latbias (version 1.0.0)

LBI: LBI

Description

Computes the Latitudinal Bias Index (LBI) for a given shapefile, by calculating the distance between two random locations within the shape multiple times (see details).

Usage

LBI(
  study_area_id,
  study_area_polygon,
  nobs = 250,
  nboot = 1000,
  fact_location = 10,
  elevation = NULL,
  raw_output = FALSE
)

Value

A data.frame or a list of two data.frames if raw_output is set to TRUE.

Arguments

study_area_id

Character string. Name ID of the study case area or country name.

study_area_polygon

Polygon shapefile. It should be a sfc object, of class POLYGON or MULTIPOLYGON.

nobs

Numeric. Number of random observations in each sample. 250 by default.

nboot

Numeric. Determines how many times the random shifts are calculated. 1,000 by default.

fact_location

Numeric. fact_location x nobs determine all the possible coordinates that can be sampled within the provided polygon for all bootstraps (faster than to generate a set of random location at each bootstrap)

elevation

Elevation raster. elevation in wgs84; if not provided, NA will be returned for null-model elevational shifts.

raw_output

Logical. FALSE by default. If TRUE, all bootstraps are returned as a data.frame. => say that the raw outputs are accessible

Details

The main output contains the following columns:

• study_area_id: ID or name of the study case region

• distance_km: average expected geographic distance shift between t1 and t2

• null_mod_SN_shift: average expected South-North shift between t1 and t2, in absolute values

• null_mod_EW_shift: average expected East-West shift between t1 and t2, in absolute values

• null_mod_elevation_shift: average expected elevation shift between t1 and t2, in absolute values

• LBI: the Latitudinal Bias Index value

LBI formula is

\(LBI = 2\times(\frac{mean(|\frac{A_nlat}{A_nlon}|)}{1+mean(|\frac{A_nlat}{A_nlon}|)}-0.5)\)

with \(A_nlat\) and \(A_nlon\) denoting the geographic displacement of the centroid positions of both sets of observation in the nth iteration by means in the latitudinal and the longitudinal dimension.

References

 Sanczuk et al. submitted.

Examples

# \donttest{
study_area <- rnaturalearth::ne_countries(scale = 110, continent = "Europe",
country = "Sweden", type = "map_units", returnclass = "sf")
study_area <- sf::st_union(study_area)

LBI(study_area_id = "Sweden", study_area_polygon = study_area,
nobs = 10, nboot = 10, fact_location = 5, elevation = NULL)

# With elevation
elevation_df <- elevatr::get_elev_raster(
locations = sf::st_as_sf(study_area), z = 5)
LBI(study_area_id = "Sweden", study_area_polygon = study_area,
nobs = 10, nboot = 10, fact_location = 5, elevation = elevation_df)
# }