mcStewart: Stewart Potentials Parallel

Description

This function computes Stewart potentials using parallel computation.

Usage

mcStewart(
  knownpts,
  unknownpts,
  varname,
  typefct = "exponential",
  span,
  beta,
  resolution,
  mask,
  cl,
  size = 1000,
  longlat = TRUE,
  returnclass = "sp"
)

Arguments

knownpts

sp or sf object; this is the set of known observations to estimate the potentials from.

unknownpts

sp or sf object; this is the set of unknown units for which the function computes the estimates. Not used when resolution is set up. (optional)

varname

character; name of the variable in the knownpts dataframe from which potentials are computed. Quantitative variable with no negative values.

typefct

character; spatial interaction function. Options are "pareto" (means power law) or "exponential". If "pareto" the interaction is defined as: (1 + alpha * mDistance) ^ (-beta). If "exponential" the interaction is defined as: exp(- alpha * mDistance ^ beta). The alpha parameter is computed from parameters given by the user (beta and span).

span

numeric; distance where the density of probability of the spatial interaction function equals 0.5.

beta

numeric; impedance factor for the spatial interaction function.

resolution

numeric; resolution of the output SpatialPointsDataFrame (in map units). If resolution is not set, the grid will contain around 7250 points. (optional)

mask

sp or sf object; the spatial extent of this object is used to create the regularly spaced points output. (optional)

numeric; number of clusters. By default cl is determined using parallel::detectCores().

size

numeric; mcStewart splits unknownpts in chunks, size indicates the size of each chunks.

longlat

logical; if FALSE, Euclidean distance, if TRUE Great Circle (WGS84 ellipsoid) distance.

returnclass

"sp" or "sf"; class of the returned object.

Value

Point object with the computed potentials in a new field named OUTPUT.

Details

The parallel implementation splits potentials computations along chunks of unknownpts (or chunks of the grid defined using resolution).

Examples

Run this code

# NOT RUN {
if(require(cartography)){
  nuts3.spdf@data <- nuts3.df
  t1 <- system.time(
    s1 <- stewart(knownpts = nuts3.spdf,resolution = 40000,
                  varname = "pop2008",
                  typefct = "exponential", span = 100000,
                  beta = 3, mask = nuts3.spdf)
  )
  t2 <- system.time(
    s2 <- mcStewart(knownpts = nuts3.spdf, resolution = 40000,
                    varname = "pop2008",
                    typefct = "exponential", span = 100000,
                    beta = 3, mask = nuts3.spdf, cl = 3, size = 500)
  )
  identical(s1, s2)
  cat("Elapsed time\n", "stewart:", t1[3], "\n mcStewart:",t2[3])
  
  iso <- isopoly(x = s2, 
                 breaks = c(0,1000000,2000000, 5000000, 10000000, 20000000, 
                            200004342),
                 mask = nuts3.spdf, returnclass = "sf")
  # cartography
  opar <- par(mar = c(0,0,1.2,0))
  bks <- sort(unique(c(iso$min, iso$max)))
  choroLayer(x = iso, var = "center", breaks = bks, border = NA,
             legend.title.txt = "pop")
  layoutLayer("potential population", "","", scale = NULL)
  par(opar)
}
# }