cc_aohi: Identify Coordinates in Artificial Hotspot Occurrence...

In azizka/CoordinateCleaner: Automated Cleaning of Occurrence Records from Biological Collections

Identify Coordinates in Artificial Hotspot Occurrence Inventory

Description

Removes or flags records within Artificial Hotspot Occurrence Inventory. Poorly geo-referenced occurrence records in biological databases are often erroneously geo-referenced to highly recurring coordinates that were assessed by Park et al 2022. See the reference for more details.

Usage

cc_aohi(
  x,
  lon = "decimalLongitude",
  lat = "decimalLatitude",
  species = "species",
  taxa = c("Aves", "Insecta", "Mammalia", "Plantae"),
  buffer = 10000,
  geod = TRUE,
  value = "clean",
  verbose = TRUE
)

Arguments

x	data.frame. Containing geographical coordinates and species names.
lon	character string. The column with the longitude coordinates. Default = “decimalLongitude”.
lat	character string. The column with the latitude coordinates. Default = “decimalLatitude”.
species	character string. The column with the species identity. Only required if verify = TRUE.
taxa	Artificial Hotspot Occurrence Inventory (AHOI) were created based on four different taxa, birds, insecta, mammalia, and plantae. Users can choose to keep all, or any specific taxa subset to define the AHOI locations. Default is to keep all: c("Aves", "Insecta", "Mammalia", "Plantae").
buffer	The buffer around each capital coordinate (the centre of the city), where records should be flagged as problematic. Units depend on geod. Default = 10 kilometres.
geod	logical. If TRUE the radius around each capital is calculated based on a sphere, buffer is in meters and independent of latitude. If FALSE the radius is calculated assuming planar coordinates and varies slightly with latitude. Default = TRUE. See https://seethedatablog.wordpress.com/ for detail and credits.
value	character string. Defining the output value. See value.
verbose	logical. If TRUE reports the name of the test and the number of records flagged.

Value

Depending on the ‘value’ argument, either a data.frame containing the records considered correct by the test (“clean”) or a logical vector (“flagged”), with TRUE = test passed and FALSE = test failed/potentially problematic . Default = “clean”.

Note

See https://ropensci.github.io/CoordinateCleaner/ for more details and tutorials.

References

Park, D. S., Xie, Y., Thammavong, H. T., Tulaiha, R., & Feng, X. (2023). Artificial Hotspot Occurrence Inventory (AHOI). Journal of Biogeography, 50, 441–449. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/jbi.14543")}

See Also

Other Coordinates: cc_cap(), cc_cen(), cc_coun(), cc_dupl(), cc_equ(), cc_gbif(), cc_inst(), cc_iucn(), cc_outl(), cc_sea(), cc_urb(), cc_val(), cc_zero()

Examples

x <- data.frame(species = letters[1:10], 
                decimalLongitude = c(runif(99, -180, 180), -47.92), 
                decimalLatitude = c(runif(99, -90,90), -15.78))
cc_aohi(x)

azizka/CoordinateCleaner documentation built on March 10, 2024, 8:32 a.m.