Aggregate Site Data Along Coarser Spatial Grid — fb_aggregate_site

This function helps aggregating site data along a coarser grid. The idea is that you have any type of data at the site scale (diversity metrics, environmental data, etc.) but you would like to work on it at a coarser scale, or you want to visualize it at that scale. This function helps you do exactly that.

Usage

fb_aggregate_site_data(site_locations, site_data, agg_geom, fun = mean, ...)

Arguments

site_locations: an sf object with the spatial geometries of sites. NOTE: the first column should be named "site" and indicate site names.
site_data: a matrix or data.frame containing values per sites to aggregate along the provided grid. Can have one or several columns (variables to aggregate). The first column must contain sites names as provided in the first argument site_locations.
agg_geom: a terra::SpatRaster or an sf object. This defines the geometry along which to aggregate the initial data. See more in the Details section.
fun: the function used to aggregate points values when there are multiple points in one cell. Default is mean.
...: additional argument(s) passed to the provided function fun

Value

An object of the same type as the agg_geom input with as many layers (if SpatRaster) or columns (if sf) as columns provided in the input site_data.

Details

The agg_geom object will condition the type of object ouput by the function. It can be of any sort as an SpatRaster or sf object. Depending on the need, it could be a regular square grid or hexagonal grid, it could also be irregular polygons like biomes or ecoregions, or points, and even lines (such as when aggregating across transects or trajectories).

Examples

library("sf")
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
## Raster grid
tavg <- system.file(
  "extdata", "annual_mean_temp.tif", package = "funbiogeo"
)
tavg <- terra::rast(tavg)

# Rasterize 3 first species counts
fb_aggregate_site_data(
    head(woodiv_locations), woodiv_site_species[, 1:4], tavg, fun = sum
)
#> class       : SpatRaster 
#> dimensions  : 290, 405, 3  (nrow, ncol, nlyr)
#> resolution  : 0.08333333, 0.08333333  (x, y)
#> extent      : -10.5, 23.25, 35.83333, 60  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#> source(s)   : memory
#> varnames    : annual_mean_temp 
#>               annual_mean_temp 
#>               annual_mean_temp 
#> names       : AALB, ACEP, APIN 
#> min values  :    0,    0,    0 
#> max values  :    0,    0,    0 

## Irregular polygons
countries <- readRDS(system.file(
  "extdata", "countries_sf.rds", package = "funbiogeo"
))
# Aggregate occurrence per country
fb_aggregate_site_data(
    head(woodiv_locations, n = 20), woodiv_site_species[, 1:4], countries,
    fun = sum
)