\dm_csml_event_details UCL ELLIS

Statistical Methods for Analysing Time Series Data of Animal Movement


Sarah Chisholm


UCL, Computer Science


Friday, 04 April 2014






MPEB 1.02

Event series

DeepMind/ELLIS CSML Seminar Series


Collecting data to help understand the behaviour and interactions of animals has changed dramatically over the last two decades. When ecologists used to exclusively follow animals by car/foot, observing locations and behaviours in person and recording these manually. It is now possible to do much of this work automatically.

Technologies to collect data on animal movement have improved immensely in recent years. GPS units are becoming more and more accurate, lighter and last longer. They and Inertial Measurement Units (IMUs) do not only record the location of the individual, but include accelerometers, gyroscopes and many other interesting sensors to collect data about animals. These devices are small and light enough to fit on animals as small as pigeons.

Whilst the amount and quality of data exceeds that previously available, the methods to analyse this data are still lagging behind. For example, a method to detect whether individuals or groups are more or less often in close proximity of each other than expected by chance does not exist without underlying assumptions about the shape and size of the individuals' territory and boundaries.

Moreover methods to identify a relationship in the movement of individuals whose movements are not stationary, i.e. no constant mean and variance, still produce spurious results (identifying cointegration when none exists), or are restricted to first order integrated series.

This talk covers two new mathematical methods to allow ecologists and behaviourists to answer questions related to these two key aspects of behaviour and interaction. The methods rely on well-established mathematical theorems, they have been tested on synthetic data and applied to data collected on leopard, wild dog and sheep movements.