title: A Significance Test for Inferring Affiliation Networks from Spatio-Temporal Data.
abstract: Scientists have long been interested in studying social structures within groups of gregarious animals. However, obtaining evidence about interactions between members of a group is difficult. Recent technologies, such as Global Positioning System technology, have made it possible to obtain a vast wealth of animal movement data, but inferring the underlying (latent) social structure of the group from such data remains an important open problem. While intuitively appealing measures of social interaction exist in the literature, they typically lack formal statistical grounding. In this article, we provide a statistical approach to the problem of inferring the social structure of a group from the movement patterns of its members. By constructing an appropriate null model, we are able to construct a significance test to detect meaningful affiliations between members of the group. We demonstrate our method on large-scale real-world data sets of positional data of flocks of Merino sheep, Ovis aries.
authors.affiliation: Department of Computer Science, University College London, London, United Kingdom.
authors.forename: Thomas
authors.initials: T
authors.lastname: Furmston
authors.affiliation: Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.
authors.forename: A Jennifer
authors.initials: AJ
authors.lastname: Morton
authors.affiliation: Department of Computer Science, University College London, London, United Kingdom.
authors.forename: Stephen
authors.initials: S
authors.lastname: Hailes
mesh_terms: D000818:Animals; D001522:Behavior, Animal; D040362:Geographic Information Systems; D008962:Models, Theoretical; D009068:Movement; D012756:Sheep; D012919:Social Behavior