# [AniMov] interpretation of kerneloverlap and kerneloverlaphr

Elodie Camprasse elodie.camprasse at gmail.com
Mon Aug 18 06:34:07 CEST 2014

```G'day everyone,

I am working on tracking data from penguins and would like to determine the
consistency of space use from each of my individuals from one trip to the
next. I calculated kerneloverlap between consecutive trips (standard kernel)
and wanted to compare the results of the kerneloverlaphr after using the
Brownian bridge model. I am not understanding the results I am getting,
however, and I was hoping someone could help me understand them. I pretty
much get opposite results (in the case of kerneloverlap, the individuals are
consistent from one trip to the next, and in the case of kerneloverlaphr,
they are not).

I have created a reproducible example (csv attached) where an individual has
GPS coordinates for two consecutive trips.

I used the following code:

# Try the kerneloverlap approach

test2_kernel_df<-SpatialPointsDataFrame(coords=test2_kernel[, c(2,3)],
data=test2_kernel[, c(1,4)], proj4string = CRS("+proj=utm +zone=55 +south
+ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"), bbox = NULL)

test2_kernel_BA<-kerneloverlap(test2_kernel_df[,1], grid=200, meth="BA")

test2_kernel_BA

test2_kernel_UDOI<-kerneloverlap(test2_kernel_df[,1], grid=200, meth="UDOI")

test2_kernel_UDOI

# Try the kernelbb and then kerneloverlaphr approach

test2_kernel\$timestamp
<-as.POSIXct(strptime(as.character(test2_kernel\$timestamp),"%d/%m/%Y
%H:%M"), "GMT")

test2_coords<-test2_kernel[, c(2,3)]

test2_coords_meters<-project(as.matrix(test2_coords), "+proj=utm +zone=55
+south +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")

test2_coords_meters<-as.data.frame(test2_coords_meters)

colnames(test2_coords_meters)<-c("Longitude_meters", "Latitude_meters")

test2_kernel\$Longitude_meters<-test2_coords_meters\$Longitude_meters

test2_kernel\$Latitude_meters<-test2_coords_meters\$Latitude_meters

test2_ltraj<-as.ltraj(xy =
test2_kernel[,c("Longitude_meters","Latitude_meters")], date =
test2_kernel\$timestamp, id=test2_kernel\$id)

lik <- liker(test2_ltraj, sig2 = 4, rangesig1 = c(0, 10))

test2_kernelbb<-kernelbb(test2_ltraj, sig1=5, sig2=4, grid = 200)

test2_kernelbb_BA<-kerneloverlaphr(test2_kernelbb, grid=200, meth="BA")

test2_kernelbb_BA

test2_kernelbb_UDOI<-kerneloverlaphr(test2_kernelbb, grid=200, meth="UDOI")

test2_kernelbb_UDOI.

In the kerneloverlap approach, I am getting high indices, like this:

test2_kernel_BA

1st_trip  2nd_trip

1st_trip 0.9998753 0.8604860

2nd_trip 0.8604860 0.9998635

> test2_kernel_UDOI

1st_trip  2nd_trip

1st_trip 1.4040774 0.7812684

2nd_trip 0.7812684 1.3015604

In the kerneloverlap approach, I am getting low indices, like this:

test2_kernelbb_BA
1st_trip  2nd_trip
1st_trip 1.0000000 0.1375754
2nd_trip 0.1375754 1.0000000
> test2_kernelbb_UDOI
1st_trip   2nd_trip
1st_trip 6.46508189 0.02059335
2nd_trip 0.02059335 2.75579098

Could anyone tell me what I'm missing please? Your help would be greatly
appreciated!

Kind regards,

Elodie

Elodie Camprasse

Hawthorn, VIC 3122

Australia

Email:  <mailto:elodie.camprasse at gmail.com> elodie.camprasse at gmail.com

Website:  <http://hors-des-sentiers-battus.e-monsite.com/>
http://hors-des-sentiers-battus.e-monsite.com/

Mobile:  <tel:%28%2B61%29%20049%20794%200793> (+61) 049 794 0793

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