3.2 Movement trajectories

We will start with simply creating trajectories between successive locations. As stated above, there are 2 types of trajectories but their are also 2 forms of Type II trajectories if we have time recorded. Depending on the duration between locations we can have uniform time lag between successive relocations termed regular trajectories and non-uniform time lag that results in irregular trajectories. We will begin this section with simply creating irregular trajectories from relocation data because, even though we set up a time schedule to collection locations at uniform times, climate, habitat, and satellites do not always permit such schedules of data collection.

1. Exercise 3.2 - Download and extract zip folder into your preferred location
2. Set working directory to the extracted folder in R under File - Change dir...
3. First we need to load the packages needed for the exercise
   
   library(adehabitatLT)
   library(chron)
   library(spatstat)#for "duplicate" function
4. Now open the script "MovementsScript.R" and run code directly from the script
   
   #We are again going to be using more of the mule deer dataset than from the earlier exercises
   muleys <-read.csv("DCmuleysedited.csv", header=T)
   str(muleys)
5. Check for duplicate locations in dataset. The reason for this is very important and will be apparent shortly.
   
   summary(duplicated(muleys))
   
   #Sort data to address error in code if needed
   #muleys <- muleys[order(muleys$id),]
6. For trajectories of type II (time recorded), the conversion of the date to the format POSIX needs to be done to get proper digits of date into R.
   
   da <- as.POSIXct(strptime(muleys$GPSFixTime,format="%Y.%m.%d %H:%M:%S"))
   head(da)
   muleys$da <- da #attach "da" to muleys dataset
   str(muleys)
   
   #Create time lag between successive locations to censor data if needed.
   timediff <- diff(muleys$da)
   muleys <-muleys[-1,]
   muleys$timediff <-as.numeric(abs(timediff))
   str(muleys)#check to see timediff column was added to muleys
   
   #Look at number of locations by animal ID
   summary(muleys$id)
   
   #Remove outlier locations or known outliers collected too far apart in time
   newmuleys <-subset(muleys, muleys$X > 599000 & muleys$X < 705000
   & muleys$Y > 4167000 & muleys$timediff < 14401)
   muleys <- newmuleys
   str(muleys)
7. Let's create a Spatial Points Data Frame in UTM zone 12 adding ID, time diff, burst to xy coordinates
   
   data.xy = muleys[c("X","Y")]
   #Creates class Spatial Points for all locations
   xysp <- SpatialPoints(data.xy)
   #proj4string(xysp) <- CRS("+proj=utm +zone=12 +ellps=WGS84")
   
   #Creates a Spatial Data Frame from
   sppt<-data.frame(xysp)
   #Creates a spatial data frame of ID
   idsp<-data.frame(muleys[2])
   #Creates a spatial data frame of dt
   dtsp<-data.frame(muleys[24])
   #Creates a spatial data frame of Burst
   busp<-data.frame(muleys[23])
   #Merges ID and Date into the same spatial data frame
   merge<-data.frame(idsp,dtsp,busp)
   #Adds ID and Date data frame with locations data frame
   coordinates(merge)<-sppt
   
   plot(merge)#visualize data
   str(merge)
8. Now create an object of class "ltraj" by animal using the ID field and display by each individual (i.e., ltraj[1]).
   
   ltraj <- as.ltraj(coordinates(merge),merge$da,id=merge$id)
   plot(ltraj)
   plot(ltraj[1])
   head(ltraj[1])#Describes the trajectory for the first deer
   
   #> head(ltraj[1])#Describes the trajectory
   *********** List of class ltraj ***********
   #Type of the traject: Type II (time recorded)
   #Irregular traject. Variable time lag between two locs
   #
   #Characteristics of the bursts:
   # id burst nb.reloc NAs date.begin date.end
   #1 D12 D12 100 0 2011-10-12 06:00:52 2011-10-24 21:00:48
   #
   #infolocs provided. The following variables are available:
   #[1] "pkey"
   
   plot(ltraj[2])
   plot(ltraj[3])
   plot(ltraj[4])
   plot(ltraj[5]
   plot(ltraj[6])
   plot(ltraj[7])
9. Let's create a histogram of time lag (i.e., interval) and distance between successive locations for each deer. This is a nice way to inspect the time lag between locations as you don't want to include a location if too much time has passed since the previous and it also shows why a trajectory is irregular.
   
   hist(ltraj[1], "dt", freq = TRUE)
   windows()#opens a new window to show both figures
   hist(ltraj[1], "dist", freq = TRUE)
   windows()
   
   hist(ltraj[2], "dt", freq = TRUE)
   windows()
   hist(ltraj[2], "dist", freq = TRUE)
   windows()
   
   hist(ltraj[3], "dt", freq = TRUE)
   windows()
   hist(ltraj[3], "dist", freq = TRUE)
   windows()
   
   hist(ltraj[4], "dt", freq = TRUE)
   windows()
   hist(ltraj[4], "dist", freq = TRUE)
   windows()
   
   hist(ltraj[5], "dt", freq = TRUE)
   windows()
   hist(ltraj[5], "dist", freq = TRUE)
   windows()
   
   hist(ltraj[6], "dt", freq = TRUE)
   windows()
   hist(ltraj[6], "dist", freq = TRUE)
   windows()
   
   hist(ltraj[7], "dt", freq = TRUE)
   windows()
   hist(ltraj[7], "dist", freq = TRUE)