This function uses coverage-based methods to estimate Hill-Simpson Diversity over a gridded map.
Arguments
- data
A data cube object (class 'processed_cube').
- cutoff_length
The minimum number of data points for each grid cell. Grid cells with fewer data points will be removed before calculations to avoid errors. Default is 5.
- coverage
The sample coverage value for the estimator. Default is 0.95.
- ...
Arguments passed on to
compute_indicator_workflow
ci_type
Type of bootstrap confidence intervals to calculate. (Default: "norm". Select "none" to avoid calculating bootstrap CIs.)
cell_size
Length of grid cell sides, in km. (Default: 10 for country, 100 for continent or world)
level
Spatial level: 'cube', 'continent', 'country', 'world', 'sovereignty', or 'geounit'. (Default: 'cube')
region
The region of interest (e.g., "Europe"). (Default: "Europe")
ne_type
The type of Natural Earth data to download: 'countries', 'map_units', 'sovereignty', or 'tiny_countries'. (Default: "countries")
ne_scale
The scale of Natural Earth data to download: 'small' - 110m, 'medium' - 50m, or 'large' - 10m. (Default: "medium")
output_crs
The CRS you want for your calculated indicator. (Leave blank to let the function choose a default based on grid reference system)
first_year
Exclude data before this year. (Uses all data in the cube by default.)
last_year
Exclude data after this year. (Uses all data in the cube by default.)
spherical_geometry
If set to FALSE, will temporarily disable spherical geometry while the function runs. Should only be used to solve specific issues. (Default is TRUE)
make_valid
Calls st_make_valid() from the sf package. Increases processing time but may help if you are getting polygon errors. (Default is FALSE).
num_bootstrap
Set the number of bootstraps to calculate for generating confidence intervals. (Default: 1000)
Value
An S3 object with the classes 'indicator_map' and 'hill2' containing the calculated indicator values and metadata.
Examples
if (FALSE) { # \dontrun{
h2_map <- hill2_map(example_cube_1, level = "country", region = "Denmark")
plot(h2_map)
} # }