Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement Analysis

• URL: http://arxiv.org/abs/2412.02879v1
• Date: Tue, 03 Dec 2024 22:25:44 GMT
• Title: Pairwise Spatiotemporal Partial Trajectory Matching for Co-movement Analysis
• Authors: Maria Cardei, Sabit Ahmed, Gretchen Chapman, Afsaneh Doryab,
• Abstract summary: Pairwise movement analysis involves identifying individuals within specific time frames.<n>We propose a novel method for partialtemporal matching that transforms data into interpretable images based on time windows.<n>We evaluate our method on a co-walking classification task, demonstrating its effectiveness in a novel co-behavior identification application.<n>This approach offers a powerful, interpretable framework fortemporal behavior analysis, with potential applications in social behavior research, urban planning, and healthcare.
• Score: 1.0942776587291776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatiotemporal pairwise movement analysis involves identifying shared geographic-based behaviors between individuals within specific time frames. Traditionally, this task relies on sequence modeling and behavior analysis techniques applied to tabular or video-based data, but these methods often lack interpretability and struggle to capture partial matching. In this paper, we propose a novel method for pairwise spatiotemporal partial trajectory matching that transforms tabular spatiotemporal data into interpretable trajectory images based on specified time windows, allowing for partial trajectory analysis. This approach includes localization of trajectories, checking for spatial overlap, and pairwise matching using a Siamese Neural Network. We evaluate our method on a co-walking classification task, demonstrating its effectiveness in a novel co-behavior identification application. Our model surpasses established methods, achieving an F1-score up to 0.73. Additionally, we explore the method's utility for pair routine pattern analysis in real-world scenarios, providing insights into the frequency, timing, and duration of shared behaviors. This approach offers a powerful, interpretable framework for spatiotemporal behavior analysis, with potential applications in social behavior research, urban planning, and healthcare.

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