depurate {paar}

Remove errors from spatial data

Description

Data can be filtered by null, edge values, global outliers and spatial outliers or local defective observations. Default values are optimized for precision agricultural data.

Usage

depurate(
  x,
  y,
  toremove = c("edges", "outlier", "inlier"),
  crs = NULL,
  buffer = -10,
  ylimitmax = NA,
  ylimitmin = 0,
  sdout = 3,
  ldist = 0,
  udist = 40,
  criteria = c("LM", "MP"),
  zero.policy = NULL,
  poly_border = NULL
)

Arguments

x

an sf points object

y

character with the name of the variable to use for depuration/filtering process

toremove

character vector specifying the procedure to implement for errors removal. Default 'edges', 'outlier', 'inlier'. See Details.

crs

coordinate reference system: integer with the EPSG code, or character with proj4string to convert coordinates if x has longitude/latitude data

buffer

numeric distance in meters to be removed. Negative values are recommended

ylimitmax

numeric of length 1 indicating the maximum limit for the y variable. If NA Inf is assumed

ylimitmin

numeric of length 1 indicating the minimum limit for the y variable. If NA -Inf is assumed

sdout

numeric values outside the interval mean ± sdout × sdout values will be removed

ldist

numeric lower distance bound to identify neighbors

udist

numeric upper distance bound to identify neighbors

criteria

character with "LM" and/or "MP" for methods to identify spatial outliers

zero.policy

default NULL, use global option value; if FALSE stop with error for any empty neighbors sets, if TRUE permit the weights list to be formed with zero-length weights vectors

poly_border

sf object with one polygon or NULL. Can be the result of concaveman::concaveman

Details

Possible values for toremove are one or more elements of:

edges

All data points for a distance of buffer m from data edges are deleted.

outlier

Values that are outside the mean±sdout are removed

inlier

Local Moran index of spatial autocorrelation is calculated for each datum as a tool to identify inliers

Value

an object of class paar with two elements:

depurated_data

sf object with the data after the removal process

condition

character vector with the condition of each observation

References

Vega, A., Córdoba, M., Castro-Franco, M. et al. Protocol for automating error removal from yield maps. Precision Agric 20, 1030–1044 (2019). https://doi.org/10.1007/s11119-018-09632-8

Examples

library(sf)
data(barley, package = 'paar')
#Convert to an sf object
barley <- st_as_sf(barley,
                   coords = c("X", "Y"),
                   crs = 32720)
depurated <-
  depurate(barley,
           "Yield")

# Summary of depurated data
summary(depurated)

# Keep only depurate data
depurated_data <- depurated$depurated_data
# Combine the condition for all data
all_data_condition <- cbind(depurated, barley)