austFilter: Filter satellite locations
In diveMove: Dive analysis and calibration

Description Usage Arguments Details Value Warning Author(s) References See Also Examples

Apply a three stage algorithm to eliminate erroneous locations, based on the procedure outlined in Austin et al. (2003).

austFilter(time, lon, lat, id=gl(1, 1, length(time)),
           speed.thr, dist.thr, window=5, ...)
grpSpeedFilter(x, speed.thr, window=5, ...)
rmsDistFilter(x, speed.thr, window=5, dist.thr, ...)

`time`	`POSIXct` object with dates and times for each point.
`lon`	numeric vectors of longitudes, in decimal degrees.
`lat`	numeric vector of latitudes, in decimal degrees.
`id`	A factor grouping points in different categories (e.g. individuals).
`speed.thr`	numeric scalar: speed threshold (m/s) above which filter tests should fail any given point.
`dist.thr`	numeric scalar: distance threshold (km) above which the last filter test should fail any given point.
`window`	integer: the size of the moving window over which tests should be carried out.
`x`	3-column matrix with column 1: `POSIXct` vector; column 2: numeric longitude vector; column 3: numeric latitude vector.
`...`	Arguments ultimately passed to `distSpeed`.

These functions implement the location filtering procedure outlined in Austin et al. (2003). grpSpeedFilter and rmsDistFilter can be used to perform only the first stage or the second and third stages of the algorithm on their own, respectively. Alternatively, the three filters can be run in a single call using austFilter.

The first stage of the filter is an iterative process which tests every point, except the first and last (w/2) - 1 (where w is the window size) points, for travel velocity relative to the preceeding/following (w/2) - 1 points. If all w - 1 speeds are greater than the specified threshold, the point is marked as failing the first stage. In this case, the next point is tested, removing the failing point from the set of test points.

The second stage runs McConnell et al. (1992) algorithm, which tests all the points that passed the first stage, in the same manner as above. The root mean square of all w - 1 speeds is calculated, and if it is greater than the specified threshold, the point is marked as failing the second stage (see Warning section below).

The third stage is run simultaneously with the second stage, but if the mean distance of all w - 1 pairs of points is greater than the specified threshold, then the point is marked as failing the third stage.

The speed and distance threshold should be obtained separately (see distSpeed).

grpSpeedFilter returns a logical vector indicating those lines that passed the test.

rmsDistFilter and austFilter return a matrix with 2 or 3 columns, respectively, of logical vectors with values TRUE for points that passed each stage. For the latter, positions that fail the first stage fail the other stages too. The second and third columns returned by austFilter, as well as those returned by rmsDistFilter are independent of one another; i.e. positions that fail stage 2 do not necessarily fail stage 3.

This function applies McConnell et al.'s filter as described in Freitas et al. (2008). According to the original description of the algorithm in McConnell et al. (1992), the filter makes a single pass through all locations. Austin et al. (2003) and other authors may have used the filter this way. However, as Freitas et al. (2008) noted, this causes locations adjacent to those flagged as failing to fail also, thereby rejecting too many locations. In diveMove, the algorithm was modified to reject only the “peaks” in each series of consecutive locations having root mean square speed higher than threshold.

Sebastian P. Luque spluque@gmail.com and Andy Liaw.

McConnell BJ, Chambers C, Fedak MA. 1992. Foraging ecology of southern elephant seals in relation to bathymetry and productivity of the Southern Ocean. Antarctic Science 4:393-398.

Austin D, McMillan JI, Bowen D. 2003. A three-stage algorithm for filtering erroneous Argos satellite locations. Marine Mammal Science 19: 371-383.

Freitas C, Lydersen, C, Fedak MA, Kovacs KM. 2008. A simple new algorithm to filter marine mammal ARGOS locations. Marine Mammal Science DOI: 10.1111/j.1748-7692.2007.00180.x

distSpeed

## Using the Example from '?readLocs':
utils::example("readLocs", package="diveMove",
               ask=FALSE, echo=FALSE)
ringy <- subset(locs, id == "ringy" & !is.na(lon) & !is.na(lat))

## Examples below use default Meeus algorithm for computing distances.
## See ?distSpeed for specifying other methods.
## Austin et al.'s group filter alone
grp <- grpSpeedFilter(ringy[, 3:5], speed.thr=1.1)

## McConnell et al.'s filter (root mean square test), and distance test alone
rms <- rmsDistFilter(ringy[, 3:5], speed.thr=1.1, dist.thr=300)

## Show resulting tracks
n <- nrow(ringy)
plot.nofilter <- function(main) {
    plot(lat ~ lon, ringy, type="n", main=main)
    with(ringy, segments(lon[-n], lat[-n], lon[-1], lat[-1]))
}
layout(matrix(1:4, ncol=2, byrow=TRUE))
plot.nofilter(main="Unfiltered Track")
plot.nofilter(main="Group Filter")
n1 <- length(which(grp))
with(ringy[grp, ], segments(lon[-n1], lat[-n1], lon[-1], lat[-1],
                            col="blue"))
plot.nofilter(main="Root Mean Square Filter")
n2 <- length(which(rms[, 1]))
with(ringy[rms[, 1], ], segments(lon[-n2], lat[-n2], lon[-1], lat[-1],
                                 col="red"))
plot.nofilter(main="Distance Filter")
n3 <- length(which(rms[, 2]))
with(ringy[rms[, 2], ], segments(lon[-n3], lat[-n3], lon[-1], lat[-1],
                                 col="green"))


## All three tests (Austin et al. procedure)
austin <- with(ringy, austFilter(time, lon, lat, speed.thr=1.1,
                                 dist.thr=300))
layout(matrix(1:4, ncol=2, byrow=TRUE))
plot.nofilter(main="Unfiltered Track")
plot.nofilter(main="Stage 1")
n1 <- length(which(austin[, 1]))
with(ringy[austin[, 1], ], segments(lon[-n1], lat[-n1], lon[-1], lat[-1],
                                    col="blue"))
plot.nofilter(main="Stage 2")
n2 <- length(which(austin[, 2]))
with(ringy[austin[, 2], ], segments(lon[-n2], lat[-n2], lon[-1], lat[-1],
                                    col="red"))
plot.nofilter(main="Stage 3")
n3 <- length(which(austin[, 3]))
with(ringy[austin[, 3], ], segments(lon[-n3], lat[-n3], lon[-1], lat[-1],
                                    col="green"))