Applying Data Science Approaches for Processing High-Resolution Animal Movement Data and Segmenting Trajectories into Behavioral Modes

Researchers: Prof. Sivan Toledo (Computer Science), Dr. Orr Spiegel (Life Sciences)

We develop new computational and modeling approaches for analyzing animal movement data collected at very high resolution with our ATLAS tracking system.


Over the past two years we have been tracking hundreds of animals, generating a rich dataset with millions of data points. This is innovative and important, but coping with these ever-growing and noisy datasets is a challenge that requires data-science tools for processing, filtering, and analysis, before we can address interesting scientific questions.


Our project aims to tackle these challenges in order to allow us to address domain-based questions within the discipline of movement ecology.


The scientific questions that we hope to address with this data include the effect of land-use changes and human development on animal behavior, the functional effectiveness of ecological corridors, and pest-control ecosystem services provided by wildlife.

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