options(Encoding="UTF-8") knitr::opts_chunk$set( fig.width = 8, fig.height = 5, collapse = TRUE, comment = "#>" )
library(segclust2d) data(simulshift) data(simulmode) simulmode$abs_spatial_angle <- abs(simulmode$spatial_angle) simulmode <- simulmode[!is.na(simulmode$abs_spatial_angle), ]
This summary provides information on:
Right now, the function in segclust2d
package accept three different kind of input data:
move
adehabitatLT
Future version may provide support for sftraj
objects as well.
data.frame is the format natively supported by segmentation()
and segclust()
.
If x_data.frame
is a data frame, the syntax is simply:
segmentation(x_data.frame, lmin = 5, Kmax = 25)
Move
object can alternatively be provided to the function. If using
segmentation()
, the user may omit seg.var
argument and the algorithm will
use the movement coordinates as segmentation variables. Alternatively if the
user specifies the segmented variable with argument seg.var
, those variables
must be present in the data associated to the Move
object x_move@data
If x_move
is a Move
object, the syntax is simply:
segmentation(x_move, lmin = 5, Kmax = 25)
ltraj
object can alternatively be provided to the function. If using
segmentation()
, the user may omit seg.var
argument and the algorithm will
use the movement coordinates as segmentation variables. Alternatively if the
user specifies the segmented variable with argument seg.var
, those variables
must be present in the data associated to the ltraj
object x_ltraj@data
If x_ltraj
is a ltraj
object, the syntax is simply:
segmentation(x_ltraj, lmin = 5, Kmax = 25)
sftraj
objects are not supported for the moment.
Computation cost for the algorithm scales non-linearly and can be both memory and time-consuming. Performance depends on computer, but from what we have tested, a segmentation on data of size > 10000 can be quite memory intensive (more than 10Go of RAM) and segmentation-clustering can be quite long for data > 1000 (few minutes to hours). For such dataset we recommend either subsampling if loosing resolution is not a big deal (looking for home-range changes over a year with hourly points might be a lost of time when daily points are sufficient) or splitting the dataset for very long data. Although for segmentation-clustering, clusters will not be easily comparable between the different part of the dataset, if one provides parts where all cluster are present for sure, there should be no problem.
Subsampling is automatically enabled in the function to avoid unwanted memory
saturation or very long computation time. By default argument subsample
is set
to TRUE
. In order to totally disable subsampling you have to provide argument
subsample
:
shiftseg <- segmentation(simulshift, Kmax = 30, lmin=5, seg.var = c("x","y"), subsample = FALSE) mode_segclust <- segclust(simulmode, Kmax = 30, lmin=5, ncluster = c(2,3,4), seg.var = c("speed","abs_spatial_angle"), subsample = FALSE)
By default subsampling is allowed (subsample = TRUE
) and subsampling will
occur if the number of data exceed a threshold (10000 for segmentation, 1000 for
segmentation-clustering). The function will subsample by the lower factor (by 2,
3, 4...) for which the dataset will fall below the threshold once subsampled.
For instance a 2500 rows dataset for segmentation-clustering would be subsampled
by 3 to fall below 1000 rows. The threshold can be changed through argument
subsample_over
.
shiftseg <- segmentation(simulshift, Kmax = 30, lmin=5, seg.var = c("x","y"), subsample_over = 2000) mode_segclust <- segclust(simulmode, Kmax = 30, lmin=5, ncluster = c(2,3,4), seg.var = c("speed","abs_spatial_angle"), subsample_over = 500)
One can also override this automatic subsampling by selecting directly the
subsampling factor through argument subsample_by
.
shiftseg <- segmentation(simulshift, Kmax = 30, lmin=5, seg.var = c("x","y"), subsample_by = 60) mode_segclust <- segclust(simulmode, Kmax = 30, lmin=5, ncluster = c(2,3,4), seg.var = c("speed","abs_spatial_angle"), subsample_by = 2)
lmin
Beware that subsampling will also affect your lmin
argument. If subsampling by
2, lmin
will be divided by 2. The function will tell about the value of lmin
and its adjustment with subsampling with different messages:
lmin <- 240 subsample_by <- 60 cli::cli_alert_success("Using {cli::col_green('lmin = ', lmin )}") lmin <- max(lmin/subsample_by,5) cli::cli_alert_success( "Adjusting lmin to subsampling. {cli::col_grey('Dividing lmin by ',subsample_by,', with a minimum of 5')}") cli::cli_alert("After subsampling, {cli::col_green('lmin = ', lmin)}. {cli::col_grey('Corresponding to lmin = ',lmin*subsample_by, ' on the original time scale')}")
In addition to reducing computation time, subsampling may also help the algorithm. Considering movement at the scale of hours when looking for home-ranges at the scale of months may blur the signal and for such analysis, one data per day may be sufficient. For all analyses, the user should think about the appropriate temporal resolution, with the idea that the finest temporal resolution may not always be appropriate.
Note that subsampling has been implemented in such way that outputs will show all points but segmentation is calculated only on subsampled points. Points used in segmentation can be retrieved through augment
in data column subsample_ind
(The subsample indices for kept points and NA for ignored points).
Outputs may be more easily explored if subsampling is done before providing the data to segclust2d
functions
The package also includes functions in order to calculate unusual covariates,
such as the turning angle at constant step length (here called spatial_angle
,
see Patin et al. 2020 for more details). For the latter, a radius have to be
chosen and can be specified through argument radius
. If no radius is
specified, the default one will be the median of the step length distribution.
Other covariates calculated are : persistence and turning speed (v_p and v_r)
from Gurarie et al (2009), distance travelled between points, speed and smoothed
version of the latter. Covariates dependent on time interval (like speed) are by
default calculated with hours, but you can change this with argument units
as
in the example below.
simple_data <- simulmode[,c("dateTime","x","y")] full_data <- add_covariates(simple_data, coord.names = c("x","y"), timecol = "dateTime", smoothed = TRUE, units ="min") head(full_data)
When pre-processing movement data before segmentation/clustering it is common to interpolate missing data points. This may however cause problem if this leads to repetition of values. This can also arise if the individual has a very stable speed (i.e. a boat or a bird deriving on the sea) leading to very similar values.
When the repetition of identical or very similar values are longer than
parameter lmin
, there are segments with null variance, which cannot be
accounted for by the algorithm. Should such cases arise, the algorithm will fail
and tell you about it:
df <- data.frame(x = rep(1,500), y = rep(2, 500)) segclust(df, seg.var = c("x","y"), lmin = 50, ncluster = 3 )
seg.var <- c("x","y") cli::cli_alert_danger( "Data have repetition of nearly-identical \\ values longer than lmin. {cli::col_grey('The algorithm cannot estimate variance \\ for segment with repeated values. \\ This is potentially caused by interpolation \\ of missing values or rounding of values.')} {cli::symbol$arrow_right} Please check for repeated \\ or very similar values of {seg.var}")
To avoid this problem interpolation should be done rather on the covariates to be segmented rather than the coordinates. Alternatively small and rare gaps of data could be ignored. If the gap is too large there is also the possibility to split the dataset.
Dataset with naturally occurring repetition of similar values (a boat at constant speed) are generally difficult to process with our segmentation/clustering algorithm.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.