sits_geo_dist {sits} | R Documentation |
Compute the minimum distances among samples and prediction points.
Description
Compute the minimum distances among samples and samples to prediction points, following the approach proposed by Meyer and Pebesma(2022).
Usage
sits_geo_dist(samples, roi, n = 1000L, crs = "EPSG:4326")
Arguments
samples |
Time series (tibble of class "sits"). |
roi |
A region of interest (ROI), either a file containing a shapefile or an "sf" object |
n |
Maximum number of samples to consider (integer) |
crs |
CRS of the |
Value
A tibble with sample-to-sample and sample-to-prediction distances (object of class "distances").
Note
As pointed out by Meyer and Pebesma, many classifications using machine learning assume that the reference data are independent and well-distributed in space. In practice, many training samples are strongly concentrated in some areas, and many large areas have no samples. This function compares two distributions:
The distribution of the spatial distances of reference data to their nearest neighbor (sample-to-sample.
The distribution of distances from all points of study area to the nearest reference data point (sample-to-prediction).
Author(s)
Alber Sanchez, alber.ipia@inpe.br
Rolf Simoes, rolfsimoes@gmail.com
Felipe Carvalho, felipe.carvalho@inpe.br
Gilberto Camara, gilberto.camara@inpe.br
References
Meyer, H., Pebesma, E. "Machine learning-based global maps of ecological variables and the challenge of assessing them", Nature Communications 13, 2208 (2022). https://doi.org/10.1038/s41467-022-29838-9
Examples
if (sits_run_examples()) {
# read a shapefile for the state of Mato Grosso, Brazil
mt_shp <- system.file("extdata/shapefiles/mato_grosso/mt.shp",
package = "sits"
)
# convert to an sf object
mt_sf <- sf::read_sf(mt_shp)
# calculate sample-to-sample and sample-to-prediction distances
distances <- sits_geo_dist(
samples = samples_modis_ndvi,
roi = mt_sf
)
# plot sample-to-sample and sample-to-prediction distances
plot(distances)
}