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Distance based edge lists

Source: R/edge_dist.R
edge_dist.Rd

edge_dist returns edge lists defined by a spatial distance within the user defined threshold. The function accepts a data.table with relocation data, individual identifiers and a threshold argument. The threshold argument is used to specify the criteria for distance between points which defines a group. Relocation data should be in two columns representing the X and Y coordinates.

Usage

edge_dist(
  DT = NULL,
  threshold,
  id = NULL,
  coords = NULL,
  timegroup,
  splitBy = NULL,
  returnDist = FALSE,
  fillNA = TRUE
)

Arguments

DT

input data.table

threshold

distance for grouping points, in the units of the coordinates

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

splitBy

(optional) character string or vector of grouping column name(s) upon which the grouping will be calculated

returnDist

boolean indicating if the distance between individuals should be returned. If FALSE (default), only ID1, ID2 columns (and timegroup, splitBy columns if provided) are returned. If TRUE, another column "distance" is returned indicating the distance between ID1 and ID2.

fillNA

boolean indicating if NAs should be returned for individuals that were not within the threshold distance of any other. If TRUE, NAs are returned. If FALSE, only edges between individuals within the threshold distance are returned.

Value

edge_dist returns a data.table with columns ID1, ID2, timegroup (if supplied) and any columns provided in splitBy. If 'returnDist' is TRUE, column 'distance' is returned indicating the distance between ID1 and ID2.

The ID1 and ID2 columns represent the edges defined by the spatial (and temporal with group_times) thresholds.

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 timegroup (and optional splitBy) arguments expect the names of a column in DT which correspond to the individual identifier, X and Y coordinates, timegroup (generated by group_times) and additional grouping columns.

If provided, the threshold must be provided in the units of the coordinates and must be larger than 0. If the threshold is NULL, the distance to all other individuals will be returned. 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 required to define the temporal groups within which edges are calculated. The intended framework is to group rows temporally with group_times then spatially with edge_dist. If you have already calculated temporal groups without group_times, you can pass this column to the timegroup argument. Note that the expectation is that each individual will be observed only once per timegroup. Caution that accidentally including huge numbers of rows within timegroups can overload your machine since all pairwise distances are calculated within each timegroup.

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. edge_dist will only consider rows within each splitBy subgroup.

See also

Other Edge-list generation: edge_nn()

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

# Temporal grouping
group_times(DT, datetime = 'datetime', threshold = '20 minutes')
#>            ID        X       Y            datetime population minutes timegroup
#>        <char>    <num>   <num>              <POSc>      <int>   <int>     <int>
#>     1:      A 715851.4 5505340 2016-11-01 00:00:54          1       0         1
#>     2:      A 715822.8 5505289 2016-11-01 02:01:22          1       0         2
#>     3:      A 715872.9 5505252 2016-11-01 04:01:24          1       0         3
#>     4:      A 715820.5 5505231 2016-11-01 06:01:05          1       0         4
#>     5:      A 715830.6 5505227 2016-11-01 08:01:11          1       0         5
#>    ---                                                                         
#> 14293:      J 700616.5 5509069 2017-02-28 14:00:54          1       0      1393
#> 14294:      J 700622.6 5509065 2017-02-28 16:00:11          1       0      1394
#> 14295:      J 700657.5 5509277 2017-02-28 18:00:55          1       0      1440
#> 14296:      J 700610.3 5509269 2017-02-28 20:00:48          1       0      1395
#> 14297:      J 700744.0 5508782 2017-02-28 22:00:39          1       0      1396

# Edge list generation
edges <- edge_dist(
    DT,
    threshold = 100,
    id = 'ID',
    coords = c('X', 'Y'),
    timegroup = 'timegroup',
    returnDist = TRUE,
    fillNA = TRUE
  )