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Removes out or flags records that are outliers in geographic space according to the method defined via the method argument. Geographic outliers often represent erroneous coordinates, for example due to data entry errors, imprecise geo-references, individuals in horticulture/captivity.

Usage

cc_outl(
  x,
  lon = "decimallongitude",
  lat = "decimallatitude",
  species = "species",
  method = "quantile",
  mltpl = 5,
  tdi = 1000,
  value = "clean",
  sampling_thresh = 0,
  verbose = TRUE,
  min_occs = 7,
  thinning = FALSE,
  thinning_res = 0.5
)

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 name. Default = “species”.

method

character string. Defining the method for outlier selection. See details. One of “distance”, “quantile”, “mad”. Default = “quantile”.

mltpl

numeric. The multiplier of the interquartile range (method == 'quantile') or median absolute deviation (method == 'mad')to identify outliers. See details. Default = 5.

tdi

numeric. The minimum absolute distance (method == 'distance') of a record to all other records of a species to be identified as outlier, in km. See details. Default = 1000.

value

character string. Defining the output value. See value.

sampling_thresh

numeric. Cut off threshold for the sampling correction. Indicates the quantile of sampling in which outliers should be ignored. For instance, if sampling_thresh == 0.25, records in the 25 not be flagged as outliers. Default = 0 (no sampling correction).

verbose

logical. If TRUE reports the name of the test and the number of records flagged.

min_occs

Minimum number of geographically unique datapoints needed for a species to be tested. This is necessary for reliable outlier estimation. Species with fewer than min_occs records will not be tested and the output value will be 'TRUE'. Default is to 7. If method == 'distance', consider a lower threshold.

thinning

forces a raster approximation for the distance calculation. This is routinely used for species with more than 10,000 records for computational reasons, but can be enforced for smaller datasets, which is recommended when sampling is very uneven.

thinning_res

The resolution for the spatial thinning in decimal degrees. Default = 0.5.

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”.

Details

The method for outlier identification depends on the method argument. If “outlier”: a boxplot method is used and records are flagged as outliers if their mean distance to all other records of the same species is larger than mltpl * the interquartile range of the mean distance of all records of this species. If “mad”: the median absolute deviation is used. In this case a record is flagged as outlier, if the mean distance to all other records of the same species is larger than the median of the mean distance of all points plus/minus the mad of the mean distances of all records of the species * mltpl. If “distance”: records are flagged as outliers, if the minimum distance to the next record of the species is > tdi. For species with records from > 10000 unique locations a random sample of 1000 records is used for the distance matrix calculation. The test skips species with fewer than min_occs, geographically unique records.

The likelihood of occurrence records being erroneous outliers is linked to the sampling effort in any given location. To account for this, the sampling_cor option fetches the number of occurrence records available from www.gbif.org, per country as a proxy of sampling effort. The outlier test (the mean distance) for each records is than weighted by the log transformed number of records per square kilometre in this country. See for https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13152 an example and further explanation of the outlier test.

Note

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

See also

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

Examples


x <- data.frame(species = letters[1:10], 
                decimallongitude = runif(100, -180, 180), 
                decimallatitude = runif(100, -90,90))
                
cc_outl(x)
#> Testing geographic outliers
#> Removed 0 records.
#>     species decimallongitude decimallatitude
#> 1         a       -87.407862      -36.713884
#> 2         b      -155.587914       44.513173
#> 3         c       -36.652208       86.716247
#> 4         d       164.612983      -23.968896
#> 5         e      -147.523974      -50.763917
#> 6         f       136.839823      -50.715290
#> 7         g        16.269704      -82.192640
#> 8         h       -48.390515       21.728698
#> 9         i      -177.532386      -42.546705
#> 10        j      -148.280092      -40.333333
#> 11        a       142.585470      -67.576278
#> 12        b       -65.612254       69.833100
#> 13        c       158.476841      -51.710305
#> 14        d       -68.131455       48.425544
#> 15        e      -174.337536       28.660911
#> 16        f       -49.056956      -16.814006
#> 17        g       -77.766152      -25.884368
#> 18        h       162.518062       29.454416
#> 19        i       173.528997       20.272774
#> 20        j       -53.351441        5.777590
#> 21        a       103.808701      -47.901447
#> 22        b      -171.105852      -63.319709
#> 23        c        52.033926      -33.507519
#> 24        d       -40.762746       -6.930392
#> 25        e       -10.173972       50.901966
#> 26        f      -106.931763      -33.119829
#> 27        g       119.329632       31.148209
#> 28        h        29.996639       89.020909
#> 29        i       146.017661       -3.687713
#> 30        j       120.878918      -46.888357
#> 31        a       -14.999815       71.968294
#> 32        b        25.878644      -42.604127
#> 33        c       170.842693       37.070038
#> 34        d       156.677683      -38.153296
#> 35        e       -69.314226      -21.191386
#> 36        f        67.262937        8.151315
#> 37        g       -94.353850       -6.720805
#> 38        h       111.262269      -48.710282
#> 39        i        57.221787       20.068861
#> 40        j        68.237536      -87.079752
#> 41        a       -71.871749       36.160388
#> 42        b        98.933072       34.123346
#> 43        c        77.532220      -47.735925
#> 44        d       125.696092      -67.147252
#> 45        e      -145.812823       28.425241
#> 46        f       -93.606062      -53.906347
#> 47        g       -82.985870        4.419120
#> 48        h       -41.277291       80.330436
#> 49        i       -43.536914      -48.887050
#> 50        j       -17.978858       84.290884
#> 51        a       165.940428       37.902116
#> 52        b        99.707370       43.632996
#> 53        c      -117.107720       38.909567
#> 54        d      -101.994700       67.770435
#> 55        e        23.355434       18.630251
#> 56        f       -46.464742      -65.966103
#> 57        g       145.895680       69.195080
#> 58        h       -49.692696       78.366364
#> 59        i       -67.479733      -48.241918
#> 60        j       -41.835389       41.302893
#> 61        a       -26.203778      -48.638077
#> 62        b       -98.216526      -62.493131
#> 63        c       -59.440694      -41.785465
#> 64        d       -99.246206      -20.329263
#> 65        e       -62.781823       89.716023
#> 66        f       157.140853      -77.975497
#> 67        g       -76.161404       22.171943
#> 68        h       -86.669290      -40.270749
#> 69        i      -148.093733       47.703233
#> 70        j        41.386105       53.162070
#> 71        a       -73.845213      -74.115801
#> 72        b      -123.933291      -80.459925
#> 73        c        72.099551       75.523175
#> 74        d      -108.173817       85.762885
#> 75        e       108.923092       66.762459
#> 76        f      -130.790167        3.836314
#> 77        g       103.680859       77.100332
#> 78        h      -129.841453      -28.545053
#> 79        i         4.046163       42.888928
#> 80        j       154.247977      -45.401568
#> 81        a       113.057824      -68.141027
#> 82        b      -112.373360       41.330494
#> 83        c        69.038851        6.063339
#> 84        d       -56.216490      -89.107026
#> 85        e      -116.505858       19.607741
#> 86        f         7.691193      -85.762648
#> 87        g        15.507339      -56.727331
#> 88        h       -10.681759      -74.453374
#> 89        i        76.996377       48.868731
#> 90        j       -92.176833       27.510738
#> 91        a       -92.081271      -47.447226
#> 92        b       105.530247      -80.904021
#> 93        c       -43.506961       -1.589615
#> 94        d       -81.166091       89.910818
#> 95        e       137.517209       -3.177786
#> 96        f      -110.217443      -14.814367
#> 97        g        56.348358      -73.511428
#> 98        h        69.549146      -50.029731
#> 99        i        29.191610      -37.203041
#> 100       j       -29.783253       56.719528
cc_outl(x, method = "quantile", value = "flagged")
#> Testing geographic outliers
#> Flagged 0 records.
#>   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
cc_outl(x, method = "distance", value = "flagged", tdi = 10000)
#> Testing geographic outliers
#> Flagged 0 records.
#>   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#>  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
cc_outl(x, method = "distance", value = "flagged", tdi = 1000)
#> Testing geographic outliers
#> Flagged 96 records.
#>   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [73] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [97] FALSE FALSE FALSE FALSE