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Welcome to T-LoCoH!

Overview | Package Features | Installation | Resources


Overview


Utilzation distribution of a female Springbok antelope in Etosha National Park, Namibia

T-LoCoH (Time Local Convex Hull) is a method for constructing home ranges and exploring spatio-temporal patterns in movement data. It is built upon the LoCoH method but contains with new analytical functions for data that have time values attached (e.g., movement data).

T-LoCoH can analyze any set of point data (with or without time stamps), but it has been tailored for data collected at regular intervals from a GPS device, such as GPS collars used in wildlife tracking studies. T-LoCoH uses the time information from each location to produce models of space-use that have strong fidelity to temporal partitioning strategies.

T-LoCoH can generate home ranges models that differentiate internal space not only by how often the animal was seen there (the traditional approach), but also how the animal used space in terms of time-use patterns and behavior mode. You can think of this as behavior maps.

T-LoCoH is not a one-click solution. To use T-LoCoH effectively, you should understand your research question, have some basic knowledge about the study system, and understand how the method works. The tutorial below does a pretty good job explaining how to use T-LoCoH intelligently for specific types of questions and applications. If you are looking for a quick and dirty home range construction method, we suggest you try the Kernel method or one of the other methods from the AdehabitatHR package.


Package Features


Installation

Installation from R-Forge (recommended)

To install T-LoCoH, type one of the following commands below at the R console. If you get stuck, see the steps for manual installation.

# Windows:
install.packages("tlocoh", dependencies=TRUE, repos=c("http://R-Forge.R-project.org", "http://cran.cnr.berkeley.edu"))
require(tlocoh)

# Mac:
install.packages("tlocoh", dependencies=TRUE, repos=c("http://R-Forge.R-project.org", "http://cran.cnr.berkeley.edu"), type="source")
require(tlocoh)

Notes


Updates

T-LoCoH is still being developed and updates are expected. To receive announcements about updates, please subscribe to the T-LoCoH email list. To see a list of new features and bug-fixes, see the Change Log. To request a feature or report a bug, please email the package administrator.


T-LoCoH Quiz

The image above illustrates:

  1. A schematic showing the diffusion of election workers between red states and blue states.
  2. A MRI showing tendonitis in the lead programmer's left arm.
  3. A T-LoCoH space-use model from simulated data revealing areas of directional travel and temporal-partitioning.

Answer: See Figure 10 in the paper.

Resources