spatsoc is an R package for detecting spatial and temporal groups in GPS relocations. It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks. In addition, the randomization function provides data-stream randomization methods suitable for GPS data.

Usage

spatsoc leverages data.table to modify by reference and iteratively work on subsets of the input data. The first input for all functions in spatsoc is DT, an input data.table. If your data is a data.frame, you can convert it by reference using setDT(DF).

Spatial and temporal grouping

spatsoc is designed to work in two steps: temporal followed by spatial grouping. Considering your specific study species and system, determine a relevant temporal and spatial grouping threshold. This may be 5 minutes and 50 meters or 2 days and 100 meters or any other thresholds - that decision is left to you. In some cases, the spatial grouping function selected is only relevant with certain temporal grouping thresholds. For example, we wouldn't expect a threshold of 5 minutes with group_polys.

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

# Read data as a data.table
DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc"))

# Cast datetime column to POSIXct
DT[, datetime := as.POSIXct(datetime)]

# Temporal groups
group_times(DT, datetime = 'datetime', threshold = '5 minutes')

# Spatial groups
group_pts(
  DT,
  threshold = 50,
  id = 'ID',
  coords = c('X', 'Y'),
  timegroup = 'timegroup'
)

Social network analysis

See the vignette about using spatsoc in social network analysis!

Installation

System dependencies

GEOS

Install GEOS:

• Debian/Ubuntu: apt-get install libgeos-dev
• Arch: pacman -S geos
• Fedora: dnf install geos geos-devel
• Mac: brew install geos
• Windows: see here

Package dependencies

• data.table
• igraph
• sp
• adehabitatHR

Functions

group_times

group_times(DT, datetime, threshold)

• DT: input data.table
• datetime: date time column name in input data.table
• threshold: threshold for grouping

DT

A data.table with a date time formatted column. The input DT will be returned with columns appended. The timegroup column corresponds to the temporal group assigned to each row. Please note that the actual value of the time group is meaningless. Reordered data will return a different time group. What is meaningful, however, is the contents of each group. Each group will contain all rows nearest to the threshold provided.

datetime format

The group_times function expects either one column (POSIXct) or two columns (IDate and ITime).

Given a character column representing the date time, convert it to POSIXct or IDate and ITime:

DT[, datetime := as.POSIXct(datetime)]
DT[, c('idate', 'itime') := IDateTime(datetime)]

These are then provided to the function using the names of the column in the input data.

group_times(DT, datetime = 'datetime', threshold = '5 minutes')

or

group_times(DT, datetime = c('idate', 'itime'), threshold = '5 minutes')

threshold recommendations

The threshold provided to group_times should be related to the fix rate of the input dataset and to the specific study system and species. If relocations are recorded every two hours, a threshold = '2 hours' will group all rows to the nearest two hour group (10am, 12pm, 2pm, 4pm, …). This, however, means that the relocations can be up to one hour apart from each other. Picking a smaller threshold, e.g.: threshold = '15 minutes' may be more relevant in some cases. The flexibility of spatsoc's threshold argument means the user must carefully consider what threshold is reasonable to their specific system.

Limitations of threshold

The threshold of group_times is considered only within the scope of 24 hours and this poses limitations on it:

• threshold must evenly divide into 60 minutes or 24 hours
• multi-day blocks should divide evenly into the range of days in the DT
• number of minutes cannot exceed 60
• threshold cannot be fractional

Columns returned by group_times

The main column returned by group_times is “timegroup”. It represents the temporal group of each row, where those nearest (either above or below) within the threshold are grouped. Its actual value does not have any meaning, but the contents of each group do. That means if the data is reordered, a row may have a different time group, but the other rows in that group should not change.

The extra columns are provided to help the user investigate, troubleshoot and interpret the timegroup.

Warnings and messages

• “columns found in input DT and will be overwritten by this function”

This message is returned to the user when a column matching those returned by group_times is found in the input DT. This is commonly the case when group_times is run multiple times consecutively.

• “no threshold provided, using the time field directly to group”

This message is returned to the user when the threshold is NULL. This is the default setting of threshold and, at times, may be suitable. In this case, the date times in the datetime column will be grouped exactly. Usually, a threshold should be provided.

group_pts

group_pts(DT, threshold, id, coords, timegroup, splitBy)

• DT: input data.table
• threshold: threshold for grouping
• id: column name of IDs in DT
• coords: column names of x and y coordinates in DT
• timegroup: (optional) column name of time group
• splitBy: (optional) column names of extra variables to group on

DT

The input data.table. It will returned with a column named group appended, which represents the spatial (and temporal if timegroup is provided) group.

threshold

The threshold must be in the units of the coordinates.

coords

The coordinates must be planar, such as UTM (of whichever zone your relocations are in).

group_lines

group_lines(DT, threshold, projection, id, coords, timegroup, sortBy, splitBy, spLines)

• DT: input data.table
• threshold: threshold for grouping
• projection: projection of coordinates in DT
• id: column name of IDs in DT
• coords: column names of x and y coordinates in DT
• timegroup: (optional) column name of time group
• sortBy: column name of date time to sort rows for building lines
• splitBy: (optional) column names of extra variables to group on
• spLines: alternatively, provide solely a SpatialLines object

DT

See 3.2.1.

threshold

The threshold argument represents a buffer area around each line. When threshold = 0, the lines are grouped by spatial overlap. If the threshold is greater than 0, the lines buffered, then grouped by spatial overlap.

coords

The coordinates must be planar, such as UTM (of whichever zone your relocations are in).

sortBy

The sortBy argument expects a date time formatted column name, which is used to order the rows for each individual (and splitBy).

group_polys

group_polys(DT, area, hrType, hrParams, projection, id, coords, timegroup, splitBy, spLines)

• DT: input data.table
• area: boolean argument if proportional area should be returned
• hrType: type of home range created
• hrParams: parameters relevant to the type of home range created
• projection: projection of coordinates in DT
• id: column name of IDs in DT
• coords: column names of x and y coordinates in DT
• timegroup: (optional) column name of time group
• splitBy: (optional) column names of extra variables to group on
• spPolys: alternatively, provide solely a SpatialPolygons object

DT and area

If area = FALSE, see 3.2.1. If area = TRUE, the DT will not be appended with a group column instead a data.table with IDs and proportional area overlap will be returned.

The default unit for area overlap is square meters.

coords

The coordinates must be planar, such as UTM (of whichever zone your relocations are in).

hrType and hrParams

Currently, spatsoc offers two types of home ranges provided by the adehabitatHR package: 'mcp' (mcp) and 'kernel' (kernelUD and getverticeshr). The parameters must match the arguments of those functions.

Internally, we match arguments to the functions allowing the user to provide, for example, both the percent (provided to getverticeshr) and grid arguments (provided to mcp).

group_polys(
  DT,
  area = FALSE,
  projection = utm,
  hrType = 'mcp',
  hrParams = list(grid = 60, percent = 95),
  id = 'ID',
  coords = c('X', 'Y')
)

randomizations

randomizations(DT, type, id, datetime, splitBy, iterations)

• DT: input data.table
• type: one of 'daily', 'step' or 'trajectory'
• id: Character string of ID column name
• datetime: field used for providing date time or time group - see details
• splitBy: List of fields in DT to split the randomization process by
• iterations: The number of iterations to randomize

See the vignette Using spatsoc in social network analysis for details about the randomizations function (specifically the section 'Data stream randomization')

Package design

Don't I need to reassign to save the output?

(Almost) all functions in spatsoc use data.table's modify-by-reference to reduce recopying large datasets and improve performance. The exceptions are group_polys(area = TRUE) and randomizations with iterations greater than 1.

Why does a function print the result, but columns aren't added to my DT?

Check that your data.table has columns allocated (with data.table::truelength) and if not, use data.table::setDT. This can happen if you are reading your data from RDS or RData files. See here.

if (truelength(DT) == 0) {
  setDT(DT)
}
# then go to spatsoc
group_times(DT, datetime = 'datetime', threshold = '5 minutes')

Summary information

Here are some useful code chunks for understanding the spatial and temporal extent of your data and the outputs of spatsoc functions.

Number of individuals

# Number of unique individuals
DT[, uniqueN(ID)]

# Number of unique individuals by timegroup
DT[, uniqueN(ID), by = timegroup]

Temporal range

# Min, max datetime
DT[, range(datetime)]

# Difference between relocations in hours
DT[order(datetime), 
   .(difHours = as.numeric(difftime(datetime, shift(datetime), units = 'hours'))), 
   by = ID]

# Difference between relocations in hours
DT[order(datetime), 
   .(difMins = as.numeric(difftime(datetime, shift(datetime), units = 'mins'))), 
   by = ID]

Spatial extent

Simple spatial extents can be calculated for all individuals or by individual.

# All individuals
DT[, .(minX = min(X),
       maxX = max(X),
       minY = min(Y),
       maxY = max(Y),)]

# By individual
DT[, .(minX = min(X),
       maxX = max(X),
       minY = min(Y),
       maxY = max(Y),),
   by = ID]

spatsoc outputs

After using the grouping functions, we can determine the number of individuals in a temporal or spatial group.

# Number of unique individuals by timegroup
DT[, uniqueN(ID), by = timegroup]

# Number of unique individuals by group
DT[, uniqueN(ID), by = group]