group_lines
groups rows into spatial groups by creating trajectories
and grouping based on spatial overlap. The function accepts a
data.table
with relocation data, individual identifiers and a
threshold
. The relocation data is transformed into SpatialLines
and overlapping SpatialLines
are grouped. The threshold
argument is used to specify the criteria for distance between lines.
Relocation data should be in two columns representing the X and Y
coordinates.
group_lines( DT = NULL, threshold = NULL, projection = NULL, id = NULL, coords = NULL, timegroup = NULL, sortBy = NULL, splitBy = NULL, spLines = NULL )
DT | input data.table |
---|---|
threshold | The width of the buffer around the lines in the units of the projection. Supply 0 to compare intersection without buffering. |
projection | PROJ.4 character string |
id | Character string of ID column name |
coords | Character vector of X coordinate and Y coordinate column names |
timegroup | timegroup field in the DT upon which the grouping will be calculated |
sortBy | Character string of date time column(s) to sort rows by. Must be a POSIXct. |
splitBy | (optional) character string or vector of grouping column name(s) upon which the grouping will be calculated |
spLines | Alternatively to providing a DT, provide a SpatialLines object created with the sp package. If a spLines object is provided, groups cannot be calculated by a timegroup or splitBy. |
group_lines
returns the input DT
appended with a
group
column.
This column represents the spatial (and if timegroup
was provided -
spatiotemporal) group calculated by overlapping lines. As with the other
grouping functions, the actual value of group is arbitrary and represents
the identity of a given group where 1 or more individuals are assigned to a
group. If the data was reordered, the group may change, but the contents of
each group would not.
A message is returned when a column named group
already exists in
the input DT
, because it will be overwritten.
The DT
must be a data.table
. If your data is a
data.frame
, you can convert it by reference using
data.table::setDT
.
The id
, coords
, sortBy
(and optional timegroup
and splitBy
) arguments expect the names of respective columns in
DT
which correspond to the individual identifier, X and Y coordinates,
sorting, timegroup (generated by group_times
) and additional grouping
columns.
The sortBy
is used to order the input data.table
when creating
SpatialLines
. It must a POSIXct
to ensure the rows are sorted
by date time. The projection
expects a PROJ.4
character string
(such as those available on https://spatialreference.org/). group_lines
expects planar coordinates (not unprojected latitude, longitude).
The threshold
must be provided in the units of the coordinates. The
threshold
can be equal to 0 if strict overlap is required, else it
needs to be greater than 0. The coordinates must be planar coordinates (e.g.:
UTM). In the case of UTM, a threshold
= 50 would indicate a 50m
distance threshold.
The timegroup
argument is optional, but recommended to pair with
group_times
. The intended framework is to group rows temporally
with group_times
then spatially with group_lines
(or
group_pts
, group_polys
). With group_lines
,
pick a relevant group_times
threshold
such as '1 day'
or
'7 days'
which is informed by your study species and system.
The splitBy
argument offers further control over grouping. If within
your DT
, you have multiple populations, subgroups or other distinct
parts, you can provide the name of the column which identifies them to
splitBy
. The grouping performed by group_lines
will only
consider rows within each splitBy
subgroup.
Other Spatial grouping:
group_polys()
,
group_pts()
# Load data.table library(data.table) # Read example data DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc")) # Subset only individuals A, B, and C DT <- DT[ID %in% c('A', 'B', 'C')] # Cast the character column to POSIXct DT[, datetime := as.POSIXct(datetime, tz = 'UTC')]#> ID X Y datetime population #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 #> --- #> 4265: C 702093.6 5510180 2017-02-28 14:00:44 1 #> 4266: C 702086.0 5510183 2017-02-28 16:00:42 1 #> 4267: C 702961.8 5509447 2017-02-28 18:00:53 1 #> 4268: C 703130.4 5509528 2017-02-28 20:00:54 1 #> 4269: C 702872.3 5508531 2017-02-28 22:00:18 1# Proj4 string for example data utm <- '+proj=utm +zone=36 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs' group_lines(DT, threshold = 50, projection = utm, sortBy = 'datetime', id = 'ID', coords = c('X', 'Y'))#> ID X Y datetime population group #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 1 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 1 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 1 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 1 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 1 #> --- #> 4265: C 702093.6 5510180 2017-02-28 14:00:44 1 1 #> 4266: C 702086.0 5510183 2017-02-28 16:00:42 1 1 #> 4267: C 702961.8 5509447 2017-02-28 18:00:53 1 1 #> 4268: C 703130.4 5509528 2017-02-28 20:00:54 1 1 #> 4269: C 702872.3 5508531 2017-02-28 22:00:18 1 1## Daily movement tracks # Temporal grouping group_times(DT, datetime = 'datetime', threshold = '1 day')#> ID X Y datetime population group timegroup #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 1 1 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 1 1 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 1 1 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 1 1 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 1 1 #> --- #> 4265: C 702093.6 5510180 2017-02-28 14:00:44 1 1 120 #> 4266: C 702086.0 5510183 2017-02-28 16:00:42 1 1 120 #> 4267: C 702961.8 5509447 2017-02-28 18:00:53 1 1 120 #> 4268: C 703130.4 5509528 2017-02-28 20:00:54 1 1 120 #> 4269: C 702872.3 5508531 2017-02-28 22:00:18 1 1 120# Subset only first 50 days DT <- DT[timegroup < 25] # Spatial grouping group_lines(DT, threshold = 50, projection = utm, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup', sortBy = 'datetime')#>#> ID X Y datetime population timegroup group #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 1 1 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 1 1 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 1 1 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 1 1 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 1 1 #> --- #> 857: C 710769.9 5507380 2016-11-24 14:00:55 1 24 63 #> 858: C 710930.9 5507290 2016-11-24 16:00:26 1 24 63 #> 859: C 711004.1 5507310 2016-11-24 18:00:49 1 24 63 #> 860: C 711274.1 5507269 2016-11-24 20:00:24 1 24 63 #> 861: C 711054.3 5506998 2016-11-24 22:00:41 1 24 63## Daily movement tracks by population group_lines(DT, threshold = 50, projection = utm, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup', sortBy = 'datetime', splitBy = 'population')#>#> ID X Y datetime population timegroup group #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 1 1 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 1 1 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 1 1 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 1 1 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 1 1 #> --- #> 857: C 710769.9 5507380 2016-11-24 14:00:55 1 24 63 #> 858: C 710930.9 5507290 2016-11-24 16:00:26 1 24 63 #> 859: C 711004.1 5507310 2016-11-24 18:00:49 1 24 63 #> 860: C 711274.1 5507269 2016-11-24 20:00:24 1 24 63 #> 861: C 711054.3 5506998 2016-11-24 22:00:41 1 24 63