spatialEco (version 1.3-2)

sp.kde: Spatial kernel density estimate

Description

A weighted or unweighted Gaussian Kernel Density estimate for spatial data

Usage

sp.kde(
  x,
  y = NULL,
  bw = NULL,
  newdata = NULL,
  nr = NULL,
  nc = NULL,
  standardize = FALSE,
  scale.factor = NULL,
  mask = TRUE
)

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

nr

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

nc

Number of columns used for 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

mask

(TRUE/FALSE) mask resulting raster if newdata is provided

Value

Raster class object containing kernel density estimate

Examples

# NOT RUN {
 library(sp)
 library(raster)
   data(meuse)
   coordinates(meuse) <- ~x+y
 			
 # Unweighted KDE (spatial locations only)				
 pt.kde <- sp.kde(x = meuse, bw = 1000, standardize = TRUE, 
                  nr=104, nc=78, scale.factor = 10000 )
 
 # Plot results
   plot(pt.kde, main="Unweighted kde")
     points(meuse, pch=20, col="red") 
 
 #### Using existing raster(s) to define grid ####

 # Weighted KDE using cadmium and extent with row & col to define grid
 e <- c(178605, 181390, 329714, 333611) 
 cadmium.kde <- sp.kde(x = meuse, y = meuse$cadmium, bw = 1000,  
                       nr = 104, nc = 78, newdata = e, 
 					  standardize = TRUE, 
 					  scale.factor = 10000  )
 plot(cadmium.kde)
   points(meuse, pch=19)
  			
 # Weighted KDE using cadmium and raster object to define grid
 r <- raster::raster(raster::extent(c(178605, 181390, 329714, 333611)),
                     nrow=104, ncol=78)
   r[] <- rep(1,ncell(r))
 cadmium.kde <- sp.kde(x = meuse, y = meuse$cadmium, bw = 1000,  
                       newdata = r, standardize = TRUE, 
 					  scale.factor = 10000  )
 plot(cadmium.kde)
   points(meuse, pch=19)
  			
 # Weighted KDE using cadmium and SpatialPixelsDataFrame object to define grid
 data(meuse.grid)
 coordinates(meuse.grid) = ~x+y
 proj4string(meuse.grid) <- CRS("+init=epsg:28992")
 gridded(meuse.grid) = TRUE
 cadmium.kde <- sp.kde(x = meuse, y = meuse$cadmium, bw = 1000,  
                       newdata = meuse.grid, standardize = TRUE, 
 					  scale.factor = 10000  )
 plot(cadmium.kde)
   points(meuse, pch=19)
# }
# NOT RUN {
# }