3.5 Regular Trajectories

  1. Exercise 3.5 - 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(raster)
    library(sp)

  4. Now open the script "RegTrajScript.R" and run code directly from the script
    muleys <-read.csv("DCmuleysedited.csv", header=T)
    str(muleys) #CODE TO SUBSET FOR AN INDIVIDUAL ANIMAL
    muley15 <- subset(muleys, id=="D15")
    str(muley15)
    summary <- table(muley15$UTM_Zone,muley15$id)
    summary
    muley15$id

    #muley15$DT <-as.POSIXct(strptime(muley15$GPSFixTime, format='%Y.%m.%d %H:%M:%OS'))
    #muley15$DT

    #Sort data to address error in code
    muley15 <- muley15[order(muley15$GPSFixTime),]
    muley15[1:10,]#code displays the first 10 records to look at what sorting did to data
    str(muley15)

    ######################################################
    ## Example of a trajectory of type II (time recorded)

    Figure 3.3Figure 3.3: Summaries of distance and time (dt) between relocations for mule deer D16.

    ### Conversion of the date to the format POSIX. Needs to be done to get proper #digits of date into R then POSIXct
    library(chron)
    da <- as.character(muley15$GPSFixTime)
    da <- as.POSIXct(strptime(muley15$GPSFixTime,format="%Y.%m.%d %H:%M:%S"))
    muley15$da <- da

    timediff <- diff(muley15$da)
    muley15 <-muley15[-1,]
    muley15$timediff <-as.numeric(abs(timediff))
    str(muley15)

    newmuleys <-subset(muley15, muley15$X > 599000 & muley15$X < 705000 & muley15$Y > 4167000 & muley15$timediff < 14401)
    muley15 <- newmuleys

    data.xy = muley15[c("X","Y")]
    #Creates class Spatial Points for all locations
    xysp <- SpatialPoints(data.xy)
    #proj4string(xysp) <- CRS("+proj=utm +zone=17 +ellps=WGS84")
    #Creates a Spatial Data Frame from
    sppt<-data.frame(xysp)
    #Creates a spatial data frame of ID
    idsp<-data.frame(muley15[2])
    #Creates a spatial data frame of dt
    dtsp<-data.frame(muley15[24])
    #Creates a spatial data frame of Burst
    busp<-data.frame(muley15[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)
    str(merge)

    ### Creation of an object of class "ltraj"
    ltraj <- as.ltraj(coordinates(merge),merge$da,id=merge$id)
    plot(ltraj)
    ltraj

    #CAN BE USED TO REMOVE TIME FROM DATE IN GPSFIXTIME COLUMN
    #Date <- as.character(muleys$GPSFixTime)
    #Date <- as.POSIXct(strptime(muleys$GPSFixTime,"%Y.%m.%d"))
    #muleys$Date <- Date
    #str(muleys)

    #We want to study the trajectory of the day at the scale of the day. We define #one trajectory per day. The trajectory should begin at 22H00. The following function returns TRUE if #the date is comprised between 06H00 and 23H00 (i.e. results in 3 locations/day bursts)
    foo <- function(date) {
    da <- as.POSIXlt(date)
    ho <- da$hour + da$min
    return(ho>15.9&ho<23.9)
    }
    deer <- cutltraj(ltraj, "foo(date)", nextr = TRUE)
    deerz

    #Remove the first and last burst if needed?
    #deer2 <- deer[-c(1,length(deer))]

    #bind the trajectories
    deer3 <- bindltraj(deer)
    deer3
    plot(deer3)
    is.regular(deer)
    FALSE
    plotltr(deer3, "dist")

    #The relocations have been collected every 3 hours, and there are some missing #data.
    ## The reference date: the hour should be exact (i.e. minutes=0):
    refda <- strptime("00:00", "%H:%M")
    refda
    #Set the missing values
    deerset <- setNA(deer3, refda, 3, units = "hour")
    #Now, look at dt for the bursts:
    plotltr(deerset, "dt")
    #dt is nearly regular: round the date:
    deerset1 <- sett0(deerset, refda, 3, units = "hour")
    plotltr(deerset1, "dt")
    is.regular(deerset1)
    #Deerset1 is now regular

    ##Is the resulting object "sd" ?
    is.sd(deerset1)

    #Show the changes in the distance between successive relocations with the time
    windows()
    plotltr(deerset1, "dist")
    ## Segmentation of the trajectory based on these distances
    lav <- lavielle(deerset1, Lmin=2, Kmax=20)
    ## Choose the number of segments
    chooseseg(lav)
    ## 20 segments seem a good choice
    ## Show the partition
    kk <- findpath(lav, 20)
    kk

    ##Results of code
    *********** List of class ltraj ***********
    #Type of the traject: Type II (time recorded)
    #Regular traject. Time lag between two locs: 10800 seconds

    #Characteristics of the bursts:
    #idburstnb.relocNAsdate.begindate.end
    #1 D15 Segment.1 199 27 2011-10-12 04:00:00 2011-11-05 22:00:00
    #2 D15 Segment.2 2 0 2011-11-06 01:00:00 2011-11-06 03:00:00
    #3 D15 Segment.3 455 64 2011-11-06 06:00:00 2012-01-02 00:00:00
    #4 D15 Segment.4 1 0 2012-01-02 03:00:00 2012-01-02 03:00:00
    #5 D15 Segment.5 2 0 2012-01-02 06:00:00 2012-01-02 09:00:00
    #6 D15 Segment.6 1 0 2012-01-02 12:00:00 2012-01-02 12:00:00
    #7 D15 Segment.7 64 8 2012-01-02 15:00:00 2012-01-10 12:00:00
    #8 D15 Segment.8 3 1 2012-01-10 15:00:00 2012-01-10 21:00:00
    #9 D15 Segment.9 2 0 2012-01-11 00:00:00 2012-01-11 03:00:00
    #10 D15 Segment.10 33 4 2012-01-11 06:00:00 2012-01-15 06:00:00
    #11 D15 Segment.11 5 1 2012-01-15 09:00:00 2012-01-15 21:00:00
    #12 D15 Segment.12 3 0 2012-01-16 00:00:00 2012-01-16 06:00:00
    #13 D15 Segment.13 2 0 2012-01-16 09:00:00 2012-01-16 12:00:00
    #14 D15 Segment.14 336 46 2012-01-16 15:00:00 2012-02-27 12:00:00
    #15 D15 Segment.15 4 1 2012-02-27 15:00:00 2012-02-28 00:00:00
    #16 D15 Segment.16 250 35 2012-02-28 03:00:00 2012-03-30 07:00:00
    #17 D15 Segment.17 1 0 2012-03-30 10:00:00 2012-03-30 10:00:00
    #18 D15 Segment.18 5 2 2012-03-30 13:00:00 2012-03-31 01:00:00
    #19 D15 Segment.19 1164 154 2012-03-31 04:00:00 2012-08-23 13:00:00
    #20 D15 Segment.20 63 9 2012-08-23 16:00:00 2012-08-31 10:00:00

    #Notice that the results show for each burst:
    (1) number of relocations
    (2) number of relocations removed (i.e., NA)
    (3) begin and end dates

    #Now if we reduce the number of segments we get the following bursts:
    ## Segmentation of the trajectory based on these distances
    lav <- lavielle(deerset1, Lmin=2, Kmax=10)
    ## Choose the number of segments
    chooseseg(lav)
    ## 20 segments seem a good choice
    ##Show the partition
    k <- findpath(lav, 10)
    kk

    *********** List of class ltraj ***********
    #Type of the traject: Type II (time recorded)
    #Regular traject. Time lag between two locs: 10800 seconds

    #Characteristics of the bursts:

    #idburstnb.relocNAsdate.begindate.end
    #1 D15 Segment.1 201 27 2011-10-12 04:00:00 2011-11-06 03:00:00
    #2 D15 Segment.2 456 64 2011-11-06 06:00:00 2012-01-02 03:00:00
    #3 D15 Segment.3 2 0 2012-01-02 06:00:00 2012-01-02 09:00:00
    #4 D15 Segment.4 1 0 2012-01-02 12:00:00 2012-01-02 12:00:00
    #5 D15 Segment.5 102 13 2012-01-02 15:00:00 2012-01-15 06:00:00
    #6 D15 Segment.6 10 1 2012-01-15 09:00:00 2012-01-16 12:00:00
    #7 D15 Segment.7 591 82 2012-01-16 15:00:00 2012-03-30 10:00:00
    #8 D15 Segment.8 5 2 2012-03-30 13:00:00 2012-03-31 01:00:00
    #9 D15 Segment.9 1164 154 2012-03-31 04:00:00 2012-08-23 13:00:00
    #10 D15 Segment.10 63 9 2012-08-23 16:00:00 2012-08-31 10:00:00

    #We can look at each segment to inspect the path traveled during the burst:
    plot(kk[1])
    plot(kk[2])
    plot(kk[3])
    plot(kk[6])
    plot(kk[7])
    plot(kk[9])

Figure 3.4


Figure 3.4: Bursts of movements for mule deer D15 after creating segements based for focal use areas.

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