Moving-window data aggregation

Calculate the mean moving window value for a given radius, shape and function for each cell in a larger resolution grid.

Usage

winmove_agg(
  coarse_dat,
  fine_dat,
  d,
  type = c("circle", "rectangle"),
  win_fun,
  agg_fun = mean,
  is_grid = TRUE,
  quiet = FALSE,
  ...
)

Arguments

coarse_dat

sf, Raster* or Spatial* object. The coarse grain data (response data) across which to calculate the aggregated moving window function

fine_dat

Raster* object. The fine grain data (predictor / covariate data) to aggregate

d

numeric. If type=circle, the radius of the circle (in units of the CRS). If type=rectangle the dimension of the rectangle (one or two numbers).

type

character. The shape of the moving window

win_fun

character. The function to apply to the moving window. The function win_fun should take multiple numbers, and return a single number. For example mean, modal, min or max. It should also accept a na.rm argument (or ignore it, e.g. as one of the 'dots' arguments. For example, length will fail, but function(x, ...){na.omit(length(x))} works. See Details

agg_fun

character. The function by which to aggregate. By default this is set to mean

is_grid

logical. Use TRUE (default) if g contains only rectangular cells (i.e. a grid). If g is any other polygon file, this should be set to false

quiet

logical. If FALSE (default) and is_grid == TRUE the user gets a warning that the aggregation assumes all cells are rectangular

...

further arguments passed to or from other methods

Value

Numeric vector containing moving window values calculated for each grid cell

Details

grainchanger has several built-in functions. Functions currently included are:

• shdi - Shannon diversity, requires the additional argument lc_class (vector or scalar)

• shei - Shannon evenness, requires the additional argument lc_class (vector or scalar)

• prop - Proportion, requires the additional argument lc_class (scalar)

• var_range - Range (max - min)

Note that winmove_agg can be run in parallel using plan(multiprocess) from the future package.

Examples

if (FALSE) {
# load required data
data(g_sf)
data(cont_ls)
data(cat_ls)

# aggregate using mean
d <- winmove_agg(g_sf, cont_ls, 5, "rectangle", mean)

# aggregate using Shannon evenness
d <- winmove_agg(g_sf, cat_ls, 5, "rectangle", shei, lc_class = 1:4)
}