mcStewart {SpatialPosition} | R Documentation |
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 |
varname |
character; name of the variable in the |
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
( |
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) |
cl |
numeric; number of clusters. By default cl is determined using
|
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. |
Details
The parallel implementation splits potentials computations along chunks of unknownpts (or chunks of the grid defined using resolution).
Value
Point object with the computed potentials in a new field
named OUTPUT
.
See Also
Examples
## 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, returnclass = "sf")
)
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,
returnclass = "sf")
)
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)
}
## End(Not run)