gasper: Graph Signal Processing

#' NYC Taxi Network Dataset
#'
#' A dataset derived from NYC taxi trip records. Additionally, the dataset includes a signal \code{f} that represents the total amount with added noise.
#'
#' @format A list with 2 elements:
#' \itemize{
#'   \item \code{A}: NYC adjacency matrix, constructed using Gaussian weights based on mean distances between locations.
#'   \item \code{f}: Signal representing the "total amount" with added artificial noise.
#' }
#'
#' @details
#'  The graph constructed represents the connectivity based on taxi trips between different locations. The weights of the edges represent the frequency and distances of trips between locations.
#'
#' The data comes from the methodology in the referenced paper. It is constructed from real-world data fetched from NYC taxis databases. The graph consists of 265 vertices which correspond to different LocationID (both Pick-Up and Drop-Off points). Gaussian weights are defined by
#' \deqn{w_{ij} = \exp(-\tau d_{i,j}^2)}{w_ij = exp(-tau * d_ij^2)}, where \eqn{d_{i,j}}{d_ij} represents the mean distance taken on all the trips between locations \eqn{i}{i} and \eqn{j}{j} or \eqn{j}{j} and \eqn{i}{i}.
#'
#' The signal \code{f} is constructed based on the "total amount" variable from the taxi dataset, with added artificial noise.
#' @references
#' de Loynes, B., Navarro, F., Olivier, B. (2021). Data-driven thresholding in denoising with Spectral Graph Wavelet Transform. Journal of Computational and Applied Mathematics, Vol. 389.
"NYCdata"