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

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.