Compute the minimum distances among samples and prediction points.

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 samples.

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:

1. The distribution of the spatial distances of reference data to their nearest neighbor (sample-to-sample.

2. The distribution of distances from all points of study area to the nearest reference data point (sample-to-prediction).

References

Meyer, H., Pebesma, E. "Machine learning-based global maps of ecological variables and the challenge of assessing them", Nature Communications 13, 2208 (2022). doi:10.1038/s41467-022-29838-9 .

Author

Alber Sanchez, alber.ipia@inpe.br

Rolf Simoes, rolfsimoes@gmail.com

Felipe Carvalho, felipe.carvalho@inpe.br

Gilberto Camara, gilberto.camara@inpe.br

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)
}