MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales

TL;DR

MultiSegVA is a visual analytics platform that enables ecologists to interactively segment animal movement data across multiple temporal scales, improving analysis without requiring complex parameter tuning.

Contribution

It introduces a novel visual query language and interactive tools for multi-scale segmentation of unlabeled time series data, tailored for movement ecology.

Findings

Effective in real-world ecological case studies

Enhances user control over multi-scale segmentation

Generalizable to other domains

Abstract

Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. MultiSegVA primarily contributes tailored, visual-interactive means and visual analytics paradigms for segmenting unlabeled time series on multiple scales. Further, to flexibly compose the multi-scale segmentation, the platform contributes a new visual query language that links a variety of segmentation techniques. To illustrate our approach, we present a domain-oriented set of segmentation techniques derived in collaboration with movement ecologists. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology, related to behavior analysis after environment-aware segmentation, and after progressive clustering. Expert feedback from movement ecologists shows the effectiveness of tailored visual-interactive means and visual analytics paradigms at segmenting multi-scale data, enabling them to perform semantically meaningful analyses. A third use case demonstrates that MultiSegVA is generalizable to other domains.