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Apply sampling bias to occurrences via a polygon

Source: R/apply_polygon_sampling_bias.R
apply_polygon_sampling_bias.Rd

This function adds a sampling bias weight column to an sf object containing occurrences based on a given polygonal area. The bias is determined by the specified bias strength, which adjusts the probability of sampling within the polygonal area.

Usage

apply_polygon_sampling_bias(occurrences_sf, bias_area, bias_strength = 1)

Arguments

occurrences_sf

An sf object with POINT geometry representing the occurrences.

bias_area

An sf object with POLYGON geometry specifying the area where sampling will be biased.

bias_strength

A positive numeric value that represents the strength of the bias to be applied within the bias_area. Values greater than 1 will increase the sampling probability within the polygon relative to outside (oversampling), while values between 0 and 1 will decrease it (undersampling). For instance, a value of 50 will make the probability 50 times higher within the bias_area compared to outside, whereas a value of 0.5 will make it half as likely.

Value

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

See also

Other detection: apply_manual_sampling_bias()

Examples

# Load packages
library(sf)
library(dplyr)
library(ggplot2)

# Simulate some occurrence data with coordinates and time points
num_points <- 10
occurrences <- data.frame(
  lon = runif(num_points, min = -180, max = 180),
  lat = runif(num_points, min = -90, max = 90),
  time_point = 1
  )

# Convert the occurrence data to an sf object
occurrences_sf <- st_as_sf(occurrences, coords = c("lon", "lat"))

# Create bias_area polygon overlapping at least two of the points
selected_observations <- st_union(occurrences_sf[2:3,])
bias_area <- st_convex_hull(selected_observations) %>%
  st_buffer(dist = 50) %>%
  st_as_sf()

occurrence_bias_sf <- apply_polygon_sampling_bias(
  occurrences_sf,
  bias_area,
  bias_strength = 2)
occurrence_bias_sf
#> Simple feature collection with 10 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -172.9904 ymin: -81.72951 xmax: 172.7625 ymax: 75.85536
#> CRS:           NA
#>    time_point                    geometry bias_weight
#> 1           1 POINT (-167.5664 -23.38865)   0.3333333
#> 2           1  POINT (99.42527 -2.255386)   0.6666667
#> 3           1  POINT (-164.214 -81.72951)   0.6666667
#> 4           1  POINT (-43.13971 72.36951)   0.3333333
#> 5           1  POINT (-172.9904 75.85536)   0.3333333
#> 6           1   POINT (9.115023 39.80854)   0.3333333
#> 7           1  POINT (-38.63359 32.45773)   0.3333333
#> 8           1  POINT (-45.8016 -14.07885)   0.6666667
#> 9           1   POINT (127.2717 47.30092)   0.3333333
#> 10          1  POINT (172.7625 -44.32055)   0.3333333

# Visualise where the bias is
occurrence_bias_sf %>%
  mutate(bias_weight = as.factor(round(bias_weight, 3))) %>%
  ggplot() +
    geom_sf(data = bias_area) +
    geom_sf(aes(colour = bias_weight)) +
    theme_minimal()