Sample location density: visualization examples

Submitted by dylan on Fri, 2006-02-03 03:37.

Depicting the relative density of sampling can be an interesting task, especially when the points are highly clustered and sparse. Three simple operations that can reveal subtle patterns in the spatial distribution of sample points were explored:

  • A voronoi tessellation vector operation performed on the actual sample locations.
  • A pseudo-density raster operation based on a count of cells, within proximity to sample points, and within a given radius.
  • A kernel-smoothed density function, based on a given standard deviation of spatial density.
  • A density estimation performed by the density() function from the Spatstat package in R
Sample Location Density - Voronoi
Voronoi tessellation
Sample Location Density - v.neighbors function
Psudo-density calculation
Sample Location Density - v.kernel function detail
Kernel-smoothed density

Spatial density estimation in RSpatial density estimation in R



The commands to perform these operations in GRASS are as follows:

# Voronoi tessellation:
v.voronoi -l -t in=pedons out=pedons_voroni
#
# Pseudo-density
v.neighbors in=pedons out=pcell_neighbors size=100
#
# Kernel-smoothed density
# up the resolution to speed calculations
g.region res=100 -a
v.kernel in=pedons out=pcell stddeviation=500
r.null map=pcell setnull=0.00
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