Flags records within 10 km of a point of the Artificial Hotspot Occurrence Inventory (AHOI; Park et al. 2023): the coordinates that recur most often in GBIF plant, bird, insect and mammal records, each with the reason it recurs (a georeferencing default, a gazetteer centroid, a popular observation site). WGS84 geodesic distance, closed ball.
Arguments
- x
A data frame of occurrence records.
- buffer
Buffer distance of the test, in the unit of its Buffer section; the default is the test's default buffer.
- ...
Further arguments to
flag_occurrences():columns,reference,gazetteers,land_scale.
Value
clean_<test>(): the rows of x the test does not flag, with the
columns of x unchanged. See keep_unflagged(), Cleaning functions.
Details
The shipped inventory is built from the original deposit (Dryad,
doi:10.5061/dryad.v41ns1s0p, CC0); reference_points() returns it with the
taxon group, the record count at each point and whether the point marks a
real observation site (determination), so it can be filtered and passed
back through gazetteers.
Custom gazetteer
gazetteers = list(aohi = points), a data frame
of longitude and latitude; see flag_occurrences().
CoordinateCleaner
Equivalent to cc_aohi() with its default four
taxa; with reference = "CoordinateCleaner" its bundled table is used.
Its 72-gon buffer edge lies within 9.5 m of the 10 km circle. Its table
differs from the Dryad deposit at a few points.
References
Park, D. S., Xie, Y., Thammavong, H. T., Tulaiha, R. and Feng, X. (2023). Artificial Hotspot Occurrence Inventory (AHOI). Journal of Biogeography 50:441-449. doi:10.1111/jbi.14543
See also
flag_occurrences(), flag_tests(), reference_points()
Other tests:
clean_age_equal(),
clean_age_outlier(),
clean_age_range_outlier(),
clean_basis_fossil(),
clean_capital(),
clean_centroid(),
clean_country_mismatch(),
clean_dataset_ddmm(),
clean_dataset_rounded(),
clean_duplicate(),
clean_equal(),
clean_establishment_introduced(),
clean_gbif_headquarters(),
clean_institution(),
clean_invalid(),
clean_issue_coordinate(),
clean_outlier(),
clean_range(),
clean_sea(),
clean_sea_nonmarine(),
clean_spatiotemporal_outlier(),
clean_urban(),
clean_zero()
Examples
a <- reference_points("aohi")
x <- data.frame(decimalLongitude = a$lon[1] + 0.01, decimalLatitude = a$lat[1])
flag_occurrences(x, tests = "aohi")$flag_aohi
clean_aohi(x)