# Beyond boundaries: a location-based toolkit for quantifying group dynamics in diverse contexts

## TL;DR

This paper introduces a toolkit for analyzing group dynamics in open environments, bridging the gap between animal and human movement studies.

## Contribution

A general-purpose Python toolkit for quantifying human group dynamics in unbounded environments using location-based metrics.

## Key findings

- The toolkit integrates cognitive factors like decision-making and situational awareness into movement analysis.
- Metrics from the toolkit predict group performance in strategic exercises based on prior movement data.
- The approach is demonstrated using GPS data from military foot marches in open environments.

## 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.