createDistMat: Calculate Hydrologic Distances for a SpatialStreamNetwork Object

Description

Creates a collection of (non-symmetric) matrices containing pairwise downstream hydrologic distances between sites in a SpatialStreamNetwork object

Usage

createDistMat(ssn, predpts = NULL, o.write = FALSE, amongpreds = FALSE)

Arguments

ssn

a SpatialStreamNetwork-class object

predpts

a valid predpoints ID from the ssn

o.write

If TRUE, overwrite existing distance matrices. Defaults to FALSE.

amongpreds

If TRUE, compute the distances between the prediction sites i. Defaults to FALSE.

Value

The createDistMat function creates a collection of hierarchical directories in the ssn@path directory, which store the pairwise distances between sites associated with the SpatialStreamNetwork-class object. See details section for additional information.

Details

A distance matrix that contains the hydrologic distance between any two sites in SpatialStreamNetwork object is needed to fit a spatial statistical model using the tail-up and tail-down autocovariance functions described in Ver Hoef and Peterson (2010). These models are implemented in R via glmssn in the SSN package. The hydrologic distance information needed to model the covariance between flow-connected (i.e. water flows from one location to the other) and flow-unconnected (i.e. water does not flow from one location to the other, but they reside on the same network) locations differs. The total hydrologic distance is a directionless measure; it represents the hydrologic distance between two sites, ignoring flow direction. The hydrologic distance from each site to a common downstream stream junction is used when creating models for flow-unconnected pairs, which we term downstream hydrologic distance. In contrast, the total hydrologic distance is used for modeling flow-connected pairs, which we term total hydrologic distance.

A downstream hydrologic distance matrix provides enough information to meet the data requirements for both the tail-up and tail-down models. When two locations are flow-connected, the downstream hydrologic distance from the upstream location to the downstream location is greater than zero, but it is zero in the other direction. When two locations are flow-unconnected the downstream hydrologic distance will be greater than zero in both directions. A site's downstream hydrologic distance to itself is equal to zero. The format of the downstream hydrologic distance matrix is efficient because distance information needed to fit both the tail-up and tail-down models is only stored once. As an example, a matrix containing the total hydrologic distance between sites is easily calculated by adding the downstream distance matrix to its transpose.

The downstream hydrologic distances are calculated based on the binaryIDs and stored as matrices. The matrices are stored in a directory named distance, which is created by the createDistMat function within the .ssn directory. The distance directory will always contain at least one directory named obs, which contains a number of .RData files, one for each network that has observed sites residing on it. The naming convention for the files is based on the netID number (e.g. dist.net1.RData). Each matrix in the obs folder contains the information to form a square matrix, which contains the downstream hydrologic distance between each pair of observed sites on the network. Direction is preserved, with columns representing the FROM site and rows representing the TO site. Row and column names correspond to the pid attribute for each site.

If the argument predpts is specified in the call to the function, the downstream hydrologic distances between the observed and prediction sites will also be computed. A new directory is created within the distance directory, with the name corresponding to the predpoints ID (e.g. preds). A sequence of .RData files is created within this directory, similar to the structure for the observed sites, except that two objects are stored for each network that contains both observed and prediction sites. The letters a and b are used in the naming convention to distinguish between the two objects (e.g. dist.net1.a and dist.net1.b). The matrices that these objects represent are not necessarily square. In matrices of type a, rows correspond to observed locations and columns to prediction locations. In contrast, rows correspond to prediction locations and columns to observed locations in matrices of type b. Direction is also preserved, with columns representing the FROM site and rows representing the TO site in both object types. Again, row and column names correspond to the pid attribute for each site.

If the argument amongpreds is set to TRUE, the downstream hydrologic distances will also be computed between prediction sites, for each network. Again these are stored within the distance directory with the name corresponding to the predpoints ID. The naming convention for these prediction to prediction site distance matrices is the same as the distance matrices stored in the obs directory (e.g. dist.net1.RData). These extra distance matrices are needed to perform block Kriging using the glmssn

References

Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach to spatial statistical models of stream networks. The Journal of the American Statistical Association, 105(489), 22--24

Examples

Run this code

library(SSN)
# NOT RUN
# mf04 <- importSSN(system.file("lsndata/MiddleFork04.ssn",
#	        package = "SSN"), o.write = TRUE)
# use SpatialStreamNetwork object mf04 that was already created
data(mf04)
mf04 <- updatePath(mf04, system.file("lsndata/MiddleFork04.ssn",
	package = "SSN"))

# create distance matrix among observed data points
createDistMat(mf04, o.write = TRUE)

# create distance matrix among observed data points
#     and between observed and prediction points
data(mf04p)
mf04p <- updatePath(mf04p, system.file("lsndata/MiddleFork04.ssn",
	package = "SSN"))
createDistMat(mf04p, predpts = "pred1km", o.write = TRUE)

# NOT RUN include prediction to prediction site distances
# createDistMat(mf04p, predpts = "pred1km", o.write = TRUE, amongpreds = TRUE)

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning