Combining spatial and telemetric features for learning animal movement models

Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to data and demonstrate its effectiveness via asymptotic analysis and synthetic experiments. We also apply our model to real datasets, and show that it outperforms the most popular radio telemetry software package used in ecology. We conclude that integration of different data sources under a single statistical framework, coupled with appropriate parameter and state estimation procedures, produces both accurate location estimates and an interpretable statistical model of animal movement.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
Pages260-267
Number of pages8
StatePublished - Dec 1 2010
Event26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 - Catalina Island, CA, United States
Duration: Jul 8 2010Jul 11 2010

Publication series

NameProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Other

Other26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
CountryUnited States
CityCatalina Island, CA
Period7/8/107/11/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Combining spatial and telemetric features for learning animal movement models'. Together they form a unique fingerprint.

Cite this