spatial pattern correlation

Usage

spc(
  data,
  overlay = "and",
  discnum = 3:8,
  minsize = 1,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1
)

Arguments

data: A data.frame, tibble or sf object of observation data.
overlay: (optional) Spatial overlay method. One of and, or, intersection. Default is and.
discnum: A numeric vector of discretized classes of columns that need to be discretized. Default all discvar use 3:8.
minsize: (optional) The min size of each discretization group. Default all use 1.
strategy: (optional) Optimal discretization strategy. When strategy is 1L, choose the highest q-statistics to determinate optimal spatial data discretization parameters. When strategy is 2L, The optimal discrete parameters of spatial data are selected by combining LOESS model.
increase_rate: (optional) The critical increase rate of the number of discretization. Default is 5%.
cores: (optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.

Value

A list.

correlation_tbl: A tibble with power of spatial pattern correlation
correlation_mat: A matrix with power of spatial pattern correlation

Examples

if (FALSE) { # \dontrun{
## The following code needs to configure the Python environment to run:
sim1 = sf::st_as_sf(gdverse::sim,coords = c('lo','la'))
g = spc(sim1, discnum = 3:6, cores = 1)
g
} # }