trip.

Description

Functions for accessing and manipulating spatial data for animal tracking, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from animal track data. There are coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the 'sp', 'sf', 'amt', 'trackeR', and other packages.

TimeOrderedRecords

Description

Object to identify DateTimes and IDs in a Spatial object.

Usage

TimeOrderedRecords(x)

Arguments

Value

TimeOrderedRecords holds a 2-element character vector, naming the data columns of DateTimes and IDs.

Examples

##' tor <- TimeOrderedRecords(c("datetime", "ID"))

A class for the identifiers of DateTime and ID records in spatial data.

Description

The main use of this class and creator function is for SpatialPointsDataFrame-classs which are used with TimeOrderedRecords for the class trip.

Value

S4 object, TimeOrderedRecords (a class to hold the names of the date-time and id columns)

Slots

TOR.columns:

2-element vector of class "character"

Note

Future versions may change significantly, this class is very basic and could probably be implemented in a better way. Specifying TOR columns by formula would be a useful addition.

See Also

TimeOrderedRecords, trip for creating trip objects, and trip-class for that class

Examples

showClass("TimeOrderedRecords")
tor <- new("TimeOrderedRecords", TOR.columns=c("datetime", "ID"))

Adjust duplicate DateTime values

Description

Duplicated DateTime values within ID are adjusted forward (recursively) by one second until no duplicates are present. This is considered reasonable way of avoiding the nonsensical problem of duplicate times.

Usage

adjust.duplicateTimes(time, id)

Arguments

Details

This function is used to remove duplicate time records in animal track data, rather than removing the record completely.

Value

The adjusted DateTime vector is returned.

Warning

I have no idea what goes on at CLS when they output data that are either not ordered by time or have duplicates. If this problem exists in your data it's probably worth finding out why.

See Also

readArgos

Examples

## DateTimes with a duplicate within ID
tms <- Sys.time() + c(1:6, 6, 7:10) *10
id <- rep("a", length(tms))
range(diff(tms))

## duplicate record is now moved one second forward
tms.adj <- adjust.duplicateTimes(tms, id)
range(diff(tms.adj))

Assign numeric values for Argos "class"

Description

Assign numeric values for Argos "class" by matching the levels available to given numbers. An adjustment is made to allow sigma to be specified in kilometres, and the values returned are the approximate values for longlat degrees. It is assumed that the levels are part of an "ordered" factor from least precise to most precise.

Usage

argos.sigma(x, sigma = c(100, 80, 50, 20, 10, 4, 2), adjust = 111.12)

Arguments

Details

The available levels in Argos are levels=c("Z", "B", "A", "0", "1", "2", "3").

The actual sigma values given by default are (as far as can be determined) a reasonable stab at what Argos believes.

Value

Numeric values for given levels.

Examples

cls <- ordered(sample(c("Z", "B", "A", "0", "1", "2", "3"), 30,
                      replace=TRUE),
               levels=c("Z", "B", "A", "0", "1", "2", "3"))
argos.sigma(cls)

As ("trip", other-classes)

Description

Coercing trip objects to other classes.

Function to create a SpatialLinesDataFrame from a trip object, resulting in a line segment for each implicit segment along the tracks. The object stores the start and end times, duration and the ID of the segment.

Usage

## S3 method for class 'trip'
as.ppp(X, ..., fatal)

## S3 method for class 'trip'
as.psp(x, ..., from, to)

as.track_xyt.trip(x, ..., from, to)

explode(x, ...)

Arguments

Value

ppp object

psp object

SpatialLinesDataFrame

SpatialLinesDataFrame object with each individual line segment identified by start/end time and trip ID

Examples

d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
sp::coordinates(d) <- ~x+y
tr <- trip(d, c("tms", "id"))

as(tr, "ppp")
d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
tr <- trip(d, c("tms", "id"))

 as(tr, "psp")
 as.psp(tr)
 d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
 tr <- trip(d)

spldf <- explode(tr)
summary(tr)

Coercion from other classes to trip objects

Description

Coercing objects to trip class

Usage

as.trip(x, ...)

Arguments

Value

S4 trip object

Methods

coerce

signature(from="ltraj", to="trip")

as.trip

signature(x="ltraj")

Examples

 d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
tr <- trip(d)
if (require(adehabitatLT)) {
    l <- as(tr, "ltraj")
    ltraj2trip(l)
    as.trip(l)
}

Split trip events into exact time-based boundaries.

Description

Split trip events within a single object into exact time boundaries, adding interpolated coordinates as required.

Usage

## S3 method for class 'trip'
cut(x, breaks, ...)

Arguments

Details

Motion between boundaries is assumed linear and extra coordinates are added at the cut points.

This function was completely rewritten in version 1.1-20.

Value

list of S4 trip objects, each with aligned boundaries in time based on cutting the input into intervals

A list of trip objects, named by the time boundary in which they lie.

Author(s)

Michael D. Sumner and Sebastian Luque

See Also

See also tripGrid.

Examples

set.seed(66)
d <- data.frame(x=1:100, y=rnorm(100, 1, 10),
                tms= as.POSIXct(as.character(Sys.time()), tz = "GMT") + c(seq(10, 1000, length=50),
                seq(100, 1500, length=50)), id=gl(2, 50))
sp::coordinates(d) <- ~x+y
tr <- trip(d, c("tms", "id"))

cut(tr, "200 sec")

bound.dates <- seq(min(tr$tms) - 1, max(tr$tms) + 1, length=5)
trip.list <- cut(tr, bound.dates)
bb <- sp::bbox(tr)
cn <- c(20, 8)
g <- sp::GridTopology(bb[, 1], apply(bb, 1, diff) / (cn - 1), cn)

tg <- tripGrid(tr, grid=g)
tg <- sp::as.image.SpatialGridDataFrame(tg)
tg$x <- tg$x - diff(tg$x[1:2]) / 2
tg$y <- tg$y - diff(tg$y[1:2]) / 2

op <- par(mfcol=c(4, 1))
for (i in 1:length(trip.list)) {
  plot(sp::coordinates(tr), pch=16, cex=0.7)
  title(names(trip.list)[i], cex.main=0.9)
  lines(trip.list[[i]])
  abline(h=tg$y, v=tg$x, col="grey")
  image(tripGrid(trip.list[[i]], grid=g), interpolate=FALSE,
  col=c("white", grey(seq(0.2, 0.7,  length=256))),add=TRUE)
  abline(h=tg$y, v=tg$x,  col="grey")
  lines(trip.list[[i]])
  points(trip.list[[i]], pch=16, cex=0.7)
}

par(op)
print("you may need to resize the window to see the grid data")

cn <- c(200, 80)
g <- sp::GridTopology(bb[, 1], apply(bb, 1, diff) / (cn - 1), cn)

tg <- tripGrid(tr, grid=g)
tg <- sp::as.image.SpatialGridDataFrame(tg)
tg$x <- tg$x - diff(tg$x[1:2]) / 2
tg$y <- tg$y - diff(tg$y[1:2]) / 2

op <- par(mfcol=c(4, 1))
for (i in 1:length(trip.list)) {
  plot(sp::coordinates(tr), pch=16, cex=0.7)
  title(names(trip.list)[i], cex.main=0.9)
  image(tripGrid(trip.list[[i]], grid=g, method="density", sigma=1),
        interpolate=FALSE,
        col=c("white", grey(seq(0.2, 0.7, length=256))),
        add=TRUE)
  lines(trip.list[[i]])
  points(trip.list[[i]], pch=16, cex=0.7)
}

par(op)
print("you may need to resize the window to see the grid data")



data("walrus818", package = "trip")
library(lubridate)
walrus_list <- cut(walrus818, seq(floor_date(min(walrus818$DataDT), "month"),
ceiling_date(max(walrus818$DataDT), "month"), by = "1 month"))
g <- rasterize(walrus818) * NA_real_
stk <- raster::stack(lapply(walrus_list, rasterize, grid = g))
st <- raster::aggregate(stk, fact = 4, fun = sum, na.rm = TRUE)
st[!st > 0] <- NA_real_

plot(st, col = oc.colors(52))

Function to ensure dates and times are in order with trip ID

Description

A convenience function, that removes duplicate rows, sorts by the date-times within ID, and removes duplicates from a data frame or SpatialPointsDataFrame.

Usage

forceCompliance(x, tor)

Arguments

Value

data.frame or SpatialPointsDataFrame-class.

Note

It's really important that data used are of a given quality, but this function makes the most common trip problems easy to apply.

See Also

trip

Calculate maximum distance from 'home' for each trip

Description

This function returns a distance from a given 'home' coordinate for each individual trip. Use the home argument to provide a single, common 2-element (x,y or lon,lat) coordinate. If home is NULL (the default), then each individual trip's first location is used.

Usage

homedist(x, home = NULL)

Arguments

Value

numeric vector of distances in km (for longlat), or in the units of the trip's projection

See Also

spDistsN1

Track intermediate points

Description

Calculate great circle intermediate points on longitude, latitude input vectors. A spherical model is used, from the geosphere package.

Usage

interp_equal(x, distance = NULL, duration = NULL)

Arguments

Details

For the result to be sensible, the input must either be in longitude/latitude, or be in a projection and have a valid CRS. Great circle movement is assumed, there's no way to use this to interpolate equal-distance in the native projection.

If no input distance or duration is provided a default is used of 15 points between each input point.

if both distance AND duration is provided, distance is ignored.

Note, the original implementation of this function was called 'interpequal()', and was used for time spent calculations. The functionality is now provided by the traipse package.

Value

S4 trip object with interpolated new locations based on distance or duration parameters

Generate a GridTopology from a Spatial object

Description

Sensible defaults are assumed, to match the extents of data to a manageable grid.

Usage

makeGridTopology(
  obj,
  cells.dim = c(100, 100),
  xlim = NULL,
  ylim = NULL,
  buffer = 0,
  cellsize = NULL,
  adjust2longlat = FALSE
)

Arguments

Details

Approximations for kilometres in longlat can be made using cellsize and adjust2longlat.

Value

S4 class GridTopology with properties set variously from input parameters

SeaWiFS ocean colour colours

Description

Generate ocean colour colours, using the SeaWiFS scheme

Usage

oc.theme(x = 50)

oc.colors(n)

Arguments

Details

This is a high-contrast palette, log-scaled originally for ocean chlorophyll.

Value

A set of colours or a theme object.

See Also

Similar functions in sp spplot, bpy.colors

Examples

 oc.colors(10)
 library(lattice)
 trellis.par.set(oc.theme())
 d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
tr <- trip(d)

 tg <- tripGrid(tr)
 plot(tg)

Rasterize trip objects based on line-segment attributes.

Description

Trip rasterize.

Arguments

Value

RasterLayer

Examples

 d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
tr <- trip(d, c("tms", "id"))

tr$temp <- sort(runif(nrow(tr)))
r <- rasterize(tr)

rasterize(tr, grid = r)

rasterize(tr, r, field = "temp")
rasterize(tr, method = "density")
rasterize(tr, method = "density", grid = r)

rasterize(tr, r, field = "tms")
rasterize(tr, r)

Read Argos "DAT" or "DIAG" files

Description

Return a (Spatial) data frame of location records from raw Argos files. Multiple files may be read, and each set of records is appended to the data frame in turn. Basic validation of the data is enforced by default.

Usage

readArgos(
  x,
  correct.all = TRUE,
  dtFormat = "%Y-%m-%d %H:%M:%S",
  tz = "GMT",
  duplicateTimes.eps = 0.01,
  p4 = "+proj=longlat +ellps=WGS84",
  verbose = FALSE,
  read_alt = NULL,
  ...
)

readDiag(x, return_trip = FALSE, read_alt = 1L, ...)

Arguments

Details

readArgos performs basic validation checks for class trip are made, and enforced based on correct.all:

No duplicate records in the data, these are simply removed. Records are ordered by DateTime ("date", "time", "gmt") within ID ("ptt"). No duplicate DateTime values within ID are allowed: to enforce this the time values are moved forward by one second - this is done recursively and is not robust.

If validation fails the function will return a SpatialPointsDataFrame-class. Files that are not obviously of the required format are skipped.

Argos location quality data "class" are ordered, assuming that the available levels is levels=c("Z", "B", "A", "0", "1", "2", "3").

A projection string is added to the data, assuming the PROJ.4 longlat - if any longitudes are greater than 360 the PROJ.4 argument "+over" is added.

readDiag simply builds a data.frame.

With read_alt the default value NULL returns the PRV location as-is. Some files may have a standardized location, and a dummy. If read_alt is set to 1 or 2 the corresponding "alternative" location is returned. 1 is a standardized location corresponding to the original PRV message, and 2 is a "dummy" location.

Value

readArgos returns a trip object, if all goes well, or simply a SpatialPointsDataFrame-class.

readDiag returns a data.frame with 8 columns:

• lon1,lat1 first pair of coordinates

• lon1,lat1 second pair of coordinates

• gmt DateTimes as POSIXct

• id Platform Transmitting Terminal (PTT) ID

• lq Argos location quality class

• iq some other thing

Warning

This works on some Argos files I have seen.

References

The Argos data documentation was (ca. 2003) at http://www.argos-system.org/manual. Specific details on the PRV ("provide data") format were found in Chapter 4_4_8, originally at 'http://www.cls.fr/manuel/html/chap4/chap4_4_8.htm'.

See Also

trip, SpatialPointsDataFrame-class, adjust.duplicateTimes, for manipulating these data, and argos.sigma for relating a numeric value to Argos quality "classes".

sepIdGaps for splitting the IDs in these data on some minimum gap.

order, duplicated, , ordered for general manipulation of this type.

Examples

argosfile <-
  system.file("extdata/argos/98feb.dat", package = "trip", mustWork = TRUE)
argos <- readArgos(argosfile)

Reprojection

Description

A reproj method for trip objects.

Usage

## S3 method for class 'trip'
reproj(x, target, ..., source = NULL)

Arguments

Value

a trip reprojected to 'target'

Filter track for speed, distance and angle.

Description

Create a filter index of a track for "bad" points with a combination of speed, distance and angle tests.

Usage

sda(x, smax, ang = c(15, 25), distlim = c(2.5, 5), pre = NULL)

Arguments

Details

This is an independent implementation from that in the package argosfilter by Freitas 2008.

Value

logical vector, with FALSE values where the tests failed

References

Freitas, C., Lydersen, C., Fedak, M. A. and Kovacs, K. M. (2008), A simple new algorithm to filter marine mammal Argos locations. Marine Mammal Science, 24: 315?V325. doi: 10.1111/j.1748-7692.2007.00180.x

Separate a set of IDs based on gaps

Description

A new set of ID levels can be created by separating those given based on a minimum gap in another set of data. This is useful for separating instruments identified only by their ID into separate events in time.

Usage

sepIdGaps(id, gapdata, minGap = 3600 * 24 * 7)

Arguments

Details

The assumption is that a week is a long time for a tag not to record anything.

Value

A new set of ID levels, named following the pattern that "ID" split into 3 would provided "ID", "ID_2" and "ID_3".

Warning

It is assumed that each vector provides is sorted by gapdata within id. No checking is done, and so it is suggested that this only be used on ID columns within existing, validated trip objects.

See Also

trip

Examples

id <- gl(2, 8)
gd <- Sys.time() + 1:16
gd[c(4:6, 12:16)] <- gd[c(4:6, 12:16)] + 10000
sepIdGaps(id, gd, 1000)

Filter track data for speed

Description

Create a filter of a track for "bad" points implying a speed of motion that is unrealistic.

Usage

speedfilter(x, max.speed = NULL, test = FALSE)

Arguments

Details

Using an algorithm (McConnnell et al., 1992), points are tested for speed between previous / next and 2nd previous / next points. Contiguous sections with an root mean square speed above a given maximum have their highest rms point removed, then rms is recalculated, until all points are below the maximum. By default an (internal) root mean square function is used, this can be specified by the user.

If the coordinates of the trip data are not projected, or NA the distance calculation assumes longlat and kilometres (great circle). For projected coordinates the speed must match the units of the coordinate system. (The PROJ.4 argument "units=km" is suggested).

Value

Logical vector matching positions in the coordinate records that pass the filter.

Warning

This algorithm is destructive, and provides little information about location uncertainty. It is provided because it's commonly used and provides an illustrative benchmark for further work.

It is possible for the filter to become stuck in an infinite loop, depending on the function passed to the filter. Several minutes is probably too long for hundreds of points, test on smaller sections if unsure.

Note

This algorithm was originally taken from IDL code by David Watts at the Australian Antarctic Division, and used in various other environments before the development of this version.

Author(s)

David Watts and Michael D. Sumner

References

The algorithm comes from McConnell, B. J. and Chambers, C. and Fedak, M. A. (1992) Foraging ecology of southern elephant seals in relation to the bathymetry and productivity of the southern ocean. Antarctic Science 4 393-398

See Also

sda for a fast distance angle filter to combine with speed filtering

Determine internal angles along a track

Description

Calculate the angles between subsequent 2-D coordinates using Great Circle distance (spherical) methods.

Usage

trackAngle(x)

## S3 method for class 'trip'
trackAngle(x)

## Default S3 method:
trackAngle(x)

Arguments

Details

If x is a trip object, the return result has an extra element for the start and end point of each individual trip, with value NA.

This is an optimized hybrid of "raster::bearing" and "maptools::gzAzimuth". New code is in the traipse package.

Value

Vector of angles (degrees) between coordinates.

Determine distances along a track

Description

Calculate the distances between subsequent 2-D coordinates using Euclidean or Great Circle distance (WGS84 ellipsoid) methods.

Usage

trackDistance(x1, y1, x2, y2, longlat = TRUE, prev = FALSE)

Arguments

Details

If x1 is a trip object, arguments x2, x3, y2 are ignored and the return result has an extra element for the start point of each individual trip, with value 0.0.

The prev argument is ignore unless x1 is a trip.

Distance values are in the units of the input coordinate system when longlat is FALSE, and in kilometres when longlat is TRUE.

This originally used spDistsN1, then implemented the sp gcdist source directly in R, and now uses geodist.

Please see the traipse package for a more modern approach.

Value

Vector of distances between coordinates.

References

Original source taken from sp package, but now using Helmert from Karney (2013) see the geodist package.

Examples

 d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
 tr <- trip(d, c("tms", "id"))

 ## the method knows this is a trip, so there is a distance for every
 ## point, including 0s as the start and at transitions between
 ## individual trips
trackDistance(tr)

## the default method does not know about the trips, so this is
##(n-1) distances between all points
trackDistance(coordinates(tr), longlat = FALSE)

## we get NA at the start, end and at transitions between trips

 angles <- trackAngle(tr)

Functions to retrieve DateTime and ID data from within (Spatial) data frames.

Description

Functions for retrieving the names of the columns used for DateTime and ID, as well as the data.

Usage

getTORnames(obj)

getTimeID(obj)

## S3 method for class 'summary.TORdata'
print(x, ...)

Arguments

Value

getTORnames retrieves the column names from an object extending the class TimeOrderedRecords, and getTimeID returns the data as a data frame from an object extending the class TimeOrderedRecords.

See Also

trip-class, for the use of this class with SpatialPointsDataFrame-class.

trip

Examples

tor <- TimeOrderedRecords(c("time", "id"))
getTORnames(tor)

A class for sets of animal trips (track data).

Description

An extension of SpatialPointsDataFrame-class by including "TimeOrderedRecords". The records within the data frame are explicitly ordered by DateTime data within IDs.

Objects from the Class

Objects can be created by calls of the form trip(obj="SpatialPointsDataFrame", TORnames="TimeOrderedRecords"). The object contains all the slots present within a SpatialPointsDataFrame-class, particularly data which contains columns of at least those specified by TOR.columns.

See Also

trip for examples of directly using the class.

trip-accessors describes methods for accessing information on trip objects.

Examples

showClass("trip")

d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))
tr <- trip(d)

summary(tr)
plot(tr)
lines(tr)

dim(tr)
names(tr)
subset(tr, id == "2")
as.data.frame(tr)

tr[1:3, ]
tr[, 1]
tr[[1]]

Internal trip Functions

Description

Internal trip functions

Usage

.validTORdata(object)

.intpFun(x)

.abdali(p1, p2)

.oc.col

.distances(x)

.g2ow(x)

Format

An object of class character of length 256.

Details

These are not to be called by the user (or in some cases are just waiting for proper documentation to be written).

Function to handle animal track data, organized as trip objects

Description

Create an object of class trip, extending the basic functionality of SpatialPointsDataFrame-class by specifying the data columns that define the "TimeOrdered" quality of the records.

Usage

trip(obj, TORnames, correct_all = TRUE)

trip(obj) <- value

## S4 method for signature 'trip,ANY'
split(x, f, drop = FALSE, ...)

## S4 method for signature 'trip,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

Arguments

Details

The original form of trip() required very strict input as a 'SpatialPointsDataFrame' and specifying which were the time and ID columns, but the input can be more flexible. If the object is a grouped data frame ('dplyr-style') then the (first) grouping is assumed to define individual trips and that columns 1, 2, 3 are the x-, y-, time-coordinates in that order. It can also be a trip object for redefining TORnames.

The trip() function can ingest track_xyt, telemetry, SpatialPointsDataFrame, sf, trackeRdata, grouped_df, data.frame, tbl_df, mousetrap, and in some cases lists of those objects. Please get in touch if you think something that should work does not.

Track data often contains problems, with missing values in location or time, times out of order or with duplicated times. The correct_all argument is set to TRUE by default and will report any inconsistencies. Data really should be checked first rather than relying on this auto-cleanup. The following problems are common:

• duplicated records (every column with the same value in another row)

• duplicated date-time values

• missing date-time values, or missing x or y coordinates

• records out of order within trip ID

For some data types there's no formal structure, but a simple convention such as a set of names in a data frame. For example, the VTrack package has AATAMS1 which may be turned into a trip with ⁠trip(AATAMS1 %>% dplyr::select(longitude, latitude, timestamp, tag.ID, everything())⁠ In time we can add support for all kinds of variants, detected by the names and contents.

See Chapter 2 of the trip thesis for more details.

Value

A trip object, with the usual slots of a SpatialPointsDataFrame-class and the added TimeOrderedRecords. For the most part this can be treated as a data.frame with Spatial coordinates.

Methods

Most of the methods available are by virtue of the sp package. Some, such as split.data.frame have been added to SPDF so that trip has the same functionality.

trip

signature(obj="SpatialPointsDataFrame", TORnames="ANY")

The main construction.

trip

signature(obj="SpatialPointsDataFrame", TORnames="TimeOrderedRecords")

Object and TimeOrdered records class

trip

signature(obj="ANY", TORnames="TimeOrderedRecords"): create a trip object from a data frame.

trip

signature(obj="trip", TORnames="ANY"): (Re)-create a trip object using a character vector for TORnames.

trip

signature(obj="trip", TORnames="TimeOrderedRecords"): (re)-create a trip object using a TimeOrderedRecords object.

See Also

speedfilter, and tripGrid for simplistic speed filtering and spatial time spent gridding.

Examples

d <- data.frame(x=1:10, y=rnorm(10), tms=Sys.time() + 1:10, id=gl(2, 5))

## the simplest way to create a trip is by order of columns

trip(d)

tr <- trip(d)
 ## real world data in CSV
mi_dat <- read.csv(system.file("extdata/MI_albatross_sub10.csv", package = "trip"),
            stringsAsFactors = FALSE)
mi_dat$gmt <- as.POSIXct(mi_dat$gmt, tz = "UTC")
mi_dat$sp_id <-  sprintf("%s%s_%s_%s", mi_dat$species,
         substr(mi_dat$breeding_status, 1, 1), mi_dat$band, mi_dat$tag_ID)
sp::coordinates(mi_dat) <- c("lon", "lat")
## there are many warnings, but the outcome is fine
## (sp_id == 'WAi_14030938_2123' has < 3 locations as does LMi_12143650_14257)
mi_dat <- trip(mi_dat, c("gmt", "sp_id") )
plot(mi_dat, pch = ".")
#lines(mi_dat)  ## ugly

mi_dat_polar <- reproj(mi_dat, "+proj=stere +lat_0=-90 +lon_0=154 +datum=WGS84")
plot(mi_dat_polar, pch = ".")
lines(mi_dat_polar)

Deprecated functions in trip

Description

These functions will be declared defunct in a future release.

Usage

as.SpatialLinesDataFrame.trip(from)

trip.split.exact(x, dates)

as.ltraj.trip(xy)

as.trip.SpatialLinesDataFrame(from)

Arguments

See Also

cut.trip, as.Other

Generate a grid of time spent by line-to-cell gridding

Description

Create a grid of time spent from an object of class trip by exact cell crossing methods, weighted by the time between locations for separate trip events.

Usage

tripGrid(x, grid = NULL, method = "pixellate", ...)

Arguments

Details

Zero-length lines cannot be summed directly, their time value is summed by assuming the line is a point. A warning used to be given, but as it achieved nothing but create confusion it has been removed. The density method returns proportionate values, not summed time durations.

See pixellate.psp and pixellate.ppp for the details on the method used. See density.psp for method="density".

Trip events are assumed to start and end as per the object passed in. To work with inferred "cutoff" positions see split.trip.exact.

Value

tripGrid returns an object of class SpatialGridDataFrame, with one column "z" containing the time spent in each cell in seconds.

Generate a grid of time spent using approximate methods

Description

Create a grid of time spent from an object of class trip by approximating the time between locations for separate trip events.

Usage

tripGrid.interp(x, grid = NULL, method = "count", dur = NULL, ...)

kdePoints(x, h = NULL, grid = NULL, resetTime = TRUE, ...)

countPoints(x, dur = 1, grid = NULL)

Arguments

Details

This set of functions was the the original tripGrid from prior to version 1.1-6. tripGrid should be used for more exact and fast calculations assuming linear motion between fixes.

The intention is for tripGrid.interp to be used for exploring approximate methods of line-to-cell gridding.

Trip locations are first interpolated, based on an equal-time spacing between records. These interpolated points are then "binned" to a grid of cells. The time spacing is specified by the dur (duration) argument to interpequal in seconds (i.e. dur=3600 is used for 1 hour). Shorter time periods will require longer computation with a closer approximation to the total time spent in the gridded result.

Currently there are methods "count" and "kde" for quantifying time spent, corresponding to the functions "countPoints" and "kdePoints". "kde" uses kernel density to smooth the locations, "count" simply counts the points falling in a grid cell.

Value

tripGrid returns an object of class SpatialGridDataFrame, with one column "z" containing the time spent in each cell in seconds. If kdePoints is used the units are not related to the time values and must be scaled for further use.

See Also

bandwidth.nrd for the calculation of bandwidth values used internally when not supplied by the user

Walrus tracking data set.

Description

Behavior of Pacific Walruses Tracked from the Alaska Coast of the Chukchi Sea.

Details

Data set is provided as a 'trip' object. This is the abstract for the work:

"We tracked movements and haulout foraging behavior of walruses instrumented with satellite-linked data loggers from the Alaskan shores of the Chukchi Sea during the autumn of 2009 (n=13) and 2010 (n=2)." Jay, C. V. and Fischbach, A.S.

Examples

data(walrus818)
plot(walrus818)
lines(walrus818)

World north polygons

Description

A spatial polygons object with coastlines of the northern hemisphere.

Usage

world_north

Format

An object of class SpatialPolygonsDataFrame with 185 rows and 11 columns.

Details

This data set exists purely to avoid requiring reprojection in the vignette, the data uses the same projection as walrus818.

Create a time-continuous KML file

Description

Export track data to a KML file, for use in Google Earth the continuous time slider.

Usage

write_track_kml(
  id,
  lon,
  lat,
  utc,
  z = NULL,
  kml_file = tempfile(fileext = ".kmz"),
  name = NULL,
  altitude_mode = c("absolute", "clampToGround", "clampToSeaFloor", "relativeToGround",
    "relativeToSeaFloor")
)

Arguments

Details

To include altitude set every argument explicitly, by input of separate 'id', 'lon', 'lat', 'utc' and 'z' arguments. If the first argument 'id' is a trip object there is no facility to include the 'z' altitude values.

If 'z' is included it is applied as a third coordinate, with 'altitude_mode' controlling the interpretation, see https://developers.google.com/kml/documentation/altitudemode. If the 'kml_file' ends with ".kmz" the file is compressed, otherwise it must end with ".kml" and the compression archive step is not applied.

Sadly the interactive time slider is only available with the desktop version of Google Earth, the data loads into the browser version but can't be interactive.

Value

character vector, file name location of file produced

Author(s)

Original implementation by Tomislav Hengl in the 'plotKML' package for 'SpatialLinesDataFrame', adapted by M. Sumner for use in continuous-time form.

Examples

kfile <- write_track_kml(walrus818[seq(1, 1000, by = 5), ])
print(kfile)
unlink(kfile)