This paper reviews the literature on the visualization of individual mobility data, with a focus on thematic integration. It emphasizes the importance of visualization in understanding mobility patterns within a population and how it helps mobility experts to address domain-specific questions. We analyze 38 papers published between 2010 and 2024 in GIS and VIS venues that describe visualizations of multidimensional data related to individual movements in urban environments, concentrating on individual mobility rather than traffic data. Our primary aim is to report advances in interactive visualization for individual mobility analysis, particularly regarding the representation of thematic information about people’s motivations for mobility. Our findings indicate that the thematic dimension is only partially represented in the literature, despite its critical significance in transportation. This gap often stems from the challenge of identifying data sources that inherently provide this information, necessitating visualization designers and developers to navigate multiple, heterogeneous data sources. We identify the strengths and limitations of existing visualizations and suggest potential research directions for the field. • Analysis of 38 visual analytics solutions designed to aid the exploration of individual mobility data across spatial, temporal, and thematic dimensions. • An analysis of how visualization tools incorporate the theme dimension and which thematic properties are addressed. • Analysis of how the subject is approached within GIS and VIS communities.

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.

The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable data exploration with ego- or exocentric views, have the user operate at different scales, or use different forms of navigation with varying levels of physical movement. This freedom results in a multitude of different viable approaches. Yet, there is no clear understanding of the advantages and disadvantages of each choice. Our goal is to investigate the affordances of several major design choices, to enable both application designers and users to make better decisions. In this article, we assess two main factors, exploration mode and frame of reference, consequently also varying visualization scale and physical movement demand. To isolate each factor, we implemented nine different conditions in a Space-Time Cube visualization use case and asked 36 participants to perform multiple tasks. We analyzed the results in terms of performance and qualitative measures and correlated them with participants' spatial abilities. While egocentric room-scale exploration significantly reduced mental workload, exocentric exploration improved performance in some tasks. Combining navigation and manipulation made tasks easier by reducing workload, temporal demand, and physical effort.

In this work, we evaluate two standard interaction techniques for Immersive Analytics environments: virtual hands, with actions such as grabbing and stretching, and virtual ray pointers, with actions assigned to controller buttons. We also consider a third option: seamlessly integrating both modes and allowing the user to alternate between them without explicit mode switches. Easy-to-use interaction with data visualizations in Virtual Reality enables analysts to intuitively query or filter the data, in addition to the benefit of multiple perspectives and stereoscopic 3D display. While many VR-based Immersive Analytics systems employ one of the studied interaction modes, the effect of this choice is unknown. Considering that each has different advantages, we compared the three conditions through a controlled user study in the spatio-temporal data domain. We did not find significant differences between hands and ray-casting in task performance, workload, or interactivity patterns. Yet, 60% of the participants preferred the mixed mode and benefited from it by choosing the best alternative for each low-level task. This mode significantly reduced completion times by 23% for the most demanding task, at the cost of a 5% decrease in overall success rates.

A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.

The use of novel displays and interaction resources to support immersive data visualization and improve analytical reasoning is a research trend in the information visualization community. In this work, we evaluate the use of an HMD-based environment for the exploration of multidimensional data, represented in 3D scatterplots as a result of dimensionality reduction (DR). We present a new modeling for this problem, accounting for the two factors whose interplay determine the impact on the overall task performance: the difference in errors introduced by performing dimensionality reduction to 2D or 3D, and the difference in human perception errors under different visualization conditions. This two-step framework offers a simple approach to estimate the benefits of using an immersive 3D setup for a particular dataset. Here, the DR errors for a series of roll call voting datasets when using two or three dimensions are evaluated through an empirical task-based approach. The perception error and overall task performance, on the other hand, are assessed through a comparative user study with 30 participants. Results indicated that perception errors were low and similar in all approaches, resulting in overall performance benefits in both desktop and HMD-based 3D techniques. The immersive condition, however, was found to require less effort to find information and less navigation, besides providing much larger subjective perception of accuracy and engagement.

Abstract 3D representations are potentially useful under many circumstances, but suffer from long known perception and interaction challenges. Current immersive technologies, which combine stereoscopic displays and natural interaction, are being progressively seen as an opportunity to tackle this issue, but new guidelines and studies are still needed, especially regarding information visualization. Many proposed approaches are impractical for actual usage, resulting in user discomfort or requiring too much time or space. In this work, we implement and evaluate an alternative data exploration metaphor where the user remains seated and viewpoint change is only realisable through physical movements. All manipulation is done directly by natural mid‐air gestures, with the data being rendered at arm's reach. The virtual reproduction of the analyst's desk aims to increase immersion and enable tangible interaction with controls and two dimensional associated information. A comparative user study was carried out against a desktop‐based equivalent, exploring a set of 9 perception and interaction tasks based on previous literature and a multidimensional projection use case. We demonstrate that our prototype setup, named VirtualDesk, presents excellent results regarding user comfort and immersion, and performs equally or better in all analytical tasks, while adding minimal or no time overhead and amplifying user subjective perceptions of efficiency and engagement. Results are also contrasted to a previous experiment employing artificial flying navigation, with significant observed improvements.