Reimagining TaxiVis through an Immersive Space-Time Cube metaphor and reflecting on potential benefits of Immersive Analytics for urban data exploration

TL;DR

This paper investigates how TaxiVis, a landmark 2013 spatio-temporal visualization of NYC taxiOD data, could be reimagined within an Immersive Analytics (IA) framework. The authors prototype an Immersive TaxiVis that integrates a Space-Time Cube (STC) metaphor, 3D query prisms, embedded time-series views, and bi-manual interactions to extend traditional querying and exploration into immersive space. They demonstrate how immersive extensions can reveal temporal patterns more clearly, support new interaction metaphors, and enable complementary egocentric perspectives and collaboration. The work discusses benefits, limitations, and a roadmap toward scalable IA applications for urban analytics and the specific Immersive TaxiVis system, highlighting both practical implications and future research directions.

Abstract

Current visualization research has identified the potential of more immersive settings for data exploration, leveraging VR and AR technologies. To explore how a traditional visualization system could be adapted into an immersive framework, and how it could benefit from this, we decided to revisit a landmark paper presented ten years ago at IEEE VIS. TaxiVis, by Ferreira et al., enabled interactive spatio-temporal querying of a large dataset of taxi trips in New York City. Here, we reimagine how TaxiVis' functionalities could be implemented and extended in a 3D immersive environment. Among the unique features we identify as being enabled by the Immersive TaxiVis prototype are alternative uses of the additional visual dimension, a fully visual 3D spatio-temporal query framework, and the opportunity to explore the data at different scales and frames of reference. By revisiting the case studies from the original paper, we demonstrate workflows that can benefit from this immersive perspective. Through reporting on our experience, and on the vision and reasoning behind our design decisions, we hope to contribute to the debate on how conventional and immersive visualization paradigms can complement each other and on how the exploration of urban datasets can be facilitated in the coming years.

Figures (6)

• Figure 1: Screenshot of the original TaxiVis system illustrating the application of temporal constraints (A) and of visually-defined spatial queries (B). Coordinated views depict associated trip data for each query over time (D). Source: Ferreira et al. ferreira2013visual (©2013 IEEE).
• Figure 2: Extensions enable pairwise comparisons between neighborhoods with bi-manual interaction, either through choropleth prism stacks (left) or two-handed brushes and embedded plots (right).
• Figure 3: Immersion allows the integration of complementary frames of reference, such as egocentric room-scale (left) and egocentric view of 360-degree street images (right).
• Figure 4: Movable 2D panels complement the interface, offering similar attribute exploration functionality as in TaxiVis.
• Figure 5: Overview of taxi activity in the week of Hurricane Sandy in October 2012. The STC point cloud and its projections (a) evidence the sudden drop in activity from Monday to Tuesday and the gradual recovery in Lower Manhattan over the next days. The insets highlight the use of interactive brushing and embedded plots to compare activity at different airports (top) and in different parts of Manhattan (bottom).