Volume-Based Space-Time Cube for Large-Scale Continuous Spatial Time Series

TL;DR

This paper introduces VolumeSTCube, a novel visualization framework that transforms large-scale spatial time series data into continuous volumetric representations, improving analysis by reducing occlusion and enhancing pattern visibility.

Contribution

It proposes a new volumetric approach for space-time cubes that effectively visualizes large-scale continuous spatiotemporal data, addressing occlusion and depth ambiguity issues.

Findings

Outperforms baseline methods in large-scale data visualization

Reduces visual occlusion through volume rendering techniques

Enhances pattern detection with surface rendering and interaction design

Abstract

Spatial time series visualization offers scientific research pathways and analytical decision-making tools across various spatiotemporal domains. Despite many advanced methodologies, the seamless integration of temporal and spatial information remains a challenge. The space-time cube (STC) stands out as a promising approach for the synergistic presentation of spatial and temporal information, with successful applications across various spatiotemporal datasets. However, the STC is plagued by well-known issues such as visual occlusion and depth ambiguity, which are further exacerbated when dealing with large-scale spatial time series data. In this study, we introduce a novel technical framework termed VolumeSTCube, designed for continuous spatiotemporal phenomena. It first leverages the concept of the STC to transform discretely distributed spatial time series data into continuously volumetric data. Subsequently, volume rendering and surface rendering techniques are employed to visualize the transformed volumetric data. Volume rendering is utilized to mitigate visual occlusion, while surface rendering provides pattern details by enhanced lighting information. Lastly, we design interactions to facilitate the exploration and analysis from temporal, spatial, and spatiotemporal perspectives. VolumeSTCube is evaluated through a computational experiment, a real-world case study with one expert, and a controlled user study with twelve non-experts, compared against a baseline from prior work, showing its superiority and effectiveness in largescale spatial time series analysis.