getStreamDistMat: Extract the stream network distance matrix from SSN objects

Description Usage Arguments Details Value Author(s) Examples

View source: R/getStreamDistMat.R

Description

The getStreamDistMat function extracts the stream network distance matrix for either observation data or prediction data, from the specified SpatialStreamNetwork object.

Usage

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getStreamDistMat(x, Name = "obs")

Arguments

x

an object of class SpatialStreamNetwork-class. Note that the createDistMat function needs to be run on an imported SpatialStreamNetwork object first in order to create the distance matrix.

Name

the internal name of the data set in the object x. For observed values, this will always be "Obs", the default. To get a stream network distance matrix for a prediction data set, the name of the data set must be given, in quotes.

Details

The internal Name for observed data in objects of class SpatialStreamNetwork is "Obs" and it is the default. If another Name is specified, it must represent a prediction data set in the SpatialStreamNetwork-class. For SpatialStreamNetwork objects, these names are obtained using the call ssn@predpoints@ID.

Note that these are not traditional distance matrices because they are asymmetric. The matrices contain the distance from one point to the common junction of both points, so they are asymmetric. For example, if two points are flow-connected, the distance from the point lower in the network to the one higher in the network is 0, while the distance from the higher point to the lower point is > 0. The convention is that the "from" point, to the common junction, is along the top of the matrix (with the column labels), and the "to" point, to the common junction, is along the left side of the matrix (with the row labels). From this matrix, it is possible to get total stream distance between any two points, an indicator matrix of flow-connectedness, etc. See examples for additional details.

Value

A list of matrices. Note that distances are only computed within networks. For "Obs" data, a matrix of distances is returned for each network, labeled "dist.net1", "dist.net2", etc., for the first and second network, etc. For prediction matrices, there are "from" and "to" matrices for both observed sites and predictions sites. The convention is that "from" are again the columns, and "to" are again the rows, but the label "a" is for from prediction sites to observation sites, and the label "b" is for from observation sites to predictions sites. Thus, the list of prediction matrices are labeled "dist.net1.a" for distance to common junction from prediction sites along the columns, to observation sites along the rows, for the first network. A prediction matrix labeled "dist.net1.b" contains distances to the common junction from observation sites along the columns to prediction sites along the rows, for the first network. If the argument amongPreds = TRUE was used for the function createDistMat, then the distance to common junction among prediction sites is returned, using the same labelling convention as for among observation sites. That is, the matrices for each network will be labeled "dist.net1", "dist.net2", etc., for the first and second network, etc.

Author(s)

Jay Ver Hoef support@SpatialStreamNetworks.com

Examples

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library(SSN)
#for examples, copy MiddleFork04.ssn directory to R's temporary directory
copyLSN2temp()
# NOT RUN
# Create a SpatialStreamNetork object that also contains prediction sites
#mf04p <- importSSN(paste0(tempdir(),'/MiddleFork04.ssn'), 
#  predpts = "pred1km", o.write = TRUE)
#use mf04p SpatialStreamNetwork object, already created
data(mf04p)
#for examples only, make sure mf04p has the correct path
#if you use importSSN(), path will be correct
mf04p <- updatePath(mf04p, paste0(tempdir(),'/MiddleFork04.ssn'))

names(mf04p)

distObs <- getStreamDistMat(mf04p)
str(distObs)
distObs$dist.net1[1:5,1:5]

# get total in-stream distance between all pairs of points
strDistNet2 <- distObs$dist.net2 + t(distObs$dist.net2)
strDistNet2[5:10,5:10]

# maximum distance to common junction between two sites
a.mat <- pmax(distObs$dist.net2,t(distObs$dist.net2))
a.mat[5:10,5:10]

# minimum distance to common junction between two sites
# sites with 0 minimum distance are flow-connected
b.mat <- pmin(distObs$dist.net2,t(distObs$dist.net2))
b.mat[5:10,5:10]

# get distance matrices between observed sites and prediction sites
distPred1km <- getStreamDistMat(mf04p, Name = "pred1km")
str(distPred1km)
distPred1km$dist.net1.a[1:5,1:5]

# create distance matrix among prediction sites
# note these sites only occur on the second network
# this is useful for block prediction
createDistMat(mf04p, predpts = "CapeHorn", o.write = TRUE, amongpreds = TRUE)
distCape <- getStreamDistMat(mf04p, Name = "CapeHorn")
str(distCape)
distCape$dist.net2[1:5,1:5]

SSN documentation built on March 13, 2020, 1:49 a.m.