humanDetection: Hyper Human Influence

In occTest: Characterizing and Filtering Species Occurrence Data

Hyper Human Influence

Description

Detect occurrences in heavily human-impacted environments

Usage

humanDetection(
  df,
  xf,
  yf,
  method = "all",
  ras.hii,
  .th.human.influence,
  .points.proj4string,
  do = TRUE,
  verbose = FALSE,
  output.dir
)

Arguments

df	Data.frame of species occurrences
xf	the field in the data frame containing the x coordinates
yf	the field in the data frame containing the y coordinates
method	character. Indicate which tests to use. Default 'all'. See Details
ras.hii	raster. Raster map of human influence index use
.th.human.influence	numeric. Threhold to identify places of high human influence
.points.proj4string	proj4string argument for df
do	logical. Should range analysis be performed? Default TRUE
verbose	logical. Print messages? Default FALSE
output.dir	character. Output directory

Details

It uses several methods to detect records in high human influence records.
Current implemented methods are:
'hii' using a raster and a threhold of human influence 'urban' using a layer of urban areas. Method implemented in CoordinateCleaner package.

Value

data.frame

Author(s)

Josep M Serra-Diaz ([e-mail]). A Zizka (CoordinateCleaner functions)

See Also

cc_urb for CoordinateCleaner functions

Other analysis: .nearestcell3(), centroidDetection(), countryStatusRangeAnalysis(), duplicatesexcludeAnalysis(), geoEnvAccuracy()

occTest documentation built on Nov. 18, 2022, 5:07 p.m.