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 with grouping and edge-list generating functions. In addition, the randomizations
function provides data-stream randomization methods suitable for GPS data and the get_gbi
function generates group by individual matrices useful for building networks with asnipe::get_network
.
See below for installation and basic usage.
For more details, see the blog post and vignettes:
New edge-list generating functions added:
edge_nn
edge_dist
and dyad id function:
dyad_id
(feedback welcome as always!)
Both documented further in a new vignette: Using edge list and dyad id functions.
Also, our article describing spatsoc
was just published at Methods in Ecology and Evolution. Link here and thanks to reviewers and editors at rOpenSci and at MEE.
More detailed news here.
Install the latest version with remotes
.
remotes::install_github('ropensci/spatsoc')
# or CRAN
install.packages('spatsoc')
spatsoc
depends on rgeos
and requires GEOS installed on the system.
apt-get install libgeos-dev
pacman -S geos
dnf install geos geos-devel
brew install geos
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 text file (e.g.: CSV), you can use data.table::fread()
to import it as a data.table
.
library(spatsoc)
library(data.table)
DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc"))
DT[, datetime := as.POSIXct(datetime, tz = 'UTC')]
group_times
groups rows temporally using a threshold defined in units of minutes (B), hours (C) or days (D).
group_pts
groups points spatially using a distance matrix (B) and a spatial threshold defined by the user (50m in this case). Combined with group_times
, the returned ‘group’ column represents spatiotemporal, point based groups (D).
group_lines
groups sequences of points (forming a line) spatially by buffering each line (A) by the user defined spatial threshold. Combined with group_times
, the returned ‘group’ column represents spatiotemporal, line overlap based groups (B).
group_polys
groups home ranges by spatial and proportional overlap. Combined with group_times
, the returned ‘group’ column represents spatiotemporal, polygon overlap based groups.
edge_dist
and edge_nn
generate edge-lists. edge_dist
measures the spatial distance between individuals (A) and returns all pairs within the user specified distance threshold (B). edge_nn
measures the distance between individuals (C) and returns the nearest neighbour to each individual (D).
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Development of spatsoc
welcomes contribution of feature requests, bug reports and suggested improvements through the issue board.
See details in CONTRIBUTING.md.
Social network analysis functions
randomizations
for data-stream randomization andget_gbi
for generating group by individual matrices.