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require(latticeDensity)
## LatticeDensity is designed to produce density maps
## accountaing for the effects of boundaries and holes.
## The minimum data needed to run latticeDensity is two
## 2-column matrices. One gives the vertices of a polygon
## and the other the Easting and Northing locations of the
## observations. Note that an observation outside the boundary
## polygon is moved automatically to the nearest location
## inside the polygon.
plot.new()
data(polygon1)
nodeFillingOutput = nodeFilling(poly=polygon1, node_spacing=0.02)
plot(nodeFillingOutput)
## The function nodeFillingOutput fills the polygon with
## a rectangular pattern of nodes, here at spacing 0.01.
## We should examine the plot to determine whether the
## density is high enough to fill the polygon and limn
## interesting parts of the boundary. If we have a list
## of polygonal holes, it can be used as an arugment in
## nodeFilling.
formLatticeOutput = formLattice(nodeFillingOutput)
plot(formLatticeOutput)
## formLatticeOutput connects neighboring nodes into a
## lattice. Note to see whether there are connections
## where there should not be (for instance, across a
## causeway) or whether some connections are missing.
## You can edit the neighbor structure using the
## function editLattice if need be.
Pointdata = csr(polygon1,100)
Pointdata = Pointdata[Pointdata[,1]<0.5,]
plot(rbind(polygon1,polygon1[1,]),type="l")
points(Pointdata,pch=19)
out = crossvalDensity(formLatticeOutput,PointPattern=Pointdata,
M=0.5,max_steps = 150)
## crossvalDensity uses crossvalidation to choose an optimal
## path length k. The larger k, the smoother the map.
## M is the probability that the random walk moves
## at each step and thus also governs the smoothness
## of the density map. The function also plots the ucv
## criterion vs number of steps. Note that in some simulations
## ones gets a plot of ucv vs k in which the ucv is still decreasing
## at the right side of the plot. In this case we could rerun
## crossvalDensity with a larger max_steps. This simulation is prone
## to have a ucv vs k curve that is very flat for most values of k,
## since the distribution of observations is homogenous Poisson to
## the left of the causeway.
densityOut = createDensity(formLatticeOutput,
PointPattern=Pointdata, k=out[[2]],intensity=FALSE, sparse = TRUE)
plot(densityOut)
## Once the optimal number of steps in the diffusion is selected (either
## by eye or by the crossvalDensity function), the function createDensity
## produces an object suitable for constructing a contour plot.
homerange(densityOut, percent = 0.95)
## The homerange function fills in the minimal area in the
## region that has integrated density of 0.95 or higher.
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