The spatsoc package provides functionality for analyzing animal relocation data in time and space to identify potential interactions among individuals and build gambit-of-the-group data for constructing social networks.

The package contains grouping and edge list generating functions that are used for identifying spatially and temporally explicit groups from input data. In addition, we provide social network analysis functions for randomizing individual identifiers within groups, designed to test whether social networks generated from animal relocation data were based on non-random social proximity among individuals and for generating group by individual matrices.

The functions were developed for application across animal relocation data, for example, proximity based social network analyses and spatial and temporal clustering of points.

Data preparation

spatsoc expects a data.table for all of its functions. If you have a data.frame, you can use data.table::setDT() to convert it by reference. If your data is a CSV, you can use data.table::fread() to import it as a data.table.

The data consist of relocations of 10 individuals over 365 days. Using these data, we can compare the various grouping methods available in spatsoc. Note: these examples will use a subset of the data, only individuals H, I and J.

# Load packages
library(spatsoc)
library(data.table)

# Read in spatsoc's example data
DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc"))

# Use subset of individuals
DT <- DT[ID %in% c('H', 'I', 'J')]

# Cast character column 'datetime' as POSIXct
DT[, datetime := as.POSIXct(datetime, tz = 'UTC')]
ID X Y datetime population
H 701724.1 5504325 2016-11-01 00:00:49 1
H 701648.5 5504276 2016-11-01 02:00:33 1
I 711042.0 5506384 2016-11-01 00:00:24 1
I 711229.0 5506446 2016-11-01 02:00:33 1
J 707568.6 5500406 2016-11-01 00:00:56 1
J 707566.5 5500404 2016-11-01 02:00:21 1

Temporal grouping

The group_times function is used to group relocations temporally. It is flexible to a threshold provided in units of minutes, hours or days. Since GPS fixes taken at regular intervals have some level of variability, we will provide a time threshold (threshold), to consider all fixes within this threshold taken at the same time. Alternatively, we may want to understand different scales of grouping, perhaps daily movement trajectories or seasonal home range overlap.

group_times(DT, datetime = 'datetime', threshold = '5 minutes')
ID X Y datetime minutes timegroup
I 711042.0 5506384 2016-11-01 00:00:24 0 1
H 701724.1 5504325 2016-11-01 00:00:49 0 1
J 707568.6 5500406 2016-11-01 00:00:56 0 1
J 707566.5 5500404 2016-11-01 02:00:21 0 2
H 701648.5 5504276 2016-11-01 02:00:33 0 2
I 711229.0 5506446 2016-11-01 02:00:33 0 2
J 707562.6 5500374 2016-11-01 04:00:41 0 3
I 711124.0 5506407 2016-11-01 04:00:44 0 3
H 701607.2 5504291 2016-11-01 04:00:54 0 3

A message is returned when group_times is run again on the same DT, as the columns already exist in the input DT and will be overwritten.

group_times(DT, datetime = 'datetime', threshold = '2 hours')
## minutes, timegroup columns found in input DT and will be overwritten by this function
ID X Y datetime hours timegroup
I 711042.0 5506384 2016-11-01 00:00:24 0 1
H 701724.1 5504325 2016-11-01 00:00:49 0 1
J 707568.6 5500406 2016-11-01 00:00:56 0 1
J 707566.5 5500404 2016-11-01 02:00:21 2 2
H 701648.5 5504276 2016-11-01 02:00:33 2 2
I 711229.0 5506446 2016-11-01 02:00:33 2 2
J 707562.6 5500374 2016-11-01 04:00:41 4 3
I 711124.0 5506407 2016-11-01 04:00:44 4 3
H 701607.2 5504291 2016-11-01 04:00:54 4 3
group_times(DT, datetime = 'datetime', threshold = '5 days')
## hours, timegroup columns found in input DT and will be overwritten by this function
ID X Y datetime block timegroup
J 707301.3 5500479 2016-11-02 02:00:21 62 1
J 707528.9 5500357 2016-11-02 08:00:49 62 1
J 707709.5 5499555 2016-11-02 22:00:44 62 1
I 710897.1 5506132 2016-11-07 18:00:53 63 2
I 710790.7 5506108 2016-11-08 10:00:21 63 2
I 702086.8 5505461 2016-11-10 06:00:26 63 2
J 707639.8 5500485 2016-11-12 22:00:15 64 3
I 701729.8 5504275 2016-11-13 06:00:21 64 3
H 701760.1 5504363 2016-11-14 14:00:11 64 3

Spatial grouping

The group_pts function compares the relocations of all individuals in each timegroup and groups individuals based on a distance threshold provided by the user. The group_pts function uses the “chain rule” where three or more individuals that are all within the defined threshold distance of at least one other individual are considered in the same group. For point based spatial grouping with a distance threshold that does not use the chain rule, see edge_dist below.

group_times(DT = DT, datetime = 'datetime', threshold = '15 minutes')
group_pts(DT, threshold = 50, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup')
## block, timegroup columns found in input DT and will be overwritten by this function
ID X Y timegroup group
H 699126.1 5508836 771 771
I 699130.0 5508761 771 771
J 699138.0 5508797 771 771
H 699930.5 5508032 772 772
H 700139.2 5507325 773 773
I 700131.7 5507321 773 773
H 700012.2 5508010 774 774
I 700015.0 5508001 774 774
J 700002.3 5508005 774 774

The group_lines function groups individuals whose trajectories intersect in a specified time interval. This represents a coarser grouping method than group_pts which can help understand shared space at daily, weekly or other temporal resolutions.

utm <- '+proj=utm +zone=21 ellps=WGS84'
group_times(DT = DT, datetime = 'datetime', threshold = '1 day')
group_lines(DT, threshold = 50, projection = utm, 
            id = 'ID', coords = c('X', 'Y'),
            timegroup = 'timegroup', sortBy = 'datetime')
## minutes, timegroup columns found in input DT and will be overwritten by this function
## group column will be overwritten by this function
ID timegroup group
H 1 1
I 1 121
J 1 204
H 2 2
I 2 122
J 2 205
H 3 3
I 3 123
J 3 206

The group_polys function groups individuals whose home ranges intersect. This represents the coarsest grouping method, to provide a measure of overlap across seasons, years or all available relocations. It can either return the proportion of home range area overlapping between individuals or simple groups. Home ranges are calculated using adehabitatHR::kernelUD or adehabitatHR::mcp. Alternatively, a SpatialPolygonsDataFrame can be input to the spPolys argument.

utm <- '+proj=utm +zone=21 ellps=WGS84'
group_times(DT = DT, datetime = 'datetime', threshold = '8 days')
group_polys(DT = DT, area = TRUE, hrType = 'mcp',
           hrParams = list('percent' = 95),
           projection = utm,
           coords = c('X', 'Y'), id = 'ID')
## timegroup columns found in input DT and will be overwritten by this function
ID1 ID2 area proportion
H H 81071930 1.0000000
H I 57514743 0.7094286
H J 66161291 0.8160814
I H 57514743 0.4573709
I I 125750781 1.0000000
I J 93471355 0.7433064
J H 66161291 0.4993401
J I 93471355 0.7054578
J J 132497451 1.0000000

Edge list generation

The edge_dist function calculates the geographic distance between between individuals within each timegroup and returns all paired relocations within the spatial threshold. edge_dist uses a distance matrix like group_pts, but, in contrast, does not use the chain rule to group relocations.

group_times(DT = DT, datetime = 'datetime', threshold = '15 minutes')
edge_dist(DT, threshold = 50, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup', fillNA = TRUE)
## block, timegroup columns found in input DT and will be overwritten by this function
timegroup ID1 ID2
158 H NA
158 I NA
158 J NA
159 H I
159 I H
159 J NA
160 H I
160 I H
160 J NA

The edge_nn function calculates the nearest neighbour to each individual within each time group. If the optional distance threshold is provided, it is used to limit the maximum distance between neighbours. edge_nn returns an edge list of each individual and their nearest neighbour.

group_times(DT = DT, datetime = 'datetime', threshold = '15 minutes')
edge_nn(DT, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup')
## minutes, timegroup columns found in input DT and will be overwritten by this function
timegroup ID NN
1 H J
1 I J
1 J I
2 H J
2 I J
2 J I

Notes

Package dependencies for spatsoc are sp, rgeos, igraph, adehabitatHR and data.table. data.table provides efficient methods for manipulating large (or small) datasets. As a result, input DT for all spatsoc functions must be a data.table and if it isn't, you can simply use data.table::setDT(df) to convert it by reference.

In addition, since the rgeos package is used in most functions (group_lines and group_polys) the input DT's coordinate system is important. rgeos expects planar coordinates and this requirement is carried forward for spatsoc. Since rgeos is used, system dependencies include GEOS.