A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space‐Time Cubes

Abstract

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

Comic Strip Narratives in Time Geography

“…The space-time cube has cognitive advantages for communicating narratives [52,53]. In a comparison with other space-time representations (coordinated views, animations, layer superimposition, layer juxtaposition) the cube, despite its propensity for clutter with too many lifelines, offered "one perceptually integrated view in which the story can unfold", with space and time paths having similar saliency [52], and support for transfer and navigation in the cognitive realm [54].…”

Crowdsourcing for Information Visualization: Promises and Pitfalls

“…-Large samples: In most cases, participant sample sizes can be increased by simply running more Human Intelligence Tasks (HITs) 13 . A larger number of participants, first of all, result in larger samples (e.g., 480 participants in [74], 550 in [32]).…”

Visualization Tools for Traffic Bottleneck Analysis

“…This comprehensive visualization allows users to see congestion patterns at multiple spatial and temporal levels derived from the work of Shneiderman et al, [23], Bach et al, [24], and Andrienko and Andrienko [25]. The elements graph uses a graphic traffic flow representation of the queued segments associated within the bottleneck (i.e., elements) on the y-axis and the time of day on the x-axis.…”