build_polys generates a simple feature collection with POLYGONs from a
data.table. The function accepts a data.table with
relocation data, individual identifiers, a projection,
home range type and parameters. The relocation
data is transformed into POLYGONs using either adehabitatHR::mcp or
adehabitatHR::kernelUD for each individual and, optionally,
combination of columns listed in splitBy. Relocation data should be in two
columns representing the X and Y coordinates.
Usage
build_polys(
DT = NULL,
projection = NULL,
hrType = NULL,
hrParams = NULL,
id = NULL,
coords = NULL,
splitBy = NULL,
spPts = NULL
)Arguments
- DT
input data.table
- projection
numeric or character defining the coordinate reference system to be passed to sf::st_crs. For example, either
projection = "EPSG:32736"orprojection = 32736.- hrType
type of HR estimation, either 'mcp' or 'kernel'
- hrParams
a named list of parameters for
adehabitatHRfunctions- id
Character string of ID column name
- coords
Character vector of X coordinate and Y coordinate column names
- splitBy
(optional) character string or vector of grouping column name(s) upon which the grouping will be calculated
- spPts
alternatively, provide solely a SpatialPointsDataFrame with one column representing the ID of each point, as specified by adehabitatHR::mcp or adehabitatHR::kernelUD
Value
build_polys returns a simple feature collection with POLYGONs
for each individual (and optionally splitBy combination).
An error is returned when hrParams do not match the arguments
of the respective hrType
adehabitatHR function.
Details
group_polys uses build_polys for grouping overlapping
polygons created from relocations.
R-spatial evolution
Please note, spatsoc has followed updates from R spatial, GDAL and PROJ for handling projections, see more below and details at https://r-spatial.org/r/2020/03/17/wkt.html.
In addition, build_polys previously used sp::SpatialPoints but has been
updated to use sf::st_as_sf according to the R-spatial evolution, see more
at https://r-spatial.org/r/2022/04/12/evolution.html.
Notes on arguments
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 (and optional splitBy) arguments
expect the names of respective columns in DT which correspond
to the individual identifier, X and Y coordinates, and additional
grouping columns.
The projection argument expects a character string or numeric
defining the coordinate reference system to be passed to sf::st_crs.
For example, for UTM zone 36S (EPSG 32736), the projection
argument is projection = "EPSG:32736" or projection = 32736.
See https://spatialreference.org
for a list of EPSG codes.
The hrType must be either one of "kernel" or "mcp". The
hrParams must be a named list of arguments matching those
of adehabitatHR::kernelUD and adehabitatHR::getverticeshr
or adehabitatHR::mcp.
The splitBy argument offers further control building
POLYGONs. If in your DT, you have multiple
temporal groups (e.g.: years) for example, you can provide the
name of the column which identifies them and build POLYGONs
for each individual in each year.
See also
Other Build functions:
build_lines()
Examples
# Load data.table
library(data.table)
data.table::setDTthreads(1)
# Read example data
DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc"))
# Cast the character column to POSIXct
DT[, datetime := as.POSIXct(datetime, tz = 'UTC')]
#> ID X Y datetime population
#> <char> <num> <num> <POSc> <int>
#> 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
#> ---
#> 14293: J 700616.5 5509069 2017-02-28 14:00:54 1
#> 14294: J 700622.6 5509065 2017-02-28 16:00:11 1
#> 14295: J 700657.5 5509277 2017-02-28 18:00:55 1
#> 14296: J 700610.3 5509269 2017-02-28 20:00:48 1
#> 14297: J 700744.0 5508782 2017-02-28 22:00:39 1
# EPSG code for example data
utm <- 32736
# Build polygons for each individual using kernelUD and getverticeshr
build_polys(DT, projection = utm, hrType = 'kernel',
hrParams = list(grid = 60, percent = 95),
id = 'ID', coords = c('X', 'Y'))
#> Simple feature collection with 10 features and 2 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 691964.9 ymin: 5489527 xmax: 717689.4 ymax: 5516125
#> Projected CRS: +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs
#> ID area geometry
#> A A 192345367 MULTIPOLYGON (((693628 5506...
#> B B 17316394 MULTIPOLYGON (((703312.2 55...
#> C C 151238156 MULTIPOLYGON (((694712.6 55...
#> D D 16127640 MULTIPOLYGON (((694883.4 54...
#> E E 75190273 MULTIPOLYGON (((694705.4 55...
#> F F 115041937 MULTIPOLYGON (((694898.6 55...
#> G G 23395212 MULTIPOLYGON (((702963.8 55...
#> H H 101217169 MULTIPOLYGON (((692020.5 55...
#> I I 176667205 MULTIPOLYGON (((694732 5505...
#> J J 192475536 MULTIPOLYGON (((693703 5506...
# Build polygons for each individual by year
DT[, yr := year(datetime)]
#> ID X Y datetime population yr
#> <char> <num> <num> <POSc> <int> <int>
#> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 2016
#> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 2016
#> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 2016
#> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 2016
#> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 2016
#> ---
#> 14293: J 700616.5 5509069 2017-02-28 14:00:54 1 2017
#> 14294: J 700622.6 5509065 2017-02-28 16:00:11 1 2017
#> 14295: J 700657.5 5509277 2017-02-28 18:00:55 1 2017
#> 14296: J 700610.3 5509269 2017-02-28 20:00:48 1 2017
#> 14297: J 700744.0 5508782 2017-02-28 22:00:39 1 2017
build_polys(DT, projection = utm, hrType = 'mcp',
hrParams = list(percent = 95),
id = 'ID', coords = c('X', 'Y'), splitBy = 'yr')
#> Simple feature collection with 20 features and 2 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 693394.8 ymin: 5490131 xmax: 715229.7 ymax: 5514806
#> Projected CRS: +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs
#> First 10 features:
#> ID area geometry
#> A-2016 A-2016 141208128 POLYGON ((707624.4 5514064,...
#> A-2017 A-2017 68039437 POLYGON ((706582.7 5510383,...
#> B-2016 B-2016 7851014 POLYGON ((699361.8 5511721,...
#> B-2017 B-2017 9473597 POLYGON ((698473.2 5512210,...
#> C-2016 C-2016 90247899 POLYGON ((711698.9 5506990,...
#> C-2017 C-2017 64419958 POLYGON ((706580.1 5510401,...
#> D-2016 D-2016 9478060 POLYGON ((698487.1 5492921,...
#> D-2017 D-2017 7624492 POLYGON ((699711.3 5492069,...
#> E-2016 E-2016 60086909 POLYGON ((707874.5 5507416,...
#> E-2017 E-2017 23034604 POLYGON ((700660.7 5508942,...