Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has three main helper functions: "compareOneToOne" (to compare two spatial maps), "compareOneToMany" (to compare a reference maps with a list of other maps), and "compareAll" (to compute a matrix of distances between a list of maps). In case of non-convex maps, the helper functions first build the convex-hull of the input bins and pads the weights with zeros.

Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.

Stamatis Kalogirou [aut, cre],
Stefanos Georganos [aut, ctb]
Implements a spatial extension of the random forest algorithm
(Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).
Future updates include more local machine learning methods
as well as a geographically weighted random forest.

Implements a spatial extension of the random forest algorithm
(Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).
Allows for a geographically weighted random forest regression
including a function to find the optical bandwidth.

Implements a spatial extension of the random forest algorithm
(Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).
Allows for a geographically weighted random forest regression
including a function to find the optical bandwidth. (Georganos
and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).