The thin_points function returns records based on coordinate thinning based on a minimum nearest neighbor distance approach.
Usage
thin_points(
df,
accepted.name = NA,
distance = 5,
reps = 100,
latitude = "latitude",
longitude = "longitude"
)Arguments
- df
Data frame of occurrence records.
- accepted.name
Accepted name of your species. This argument is not required if the data frame already contains an accepted_name column.
- distance
Default = 5. Distance in km to separate records.
- reps
Default = 100. Number of times to perform thinning algorithm.
- latitude
Default = "latitude". The name of the latitude column in the data frame.
- longitude
Default = "longitude". The name of the longitude column in the data frame.
Value
df is a data frame with the cleaned data.
Information about the columns in the returned data frame can be found in the documentation for gators_download().
Details
This function is a wrapper for spatial thinning using the spThin package (Aiello-Lammens et al., 2015) In summary, the thinning algorithm provided by spThin calculates the pairwise distances between data points, then randomly samples a single point from all points less than or equal to the set minimum nearest neighbor distance. This process is repeated until the pairwise distances among points do not fall below the minimum nearest neighbor distance.