Background

Four regions were selected within the PINN boundary for analysis of intrinsic slope classes, based on terrain type.Figure 1 illustrates the four regions that were choosen. Figure 2 shows histograms of each region along with intrinsic slope class breaks (red) and NRCS slope class breaks (gray). Note that NRCS slope classes were derived from a similar analysis (k-means clustering) of slope values from the entire park: e.g. without regard for specific landform. See attached PDF version of Figure 2 at the bottom of this page.

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

Voronoi tessellation

Psudo-density calculation

Kernel-smoothed density

Spatial density estimation in R