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Apply manual sampling bias to occurrences via a grid

Source: R/apply_manual_sampling_bias.R
apply_manual_sampling_bias.Rd

This function adds a sampling bias weight column to an sf object containing occurrences. The sampling probabilities are based on bias weights within each cell of a provided grid layer.

Usage

apply_manual_sampling_bias(occurrences_sf, bias_weights)

Arguments

occurrences_sf

An sf object with POINT geometry representing the occurrences.

bias_weights

An sf object with POLYGON geometry representing the grid with bias weights. This sf object should contain a bias_weight column and a geometry column. Higher weights indicate a higher probability of sampling. Weights must be numeric values between 0 and 1 or positive integers, which will be rescaled to values between 0 and 1.

Value

An sf object with POINT geometry that includes a bias_weight column containing the sampling probabilities based on the sampling bias.

See also

Other detection: apply_polygon_sampling_bias()

Examples

# Load packages
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)

# Create polygon
plgn <- st_polygon(list(cbind(c(5, 10, 8, 2, 3, 5), c(2, 1, 7, 9, 5, 2))))

# Get occurrence points
occurrences_sf <- simulate_occurrences(plgn)
#> [using unconditional Gaussian simulation]

# Create grid with bias weights
grid <- st_make_grid(
    plgn,
    n = c(10, 10),
    square = TRUE) %>%
  st_sf()
grid$bias_weight <- runif(nrow(grid), min = 0, max = 1)

# Calculate occurrence bias
occurrence_bias <- apply_manual_sampling_bias(occurrences_sf, grid)
occurrence_bias
#> Simple feature collection with 40 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 2.805565 ymin: 1.46267 xmax: 8.254267 ymax: 8.551619
#> CRS:           NA
#> First 10 features:
#>    time_point sampling_p1 bias_weight                  geometry
#> 40          1   0.9214596  0.23581957  POINT (7.10732 1.658796)
#> 5           1   0.9993232  0.13919488   POINT (7.90152 1.46267)
#> 13          1   0.9979411  0.04815941 POINT (6.158066 1.950062)
#> 31          1   0.9950043  0.04815941 POINT (6.102045 2.024401)
#> 25          1   0.9998381  0.68258195 POINT (7.045201 1.830053)
#> 4           1   0.9782428  0.62286399 POINT (7.956353 1.897629)
#> 14          1   0.6684262  0.55225256 POINT (4.923372 3.282674)
#> 27          1   0.9999701  0.55225256 POINT (4.751308 2.751113)
#> 28          1   0.9987659  0.55225256  POINT (4.98449 2.713699)
#> 23          1   0.9926203  0.77067736 POINT (5.477754 3.061427)

# Visualise where the bias is
ggplot() +
  geom_sf(data = plgn) +
  geom_sf(data = grid, alpha = 0) +
  geom_sf(data = occurrence_bias, aes(colour = bias_weight)) +
  geom_sf_text(data = grid, aes(label = round(bias_weight, 2))) +
  theme_minimal()