makeRoads: Create roads from distances

In madorning/energySim0.1.0: Spatial Energy Simulation: Linking Subsurface to Surface Resources

Description

Default returns total 'road length' by connecting given new locations (xyStarts) and known existing locations (roadNodes) using Euclidean distance. Wrapper function for a call to nn2 which uses a K-D tree from the RANN package.

Usage

makeRoads(xyStarts, roadNodes, totalLength = TRUE)

Arguments

xyStarts	Matrix of easting (1st column) and northing (2nd column) of new pad center locations. Corresponding eastings and northings assumed to be in the same row.
roadNodes	Matrix of existing vertices of current road network. Easting (1st column) and northing (2nd column) pairs. Corresponding eastings and northings assumed to be in the same row.
totalLength	Default value is TRUE and only the total length of roads is returned. If FALSE, a list xyEnds containing the ending xy coordinates from the roadNodes are returned for each pad in addition to the total length of roads.

Details

A K-D tree, or K-dimensional tree, is a binary search tree data structure used for organizing points that can be used for quick and efficient sorting and nearest neighbor searching of large sets of points.

Value

If totalLength is TRUE, the total length of road using a Euclidean distance between starting and ending locations is returned. If totalLength is FALSE, a list xyEnds containing the ending xy coordinates from the roadNodes are returned for each pad in addition to the total length of roads.

Note

Edited by CDMartinez 15 Mar 16

Author(s)

Created by CDMartinez 15 Mar 16

Examples

library(raster)
set.seed(46)
OGasmt <- continuousAssessment(auMC = 5,
auType = 'Gas',
auProbability = 1,
auAreaProductive = c(100,400,800),
auAreaDrainage = c(10,20,40),
auPercAreaUntested = c(93,96,99),
auPercAreaSweet = c(100,100,100),
auPercFutureSS = c(20,40,50),
auEURss = c(0.15,0.4,0.65),
auLGR = c(.08,.5,1),
year = 2016)

OGasmt <- convertAcre2sqMeter(OGasmt)

rBase <- raster(resolution = c(10,10), xmn = 0, xmx = 2000, ymn = 0, ymx = 2000)
values(rBase) <- sample(1:10, 40000, replace = TRUE)

points <- rbind(c(250,250),c(250,1750),c(1750,1750),c(1750,250),c(250,250))
shape <- SpatialPolygons(list(Polygons(list(Polygon(points)), 'auOutline')))

plot(rBase, xlim = c(0,2000), ylim = c(0,2000))
lines(shape)

spatialPrep <- prepareSimSpatial(surfaceRaster = rBase, shape, OGasmt)
distributionPrep <- prepareSimDistributions(spatialPrep,wellsPerPad = 3,
padArea = 500, EA = OGasmt, numIterations=5)

# Create road network
nVertices <- 500
road1 <- cbind(seq(0, 2000, length.out = nVertices),
seq(0, 100, length.out = nVertices)*sin(seq(-pi, 1.5*pi, length.out = nVertices)) + 600)
road2 <- cbind(200*cos(seq(-pi, 1.5*pi, length.out = nVertices)) +
seq(200, 1800, length.out = nVertices), seq(0, 2000, length.out = nVertices))
# Prepare road input: a two-column matrix of (Easting, Northing)
prepRoads <- rbind(road1, road2, cbind(road1[,1],rev(road1[,2]) + 700))

pads <- placePads(distributionPrep, 5)
roadLength <- makeRoads(xyStarts = pads$xyPadCenter, roadNodes = prepRoads)

madorning/energySim0.1.0 documentation built on May 22, 2019, 2:23 p.m.