spatialEco (version 1.0-0)

sp.kde: Spatial kernel density estimate

Description

A weighted or unweighted Gaussian Kernel Density estimate for spatial data

Usage

sp.kde(x, y, bw, newdata, n, standardize = FALSE, scale.factor)

Arguments

x

sp SpatialPointsDataFrame object

y

Optional values, associated with x coordinates, to be used as weights

bw

Distance bandwidth of Gaussian Kernel, must be units of projection

newdata

A Rasterlayer, any sp class object or c[xmin,xmax,ymin,ymax] vector to estimate the kde extent

n

Number of cells used in creating grid. If not defined a value based on extent or existing raster will be used

standardize

Standardize results to 0-1 (FALSE/TRUE)

scale.factor

Optional numeric scaling factor for the KDE (eg., 10000), to account for small estimate values

Value

Raster class object containing kernel density estimate

Examples

# NOT RUN {
 library(sp)
   data(meuse)
   coordinates(meuse) <- ~x+y
 
 # Weighted KDE using cadmium and spatial locations 
 cadmium.kde <- sp.kde(x = meuse, y = meuse$cadmium, bw = 1000, n = 5000, 
                       standardize = TRUE, scale.factor = 10000  )
 				
 # Unweighted KDE (spatial locations only)				
 pt.kde <- sp.kde(x = meuse, bw = 1000, standardize = TRUE, n = 5000, 
                  scale.factor = 10000  )
 
 # Plot results
 par(mfrow=c(1,2))
   plot(cadmium.kde, main="weighted kde")
     points(meuse, pch=20, col="red")
   plot(pt.kde, main="Unweighted kde")
     points(meuse, pch=20, col="red") 
 
 # Using existing raster
 library(raster)
 data(meuse.grid)
 coordinates(meuse.grid) = ~x+y
 proj4string(meuse.grid) <- CRS("+init=epsg:28992")
 gridded(meuse.grid) = TRUE
 meuse.grid <- raster(meuse.grid)
 
 cadmium.kde <- sp.kde(x = meuse, y = meuse$cadmium, newdata = meuse.grid, bw = 1000, 
                       standardize = TRUE, scale.factor = 10000  )
   plot(cadmium.kde, main="weighted kde")
     points(meuse, pch=20, cex=0.5, col="red")
# }
# NOT RUN {
# }