,
  classes,
  width,
  parallel = TRUE,
  ...
)

Arguments

observations	SpatialPointsDataFrame.
predictionDomain	SpatialPixelsDataFrame.
classes	vector of selected points as factors.
width	maximum width for buffer distance.
parallel	optional parallelization setting.
...	optional arguments to pass to raster::distance function.

Value

object of class SpatialPixelsDataFrame with distances to points

Author(s)

Tom Hengl

References

• Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., and Gräler, B. (2018) Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518. doi: 10.7717/peerj.5518

Examples

library(raster)
library(rgdal)
demo(meuse, echo=FALSE)
b <- buffer.dist(meuse["zinc"], meuse.grid[1],
        classes=as.factor(1:nrow(meuse)), parallel=FALSE)

Envirometrix/landmap documentation built on June 10, 2022, 10:12 p.m.