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Beyond Boundaries: A Location-Based Toolkit for Quantifying Group Dynamics in Diverse Contexts

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Title: Beyond Boundaries: A Location-Based Toolkit for Quantifying Group Dynamics in Diverse Contexts
Language: English
Authors: Seth Elkin-Frankston (ORCID 0000-0002-8913-0199), James McIntyre, Tad T. Brunyé, Aaron L. Gardony, Clifford L. Hancock, Meghan P. O'Donovan, Victoria G. Bode, Eric L. Miller
Source: Cognitive Research: Principles and Implications. 2025 10.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 18
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF)
US Army Futures Command, Combat Capabilities Development Command Soldier Center (DEVCOM)
Contract Number: 1934553
1931978
Document Type: Journal Articles
Reports - Research
Descriptors: Group Dynamics, Military Personnel, Measures (Individuals), Computer Software, Data Collection, Data Analysis, Geographic Information Systems, Cognitive Processes, Prediction
DOI: 10.1186/s41235-025-00617-6
ISSN: 2365-7464
Abstract: Existing toolkits for analyzing movement dynamics in animal ecology primarily focus on individual or group behavior in habitats without predefined boundaries, while methods for studying human activity often cater to bounded environments, such as team sports played on defined fields. This leaves a gap in tools for modeling and analyzing human group dynamics in large-scale, unbounded, or semi-constrained environments. Examples of such contexts include tourist groups, cycling teams, search and rescue teams, and military units. To address this issue, we survey existing methods and metrics for characterizing individual and collective movement in humans and animals. Using a rich GPS dataset from groups of military personnel engaged in a foot march, we develop a comprehensive, general-purpose toolkit for quantifying group dynamics using location-based metrics during goal-directed movement in open environments. This toolkit includes a repository of Python functions for extracting and analyzing movement data, integrating cognitive factors such as decision-making, situational awareness, and group coordination. By extending location-based analytics to non-traditional domains, this toolkit enhances the understanding of collective movement, group behavior, and emergent properties shaped by cognitive processes. To demonstrate its practical utility, we present a use case utilizing metrics derived from the foot march data to predict group performance during a subsequent strategic and tactical exercise, highlighting the influence of cognitive and decision-making behaviors on team effectiveness.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1460990
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Seth+Elkin-Frankston%22">Seth Elkin-Frankston</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8913-0199">0000-0002-8913-0199</externalLink>)<br /><searchLink fieldCode="AR" term="%22James+McIntyre%22">James McIntyre</searchLink><br /><searchLink fieldCode="AR" term="%22Tad+T%2E+Brunyé%22">Tad T. Brunyé</searchLink><br /><searchLink fieldCode="AR" term="%22Aaron+L%2E+Gardony%22">Aaron L. Gardony</searchLink><br /><searchLink fieldCode="AR" term="%22Clifford+L%2E+Hancock%22">Clifford L. Hancock</searchLink><br /><searchLink fieldCode="AR" term="%22Meghan+P%2E+O'Donovan%22">Meghan P. O'Donovan</searchLink><br /><searchLink fieldCode="AR" term="%22Victoria+G%2E+Bode%22">Victoria G. Bode</searchLink><br /><searchLink fieldCode="AR" term="%22Eric+L%2E+Miller%22">Eric L. Miller</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Existing toolkits for analyzing movement dynamics in animal ecology primarily focus on individual or group behavior in habitats without predefined boundaries, while methods for studying human activity often cater to bounded environments, such as team sports played on defined fields. This leaves a gap in tools for modeling and analyzing human group dynamics in large-scale, unbounded, or semi-constrained environments. Examples of such contexts include tourist groups, cycling teams, search and rescue teams, and military units. To address this issue, we survey existing methods and metrics for characterizing individual and collective movement in humans and animals. Using a rich GPS dataset from groups of military personnel engaged in a foot march, we develop a comprehensive, general-purpose toolkit for quantifying group dynamics using location-based metrics during goal-directed movement in open environments. This toolkit includes a repository of Python functions for extracting and analyzing movement data, integrating cognitive factors such as decision-making, situational awareness, and group coordination. By extending location-based analytics to non-traditional domains, this toolkit enhances the understanding of collective movement, group behavior, and emergent properties shaped by cognitive processes. To demonstrate its practical utility, we present a use case utilizing metrics derived from the foot march data to predict group performance during a subsequent strategic and tactical exercise, highlighting the influence of cognitive and decision-making behaviors on team effectiveness.
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