Removes or flags records within a radius around the geographic centroids of political countries and provinces. Poorly geo-referenced occurrence records in biological databases are often erroneously geo-referenced to centroids.
Usage
cc_cen(
x,
lon = "decimalLongitude",
lat = "decimalLatitude",
species = "species",
buffer = 1000,
geod = TRUE,
test = "both",
ref = NULL,
verify = FALSE,
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.
- buffer
numerical. The buffer around each province or country centroid, where records should be flagged as problematic. Units depend on geod. Default = 1 kilometre.
- 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.
- test
a character string. Specifying the details of the test. One of c(“both”, “country”, “provinces”). If both tests for country and province centroids.
- ref
SpatVector (geometry: polygons). Providing the geographic gazetteer. Can be any SpatVector (geometry: polygons), but the structure must be identical to
countryref. Default =countryref.- verify
logical. If TRUE records are only flagged if they are the only record in a given species flagged close to a given reference. If FALSE, the distance is the only criterion
- 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.
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_cen(x, geod = FALSE)
#> Testing country centroids
#> Removed 0 records.
#> species decimalLongitude decimalLatitude
#> 1 a -41.986559 -67.231328
#> 2 b 179.874885 78.719439
#> 3 c -54.252343 -51.051559
#> 4 d 161.034576 29.896562
#> 5 e -102.204009 -53.297878
#> 6 f -168.446625 8.746459
#> 7 g -127.686296 58.950255
#> 8 h 127.578200 -67.039623
#> 9 i -103.266248 -42.986499
#> 10 j -104.288134 -38.084935
#> 11 a -165.772552 -87.341577
#> 12 b 160.118929 63.670271
#> 13 c -91.825925 -15.471391
#> 14 d 101.204125 45.182492
#> 15 e -76.234619 39.422512
#> 16 f 135.128848 2.763954
#> 17 g -73.529966 85.470526
#> 18 h 174.069147 -25.060039
#> 19 i 32.341520 -59.328809
#> 20 j 93.297017 50.965321
#> 21 a 120.987110 -84.433699
#> 22 b 94.615009 52.095665
#> 23 c -29.782827 -60.011338
#> 24 d -130.293057 -84.831738
#> 25 e -150.895814 51.471210
#> 26 f 56.153745 58.555415
#> 27 g 36.721389 83.743275
#> 28 h 56.518499 -21.844588
#> 29 i -61.445821 -58.610461
#> 30 j 172.610719 18.675072
#> 31 a 77.467008 54.983472
#> 32 b 134.146910 -83.397706
#> 33 c 173.982149 41.923920
#> 34 d -101.317323 -51.250922
#> 35 e 59.230823 -87.119434
#> 36 f -39.756946 -66.851536
#> 37 g -163.417090 33.491359
#> 38 h 42.089241 25.549428
#> 39 i 35.450998 -31.079066
#> 40 j -33.532693 -20.253243
#> 41 a 128.998134 37.350071
#> 42 b 6.365227 35.577977
#> 43 c 172.545628 76.618218
#> 44 d -173.874352 -7.376810
#> 45 e 62.441217 17.271756
#> 46 f -46.342842 -60.298022
#> 47 g 150.483829 12.043761
#> 48 h 64.072114 71.653927
#> 49 i 59.454886 17.005011
#> 50 j 92.174792 59.704185
#> 51 a 15.421376 16.813506
#> 52 b -93.856285 50.214714
#> 53 c 3.201686 -18.401104
#> 54 d -29.784826 62.978899
#> 55 e 81.701587 43.532205
#> 56 f 49.566799 -32.797761
#> 57 g -37.292416 -69.897560
#> 58 h 165.413739 -71.802834
#> 59 i -72.483109 54.017595
#> 60 j -161.927580 -21.609586
#> 61 a 27.427471 -80.513825
#> 62 b -101.553909 87.571566
#> 63 c -134.691743 18.751314
#> 64 d 157.734967 -63.219110
#> 65 e 108.459046 6.976831
#> 66 f 92.899303 -67.357939
#> 67 g 11.723459 83.372716
#> 68 h 16.849719 -81.551697
#> 69 i -145.466459 -60.946347
#> 70 j -40.194089 78.567737
#> 71 a -117.953320 84.722645
#> 72 b 68.661305 37.849329
#> 73 c 63.075061 69.316367
#> 74 d 160.666145 85.837557
#> 75 e -109.360972 -83.718064
#> 76 f 168.709501 -11.023692
#> 77 g -40.645340 26.004557
#> 78 h 54.123804 89.069833
#> 79 i 113.254633 -33.360728
#> 80 j -154.452682 64.058754
#> 81 a 9.658917 7.260783
#> 82 b 94.850937 67.215123
#> 83 c -23.260809 2.805412
#> 84 d 18.890041 66.502674
#> 85 e -106.548967 64.174067
#> 86 f -168.830634 -27.777027
#> 87 g 169.094541 -89.981336
#> 88 h -115.699286 -53.067646
#> 89 i 100.185403 80.139688
#> 90 j 138.855887 -39.359968
#> 91 a 121.120650 68.568159
#> 92 b 37.932639 83.000930
#> 93 c 146.476605 -89.276678
#> 94 d -167.072469 9.522037
#> 95 e -132.689336 -50.537525
#> 96 f -146.149068 26.410886
#> 97 g 70.770117 40.986368
#> 98 h -33.937660 67.065740
#> 99 i -156.370810 -21.243322
#> 100 j -47.920000 -15.780000
if (FALSE) {
cc_inst(x, value = "flagged", buffer = 50000) #geod = T
}