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A Significance Test for Inferring Affiliation Networks from Spatio-Temporal Data.

Furmston T, Morton AJ, Hailes S - PLoS ONE (2015)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University College London, London, United Kingdom.

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 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.

No MeSH data available.


An example of high-resolution positional data collected through GPS technology.This example illustrates the movement patterns of a single sheep from a flock of Merino sheep. The data is split into the period in the holding pen, the period in the race (between the holding pen and the field) and the period in the field. The holding pen and the field are illustrated in the figure on the left, while the tracks of the animal are illustrated (in blue) in the figure on the right. The data set for an entire flock consists of such a trajectory sequence for each individual in the flock. More details of this data set can be found in the data collection section of the paper. Given such a set of movement patterns, the aim of this paper is to provide tools to infer the affiliation network of the group.
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pone.0132417.g001: An example of high-resolution positional data collected through GPS technology.This example illustrates the movement patterns of a single sheep from a flock of Merino sheep. The data is split into the period in the holding pen, the period in the race (between the holding pen and the field) and the period in the field. The holding pen and the field are illustrated in the figure on the left, while the tracks of the animal are illustrated (in blue) in the figure on the right. The data set for an entire flock consists of such a trajectory sequence for each individual in the flock. More details of this data set can be found in the data collection section of the paper. Given such a set of movement patterns, the aim of this paper is to provide tools to infer the affiliation network of the group.

Mentions: This works aims to provide a set of analytical tools that will, given the movement patterns of a group of animals, facilitate the study of affiliative structures within that group. In particular, this paper provides a technique to infer the affiliative network of a group from the movement patterns of its members. Experimental validation of our approach is done using complex real-world high-resolution movement data of Merino sheep, with observations occurring at a rate of 1Hz, that was obtained over the course of hours or days. However, the methods presented in this paper are agnostic to the resolution of the movement patterns, as well as the time-scale of data collection. An illustrative example of the type of data under consideration is given in Fig 1. This example illustrates the movement patterns of a single sheep from a flock of Merino sheep. The data is split into the period in the holding pen, the period in the race (between the holding pen and the field) and the period in the field. The holding pen and the field are illustrated in the figure on the left, while the tracks of the animal are illustrated (in blue) in the figure on the right. The data set for an entire flock consists of such a trajectory sequence for each individual in the flock. Further details of the real-world data sets used to validate the methods presented in this paper are given in the data collection section.


A Significance Test for Inferring Affiliation Networks from Spatio-Temporal Data.

Furmston T, Morton AJ, Hailes S - PLoS ONE (2015)

An example of high-resolution positional data collected through GPS technology.This example illustrates the movement patterns of a single sheep from a flock of Merino sheep. The data is split into the period in the holding pen, the period in the race (between the holding pen and the field) and the period in the field. The holding pen and the field are illustrated in the figure on the left, while the tracks of the animal are illustrated (in blue) in the figure on the right. The data set for an entire flock consists of such a trajectory sequence for each individual in the flock. More details of this data set can be found in the data collection section of the paper. Given such a set of movement patterns, the aim of this paper is to provide tools to infer the affiliation network of the group.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4508121&req=5

pone.0132417.g001: An example of high-resolution positional data collected through GPS technology.This example illustrates the movement patterns of a single sheep from a flock of Merino sheep. The data is split into the period in the holding pen, the period in the race (between the holding pen and the field) and the period in the field. The holding pen and the field are illustrated in the figure on the left, while the tracks of the animal are illustrated (in blue) in the figure on the right. The data set for an entire flock consists of such a trajectory sequence for each individual in the flock. More details of this data set can be found in the data collection section of the paper. Given such a set of movement patterns, the aim of this paper is to provide tools to infer the affiliation network of the group.
Mentions: This works aims to provide a set of analytical tools that will, given the movement patterns of a group of animals, facilitate the study of affiliative structures within that group. In particular, this paper provides a technique to infer the affiliative network of a group from the movement patterns of its members. Experimental validation of our approach is done using complex real-world high-resolution movement data of Merino sheep, with observations occurring at a rate of 1Hz, that was obtained over the course of hours or days. However, the methods presented in this paper are agnostic to the resolution of the movement patterns, as well as the time-scale of data collection. An illustrative example of the type of data under consideration is given in Fig 1. This example illustrates the movement patterns of a single sheep from a flock of Merino sheep. The data is split into the period in the holding pen, the period in the race (between the holding pen and the field) and the period in the field. The holding pen and the field are illustrated in the figure on the left, while the tracks of the animal are illustrated (in blue) in the figure on the right. The data set for an entire flock consists of such a trajectory sequence for each individual in the flock. Further details of the real-world data sets used to validate the methods presented in this paper are given in the data collection section.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University College London, London, United Kingdom.

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 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.

No MeSH data available.