ebirdst (version 2.2021.1)

sample_grid: Spatiotemporal sampling of points on a grid

Description

Subsample points to deal with spatiotemporal bias in observations by defining a grid in space and time, then sampling the given number of points from each cell. sample_case_control() additionally samples presence and absence independently.

Usage

sample_grid(x, res, t_res, n = 1, replace = FALSE, jitter = TRUE)
sample_case_control(x, res, t_res, n = 1, replace = FALSE, jitter = TRUE)

Value

Logical vector indicating which rows are selected.

Arguments

x

data frame or sf object; the points to subsample. If x is a data frame the coordinates should be provided as columns latitude and longitude. The day of year should be expressed as an integer from 1-366.

res

numeric; the size in meters of the grid to sample from. This can be a 2 element vector indicating the x and y dimensions of the cells.

t_res

numeric; the temporal resolution for sampling expressed as a proportion of the year. For example, 7 / 365 would result in sampling from each week.

n

integer; the number of points to sample from each grid cell.

replace

logical; whether to sample with replacement.

jitter

logical; to avoid always using the same grid for sampling, the grid can be jittered so that the origin is different each time this function is called.

Examples

if (FALSE) {
# download example data
path <- ebirdst_download("example_data", tifs_only = FALSE)
# or get the path if you already have the data downloaded
path <- get_species_path("example_data")

# test data to sample
preds <- load_predictions(path, return_sf = TRUE)

# sample on a 100, 4 month grid
s <- sample_grid(preds, res = 100000, t_res = 1 / 4)
preds_grid <- preds[s, ]

# case control sampling independently samples presence and absence
s <- sample_case_control(preds, res = 1000000, t_res = 1 / 12)
preds_cc <- preds[s, ]

# grid sampling preserves the presence/absence ratio
table(preds$obs > 0) / nrow(preds)
table(preds_grid$obs > 0) / nrow(preds_grid)
# while case control sampling increases the prevelance of presences
table(preds_cc$obs > 0) / nrow(preds_cc)

# plot
library(sf)
p <- par(mar = c(0, 0, 0, 0))
plot(st_geometry(preds), col = "black", pch = 19, cex = 0.2)
plot(st_geometry(preds_cc), col = "red", pch = 19, cex = 0.5, add = TRUE)
}