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What can spatial collectives tell us about their environment?

What can spatial collectives tell us about their environment?

Wood, Zena ORCID: 0000-0001-8843-9832 (2014) What can spatial collectives tell us about their environment? In: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014). IEEE, Piscataway, NJ, US, pp. 329-336. ISBN 9781479945184 (doi:https://doi.org/10.1109/CIDM.2014.7008686)

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Abstract

Understanding how large groups of individuals move within their environment, and the social interactions that occur during this movement, is central to many fundamental interdisciplinary research questions; ranging from understanding the evolution of cooperation, to managing human crowd behaviour. If we could understand how groups of individuals interact with their environment, and any role that the environment plays in their behaviour, we could design and develop space to better suit their needs. Spatiotemporal datasets that record the movement of large groups of individuals are becoming increasingly available. A method, based on a set of coherence criteria, has previously been developed to identify different types of collective within such datasets. However, further investigations have revealed that the method can be used to reveal important information about the environment. This paper applies the method to a spatiotemporal dataset that records the movements of ships within the Solent, in the UK, over a twenty-four hour period to explore what can be inferred from the movement of groups of individuals, referred to as spatial collectives, regarding the environment.

Item Type: Conference Proceedings
Title of Proceedings: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014)
Additional Information: Symposium Series on Computational Intelligence, 9-12 December 2014, Orlando, Florida, US
Uncontrolled Keywords: Spatial coherence
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Nov 2020 22:35
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/15487

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