Build Polygons

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" or projection = 32736.

hrType

type of HR estimation, either 'mcp' or 'kernel'

hrParams

a named list of parameters for adehabitatHR functions

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

group_polys

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
#>     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
#>     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,...