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.
Usage
group_lines(
DT = NULL,
threshold = NULL,
projection = NULL,
id = NULL,
coords = NULL,
timegroup = NULL,
sortBy = NULL,
splitBy = NULL,
spLines = NULL
)Arguments
- 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
character string defining the projection to be passed to
sp::CRS. For example, for UTM zone 36S (EPSG 32736), the projection argument is 'EPSG:32736'. See details.- id
Character string of ID column name
- coords
Character vector of X coordinate and Y coordinate column names
- timegroup
timegroup field in the DT within 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.
Value
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.
Details
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 projection argument expects a character string defining
the EPSG code. For example, for UTM zone 36N (EPSG 32736), the projection
argument is "EPSG:32736". See https://spatialreference.org
for a list of EPSG codes. Please note, R spatial has followed updates
to GDAL and PROJ for handling projections, see more at
https://r-spatial.org/r/2020/03/17/wkt.html. It is likely
that build_polys will return "Warning in proj4string(xy) :
CRS object has comment, which is lost in output" due to these changes.
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 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.
See also
Other Spatial grouping:
group_polys(),
group_pts()
Examples
# 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
# EPSG code for example data
utm <- 'EPSG:32736'
group_lines(DT, threshold = 50, projection = utm, sortBy = 'datetime',
id = 'ID', coords = c('X', 'Y'))
#> Warning: GEOS support is provided by the sf and terra packages among others
#> 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')
#> group column will be overwritten by this function
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> 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')
#> group column will be overwritten by this function
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> Warning: GEOS support is provided by the sf and terra packages among others
#> 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