trajectories (version 0.1-2)

Track-class: Classes "Track", "Tracks", and "TracksCollection"

Description

Classes for representing sets of trajectory data, with attributes, for different IDs (persons, objects, etc)

Usage

Track(track, df = NULL, fn = TrackStats)
  Tracks(tracks, tracksData = data.frame(row.names=names(tracks)), 
  		fn = TrackSummary)
  TracksCollection(tracksCollection, tracksCollectionData,
  		fn = TracksSummary)
  TrackSummary(track)
  TracksSummary(tracksCollection)
  ## S3 method for class 'Track':
[(x, i, j, ..., drop = TRUE)
  ## S3 method for class 'TracksCollection':
[(x, i, j, ..., drop = TRUE)
  ## S3 method for class 'Track,data.frame':
coerce(from, to)
  ## S3 method for class 'Tracks,data.frame':
coerce(from, to)
  ## S3 method for class 'TracksCollection,data.frame':
coerce(from, to)

Arguments

track

object of class STIDF-class, representing a single trip

df

optional data.frame with information between track points

tracks

named list with Track objects

tracksData

data.frame with summary data for each Track

tracksCollection

list, with Tracks objects

tracksCollectionData

data.frame, with summary data on tracksCollection

fn

function;

x

object of class Track etc

i

selection of spatial entities

j

selection of temporal entities (see syntax in package xts)

...

selection of attribute(s)

drop

logical

from

from

to

target class

Value

• Functions Track, Tracks and TracksCollection are constructor functions that take the slots as arguments, check object validity, and compute summary statistics on the track and tracks sets.

Objects from the Class

Objects of class Track extend STIDF-class and contain single trips or tracks, objects of class Tracks contain multiple Track objects for a single ID (person, object or tracking device), objects of class TracksCollection contain multiple Tracks objects for different IDs.

References

http://www.jstatsoft.org/v51/i07/

Examples

library(sp)
library(spacetime)
t0 = as.POSIXct(as.Date("2013-09-30",tz="CET"))
# person A, track 1:
x = c(7,6,5,5,4,3,3)
y = c(7,7,6,5,5,6,7)
n = length(x)
t = t0 + cumsum(runif(n) * 60)
require(rgdal)
crs = CRS("+proj=longlat")
#crs = CRS(as.character(NA))
stidf = STIDF(SpatialPoints(cbind(x,y),crs), t, data.frame(co2 = rnorm(n)))
A1 = Track(stidf)
# person A, track 2:
x = c(7,6,6,7,7)
y = c(6,5,4,4,3)
n = length(x)
t = max(t) + cumsum(runif(n) * 60)
stidf = STIDF(SpatialPoints(cbind(x,y),crs), t, data.frame(co2 = rnorm(n)))
A2 = Track(stidf)
# Tracks for person A:
A = Tracks(list(A1=A1,A2=A2))
# person B, track 1:
x = c(2,2,1,1,2,3)
y = c(5,4,3,2,2,3)
n = length(x)
t = max(t) + cumsum(runif(n) * 60)
stidf = STIDF(SpatialPoints(cbind(x,y),crs), t, data.frame(co2 = rnorm(n)))
B1 = Track(stidf)
# person B, track 2:
x = c(3,3,4,3,3,4)
y = c(5,4,3,2,1,1)
n = length(x)
t = max(t) + cumsum(runif(n) * 60)
stidf = STIDF(SpatialPoints(cbind(x,y),crs), t, data.frame(co2 = rnorm(n)))
B2 = Track(stidf)
# Tracks for person A:
B = Tracks(list(B1=B1,B2=B2))
Tr = TracksCollection(list(A=A,B=B))
stplot(Tr, scales = list(draw=TRUE))
stplot(Tr, attr = "direction", arrows=TRUE, lwd = 3, by = "direction")
stplot(Tr, attr = "direction", arrows=TRUE, lwd = 3, by = "IDs")
plot(Tr, col=2, axes=TRUE)
dim(Tr)
dim(Tr[2])
dim(Tr[2][1])
u = stack(Tr) # four IDs
dim(u)
dim(unstack(u, c(1,1,2,2))) # regroups to original
dim(unstack(u, c(1,1,2,3))) # regroups to three IDs
dim(unstack(u, c(1,2,2,1))) # regroups differently
as(Tr, "data.frame")[1:10,] # tracks separated by NA rows
as(Tr, "segments")[1:10,]   # track segments as records
Tr[["distance"]] = Tr[["distance"]] * 1000
Tr$distance = Tr$distance / 1000
Tr$distance