Sliding window analysis

In this example, we will get precipitation data from Québec, and use a sliding window analysis to smooth them out. The beginning of the code should now be familiar:

using SimpleSDMLayers
using Plots
using Statistics

precipitation = SimpleSDMPredictor(WorldClim, BioClim, 12; left=-80.0, right=-56.0, bottom=44.0, top=62.0)
SDM predictor → 108×144 grid with 11412 Float32-valued cells
  Latitudes	(44.083333333333336, 61.916666666666664)
  Longitudes	(-79.91666666666667, -56.083333333333336)

The sliding window works by taking all pixels within a given radius (expressed in kilometres) around the pixel of interest, and then applying the function given as the second argument to their values. Empty pixels are removed. In this case, we will do a summary across a 100 km radius around each pixel:

averaged = slidingwindow(precipitation, Statistics.mean, 100.0)
SDM response → 108×144 grid with 11412 Float32-valued cells
  Latitudes	(44.083333333333336, 61.916666666666664)
  Longitudes	(-79.91666666666667, -56.083333333333336)

We can finally overlap the two layers – the result of sliding window is a little bit smoother than the raw data.

plot(precipitation, c=:alpine)
contour!(averaged, c=:white, lw=2.0)