# rDistance: Calculate the distance map for a raster In EhrmannS/rasterTools: obtain and process earth observation data

## Description

The distance map of a binarised raster contains the distance of each background cell to the nearest foreground cell.

## Usage

 `1` ```rDistance(obj, method = "euclidean") ```

## Arguments

 `obj` [`RasterLayer(1)`] The object to modify. `method` [`character(1)`] the distance measure to be calculated. Either `"euclidean"` (default), `"manhatten"` or `"chessboard"` distance.

## Details

In contrast to `distance`, the distance values here do not warp around the boundaries of the map.

## Value

A `RasterLayer` of the same dimension as `obj`, where the value of the background cells has been replaced with the distance to the nearest foreground cell.

## References

Meijster, A., Roerdink, J.B.T.M., Hesselink, W.H., 2000. A general algorithm for computing distance transforms in linear time, in: Goutsias, J., Vincent, L., Bloomberg, D.S. (Eds.), Mathematical Morphology and Its Applications to Image and Signal Processing. Springer, pp. 331–340.

Other operators to modify cell values: `rBinarise`, `rCategorise`, `rFillNA`, `rOffset`, `rPermute`, `rRange`, `rSubstitute`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```input <- rtData\$continuous # the different distance metrics binarised <- rBinarise(input, thresh = 40) disEuc <- rDistance(binarised) disMan <- rDistance(binarised, method = "manhattan") disChb <- rDistance(binarised, method = "chessboard") distances <- raster::stack(binarised, disEuc, disMan, disChb) visualise(distances) # calculate distance from edge to patch interior inverted <- rPermute(binarised) visualise(rDistance(inverted)) ```