multiN.RdAlgorithm based on latent Gaussian fields to allocate two different types of crops across a landscape.
multiN(d, area, prop, range = 0, algo = "random")a symmetric matrix of the pairwise distances between the centroids of the fields of the landscape.
vector containing field areas.
proportion of landscape surface covered by the second type of crop.
range of spatial autocorrelation between fields (must be greater or equal 0). The greater the value of range, the higher the degree of spatial aggregation (roughly, range between 0 and 0.1 for fragmented landscapes, between 0.1 and 0.5 for balanced landscapes, between 0.5 and 3 for aggregated landscapes, and above 3 for highly aggregated landscapes).
the algorithm used for the computation of the variance-covariance matrix of the multivariate normal distribution: "exp" for exponential function, "periodic" for periodic function, "random" for random draw (see details). If algo="random", the parameter range is ignored.
A dataframe containing the index of each field (column 1) and the index (0 or 1) of the type of crop grown on these fields (column 2).
This algorithm allows the control of the proportions of each type of crop in terms of surface coverage, and their level of spatial aggregation. A random vector of values is drawn from a multivariate normal distribution with expectation 0 and a variance-covariance matrix which depends on the pairwise distances between the centroids of the fields. Two different functions allow the computation of the variance-covariance matrix to allocate crops with more or less spatial aggregation (depending on the value of the range parameter). The exponential function codes for an exponential decay of the spatial autocorrelation as distance between fields increases. The periodic function codes for a periodic fluctuation of the spatial autocorrelation as distance between fields increases. Alternatively, a normal distribution can be used for a random allocation of the types of crops. Next, the two types of crops are allocated to different fields depending on whether the value drawn from the multivariate normal distribution is above or below a threshold. The proportion of each type of crop in the landscape is controlled by the value of this threshold (parameter prop).
if (FALSE) {
d <- matrix(rpois(100, 100), nrow = 10)
d <- d + t(d) ## ensures that d is symmetric
area <- data.frame(id = 1:10, area = 10)
multiN(d, area, prop = 0.5, range = 0.5, algo = "periodic")
}