Remove errors from spatial data

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

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

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

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")
#> Concave hull algorithm is computed with
#> concavity = 2 and length_threshold = 0

# Summary of depurated data
summary(depurated)
#>       normal point             border spatial outlier MP spatial outlier LM 
#>         5673 (77%)          964 (13%)         343 (4.6%)         309 (4.2%) 
#>         global min            outlier 
#>          99 (1.3%)         6 (0.081%) 

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