The provided data.frame of names will be thinned down to a smaller number of names. The thinning process attempts to select a subset of names that are uniformly spatially distributed, while simultaneously choosing the most important names (according to their relative score in the score_col column.

an_thin(gaz, n, score_col = "score", score_weighting = 5,
  row_limit = 2000)

Arguments

gaz

data.frame or SpatialPointsDataFrame: typically as returned by an_suggest

n

numeric: number of names to return

score_col

string: the name of the column that gives the relative score of each name (e.g. as returned by an_suggest). Names with higher scores will be preferred by the thinning process. If the specified score_col column is not present in gaz, or if all values within that column are equal, then the thinning will be based entirely on the spatial distribution of the features

score_weighting

numeric: weighting of scores relative to spatial distribution. A lower score_weighting value will tend to choose lower-scored names in order to achieve better spatial uniformity. A higher score_weighting value will trade spatial uniformity in favour of selecting higher-scored names

row_limit

integer: the maximum number of rows allowed in gaz; see Details. Data frames larger than this will not be processed (with an error).

Value

data.frame

Details

Note that the algorithm calculates all pairwise distances between the rows of gaz. This is memory-intensive, and so if gaz has many rows the algorithm will fail or on some platforms might crash. Input gaz data.frames with more than row_limit rows will not be processed for this reason. You can try increasing row_limit from its default value if necessary.

See also

Examples

if (FALSE) { g <- an_read(cache = "session") ## get a single name per feature, preferring the ## Japanese name where there is one g <- an_preferred(g, origin = "Japan") ## suggested names for a 100x100 mm map covering 60-90E, 70-60S ## (this is about a 1:12M scale map) suggested <- an_suggest(g, map_extent = c(60, 90, -70, -60), map_dimensions = c(100, 100)) ## find the top 20 names by score head(suggested, 20) ## find the top 20 names chosen for spatial coverage and score an_thin(suggested, 20) }