vignettes/v01_preparing_data.Rmd
v01_preparing_data.Rmd
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)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:
#> ✔ Using lmin = 240
#> ✔ Adjusting lmin to subsampling.
#> Dividing lmin by 60, with a minimum of 5
#> → After subsampling, lmin = 5.
#> Corresponding to lmin = 300 on the original time scaleIn 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 )#> ✖ Data have repetition of nearly-identical values longer than lmin.
#> The algorithm cannot estimate variance for segment with repeated values. This is potentially caused by interpolation of missing values or rounding of values.
#> → Please check for repeated or very similar values of x and yTo 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.