sarp.snowprofile.alignment

The goal of sarp.snowprofile.alignment is to

• align snow profiles by matching their individual layers based on Dynamic Time Warping (DTW)
• assess the similarity of the aligned profiles with an independent similarity measure that is geared towards avalanche hazard assessment
• cluster sets of snow profiles according to similar conditions based on the independent similarity measure, and
• aggregate clusters of snow profiles to summarize the conditions by the most representative (medoid) profile

Background: Snow profiles describe the vertical (1D) stratigraphy of layered snow with different layer characteristics, such as grain type, hardness, deposition/burial date, and many more. Hence, they represent a data format similar to multi-variate time series containing categorical, ordinal, and numerical data types.

Installation

You can install the CRAN release of this package with install_packages('sarp.snowprofile.alignment') or get the most recent version from our bitbucket repository with:

devtools::install_bitbucket('sfu-arp/sarp.snowprofile.alignment', ref = 'master')

Example

Aligning pairs of snow profiles

library(sarp.snowprofile.alignment)
#> Loading required package: sarp.snowprofile

plotSPalignment(SPpairs$D_generalAlignment1, SPpairs$D_generalAlignment2)

The corresponding layers between the two snow profiles query and reference have been matched, and the layer thicknesses of the query profile have been adjusted, so that the warped query is optimally aligned with the query.

Clustering snow profiles and finding a representative profile

Consider a (very) short list of 5 snow profiles:

setSP <- SPgroup[c(1, 2, 5, 6, 7)]
par(mfrow = c(1, 2))
plot(setSP[[2]], main = "Profile 2")
plot(setSP, SortMethod = 'unsorted', labelOriginalIndices = TRUE, main = "Set of 5 profiles")

Compute the snow profile that represents the group the best by aligning the profiles and assessing their similarity:

idxMedoid <- medoidSP(setSP)
#> You are about to compute 5^2 = 25 profile alignments. This will take roughly 7.5 seconds, depending on the profile depths and resampling rate. Starting now..
#> Computed pairwise distance matrix. It actually took 
#> Time difference of 5.391558 secs
#> 5.391558
paste("The most representative profile is Profile", idxMedoid)
#> [1] "The most representative profile is Profile 2"

The last computation told us the most representative profile: It is Profile 2, which we have plotted earlier.

Getting started

A broader and deeper overview of the package is provided in the vignettes, cf. browseVignettes("sarp.snowprofile.alignment")). For a quick start, see ?sarp.snowprofile.alignment. This package builds on top of the general package sarp.snowprofile for data import/export and manipulation, cf. browseVignettes("sarp.snowprofile")).