# INSPIRE: An INcremental, SPatio-temporal and Immersive approach for Representing and Exploring mobility data

Clément Quere, Aline Menin, Hui-Yin Wu, Paulo Moura, Marco Winckler

<figure><img src="image_1.png" /></figure>

## To cite this version:

Clément Quere, Aline Menin, Hui-Yin Wu, Paulo Moura, Marco Winckler. INSPIRE: An INcremental, SPatio-temporal and Immersive approach for Representing and Exploring mobility data. CHItaly 2025 - 16th Biannual Conference of the Italian SIGCHI Chapter, Oct 2025, Salerno, Italy. 10.1145/3750069.3750444. http://hal-05178219v2

HAL Id: hal-05178219

https://hal.science/hal-05178219v2

Submitted on 28 Jul 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
INSPIRE : An INcremental, SPatio-temporal and Immersive
approach for Representing and Exploring mobility data

Clément Quere

I3S Laboratory, University Côte
d'Azur, CNRS, Inria
Sophia-Antipolis, France
clement.quere@inria.fr

Aline Menin

I3S Laboratory, University Côte
d'Azur, CNRS, Inria
Sophia-Antipolis, France
aline.menin@inria.fr

Hui-Yin Wu

Inria, University Côte d'Azur
Sophia-Antipolis, France
hui-yin.wu@inria.fr

Paulo Moura

Institute of Risk, Environment, and
Sustainable Development (IMREDD),
University Côte d'Azur
Nice, France
paulo.moura@univ-cotedazur.fr

Marco Winckler

I3S Laboratory, University Côte
d'Azur, CNRS, Inria
Sophia-Antipolis, France
marco.winckler@inria.fr

<figure><img src="image_3.png" /><figcaption>Figure 1: (A) Visualization of spatio-temporal data in an immersive cube with a spatial filter centered on the map and a temporal filter on the vertical axis. (B) Doubling the spatial filter to compare two geographical areas and adjusting the temporal filter. (C) Selection of a point of interest revealing trajectories for detailed analysis.</figcaption></figure>

Abstract

The visualization of spatio-temporal data is essential for decision-
making, but is complex due to its multidimensional nature (space,
time). The spatio-temporal cube (STC), introduced in 1976, enables
this data to be represented in 3D, with space at the base and time at
the vertical. Although useful, the STC has limitations such as loss of
information and visual clutter. This paper therefore explores the use
of immersive virtual reality technologies to enhance the exploration
of this data, via an interactive egocentric approach, where the user
can refine their queries using custom spatio-temporal filters. Our
method was tested on 24-hour urban mobility dataset of individual
journeys within the city. We carried out a user study to assess the
satisfaction and usefulness of our approach. The results show that

our filters are useful and effective for analyzing urban mobility, and
easy to use.

CCS Concepts

*   **Human-centered computing** → **Virtual reality**; **User interface management systems**; *User studies*.

Keywords

Virtual reality, Space-time cube, Spatio-temporal data, Mobility data, Immersive visualization, Incremental approach

ACM Reference Format:

Clément Quere, Aline Menin, Hui-Yin Wu, Paulo Moura, and Marco Winckler. 2025. INSPIRE : An INcremental, SPatio-temporal and Immersive approach for Representing and Exploring mobility data. In *CHItaly 2025: 16th Biannual Conference of the Italian SIGCHI Chapter (CHItaly 2025), October 06–10, 2025, Salerno, Italy*. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3750069.3750444

<figure><img src="image_4.png" /></figure>

This work is licensed under a Creative Commons Attribution 4.0 International License.
CHItaly 2025, Salerno, Italy

© 2025 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-2102-1/25/10
https://doi.org/10.1145/3750069.3750444
# 1 Introduction

Visual analytics plays a crucial role in enhancing our ability to explore large and complex data sets in a variety of domains by combining scientific information and visualization, computational transformations, analytical tools, and visualization techniques. In particular, visual analysis plays an essential role in the processing of spatio-temporal (ST) data, i.e. data described in space and time. It supports decision-making processes in many fields, such as climate science, social science, transport, and geosciences. However, the multidimensional nature and inherent properties of ST data hinder the design of effective visualization solutions. Data are often defined on several levels of granularity in space (neighborhood, city, region), time (hours, days, weeks, months), object (individual, population) [3, 37]. Moreover, the representation of space and time is intrinsically constrained by the geographical arrangement of spatial positions and the chronological order of time units.

In this article, we focus on the visual exploration of a specific type of ST data, i.e. human mobility data. By tracking the movements of individuals and groups, we can uncover the personal motivations behind mobility, which allows us to better understand its impact on quality of life and spatial usage. In addition to the three fundamental dimensions of ST data (space, time, and object), human mobility includes a fourth dimension [34], the thematic dimension, which encompasses information describing the purpose of movement and modes of transportation, as well as individual (demographic) and environmental (e.g. weather conditions, transportation infrastructure) characteristics.

One widely used technique is the STC [20], which enables the perceptual integration of multiple data dimensions through the use of a 3D cube. Its base represents the two-dimensional geographic space, while the height corresponds to the temporal dimension. This approach allows for the representation of specific information, such as individual trajectories defined by (space, time) points and described by thematic attributes (e.g. activities, mode of transport), which can themselves be visualized using colors or symbols. Despite its ability to represent multidimensional data, the large number of displayed dimensions also increases the cognitive load required to interpret the information conveyed by the STC. Furthermore, like all 3D visualization techniques, it is limited in terms of perception and interaction when used on traditional computer screens [4].

The emergence of immersive technologies in recent decades, such as augmented reality (AR) and virtual reality (VR), has fostered a rapid increase in immersive analytics research, particularly in application areas where ST data mining plays a crucial role, such as mobility data [48], smart cities [8], maritime traffic [46], and biomedicine [12]. Inspired by these approaches, we investigate the potential of VR technologies to enhance mobility data mining within a STC. With this paper, we contribute to visual information retrieval by proposing personalized filters that enable dynamic and interactive interrogation of data in space and time. Users can refine their analysis by interacting with space and time (e.g. a specific area, or a given period), adopting an incremental approach that allows the user to display data of interest to their analysis. In particular, our contributions are as follows:

* A suite of interactive tools for information retrieval in an immersive STC, to facilitate ST data exploration.

* User studies demonstrating the use of our approach and proposed filters in data exploration, and its effectiveness in various data-specific queries.

The remainder of the paper is organized as follows. Section 2 introduces the concept of visualizing ST data. Section 3 summarizes previous work on different application domains using immersive systems and the different types of filters that exist within the STC. Section 4 presents our approach, and section 5 our user studies. Next, section 6 shows our results. Section 7 presents an analysis of the lessons learned from the results of the user evaluations, as well as perspectives for our future work. Lastly, section 8 presents our conclusions.

# 2 Visualization of ST data

ST data are inherently multidimensional. They enable the analysis of objects (what) located at a specific position (where) at a given time (when) [37]. Each of these dimensions can be explored at various levels of granularity. For example, space can be described at the scale of countries, cities, neighborhoods, or even geographic coordinates (latitude and longitude), while time can be segmented into years, days, hours, minutes, or seconds. The main challenge lies in making this data representable across all possible levels of granularity, or in identifying the most appropriate level depending on the application domain. Furthermore, when dealing with movement data, a fourth dimension, thematic, comes into play [34]. This dimension encompasses attributes or variables associated with the data, such as contextual information (e.g. weather, environment, infrastructure), measurements (e.g. temperature), or descriptive details about individuals (e.g. demographics) and their movements. It serves to qualify the spatial and temporal data in order to extract precise meaning. Such data can be represented as trajectories, composed of paths that integrate various dimensions, thereby enriching analysis and enhancing our understanding of observed dynamics. However, the simultaneous representation of multiple trajectories poses a significant challenge, as it can quickly lead to a complex and difficult interpretation of the visualization, often compared to a "spaghetti plot".

ST data visualization takes the form of animated maps, heat maps, and interactive graphs. These representations enable effective analysis of dynamic phenomena such as weather conditions, traffic flows, or demographic trends. More specifically, space is represented through various forms of thematic maps, such as connection maps, flow maps, and choropleth maps, while time can be expressed through temporal juxtaposition or animation [33, 36]. Among these techniques, flow maps stand out for their ability to represent movement data (e.g. transport, goods or population flows), visually showing trajectories between different locations. They provide a rapid overview of spatial dynamics, highlighting the directions, volumes and intensities of movements. However, although intuitive, flow maps have certain limitations: they can quickly become illegible when flows are numerous or intersecting, particularly on a large scale. What's more, they often require prior data processing to group or simplify routes in order to maintain acceptable legibility. To overcome the limitations of traditional flow maps, recent techniques apply aggregation, for example by merging edges that share similar trajectories during rendering [36], or
by grouping large origin-destination flows for dynamic simplification [15], while interactive approaches such as “flowstrates” enable real-time spatio-temporal merging and aggregation in linked visual views. In contrast, choropleth maps and other classic thematic maps are better suited to representing aggregated data, such as population, unemployment rates, or other indicators by geographical area. They use visual variables such as color, shape, or texture to encode information. However, the addition of several visual variables can quickly overload the map, making it difficult for the user to read. As a result, these representations often focus on one or two thematic aspects at a time, limiting the richness of explorable analyses and leaving part of the spatial or temporal phenomena under-represented [33].

Beyond its ability to represent complex ST data, STC is now part of a dynamic of innovation driven by immersive technologies [33]. The integration of three-dimensional visualization in VR or AR environments overcomes the constraints of 2D visual representation. These immersive environments offer users the ability to intuitively explore motion data in a fully three-dimensional space, enhancing the perception of spatial and temporal relationships. In addition, they encourage the addition of complementary sensory modalities, such as sound, light, or gestural interactions, which enrich the user experience and pave the way for new forms of exploratory analysis. However, the main challenge lies in the design of these immersive environments themselves. It is not just a question of ensuring fluid navigation and natural interaction, but also of adopting relevant and comprehensible visual representations. Poor modeling or an inappropriate choice of visual codes can lead to cognitive overload and even misinterpretation of data. The successful integration of STC into an immersive space is therefore closely dependent on the quality of the technical implementation and the appropriateness of the visualization choices made.

Beyond representing data, it is essential to determine how users will interact with it. As the volume and diversity of data continues to grow, it challenges both computational capabilities and human cognitive limits. Visualization environments have traditionally employed three main exploration strategies: (i) Shneiderman's well-known mantra, "Overview first, zoom and filter, then details-on-demand" [44], (ii) opportunistic exploration, which provides a flexible environment that dynamically adapts to user interactions [30], and (iii) semantic zoom, which enables seamless navigation through multiple levels of data abstraction [24]. The first approach is particularly effective for processing large datasets while reducing cognitive overload. In contrast, the opportunistic approach does not follow a predefined exploration path, but encourages users to discover unexpected information through intuitive interactions and adaptable visualizations. This method is particularly useful in scenarios where exploration objectives are not explicitly defined from the outset. Finally, semantic zooming differs from traditional visual zooming: instead of simply adjusting the scale, it dynamically modifies representations to highlight relevant contextual or synthetic information, making it particularly useful for exploring complex data without losing important contextual elements. In parallel with these strategies, adaptive interaction paradigms are used for immersive analysis. The aim is to modify the visual representations or real-time interaction techniques of immersive systems, depending on user behavior, context or preferences. For example, adaptive

systems can reposition visual elements to avoid occlusions. Systems learn from user interactions, enabling more personalized and efficient exploration, particularly with data rich environments [9].

In this paper, we use the potential of immersive technologies and the flexibility of the opportunistic exploration approach to improve data exploration in an STC. Previous studies have shown that immersion can facilitate data exploration in an STC by lowering the learning curve and simplifying complex operations [48]. To handle large volumes of data, we are adopting an incremental exploration strategy with ST filters that retains the adaptability of the opportunistic approach, allowing users to dynamically adjust their environment while testing it with specific and explicitly defined goals.

# 3 State of the art

In this section, we will review the literature in this area, focusing on: (i) immersive visualization in various application domains, and (ii) exploration strategies with ST filters for immersive visualization.

## 3.1 Application domains of immersive visualization

Mixed reality (MR) and AR offer new possibilities for contextual and in-situ analysis of ST data by superimposing virtual information on the real world. For example, Luo et al. [29] have developed *PEARL*, a system for analyzing human body movements and interactions with physical objects in a room. Similarly, Reipschlager et al. [38] have visualized human positions and interactions using 3D avatars, heat maps and field-of-view representations. While these approaches are effective for indoor (in-situ) analyses, they encounter limitations when applied to use cases requiring country-level granularity. This is due to space constraints and the absence of adequate geographic representation in a closed environment, which complicates data visualization at this scale [1].

A more advanced MR system, *MIRIA* [7], enables in-situ analysis of spatial interactions with multiple visualization techniques, including heat maps and geographical maps, making it effective for contextual real-world analysis. In *ecology*, these technologies superimpose the migration routes of endangered species onto real landscapes, contributing to conservation efforts [53]. In *health and sport*, MR/AR tools aid patient rehabilitation by visualizing bone structures for healthcare patients [13]. In urban planning, ST visualizations help decision-makers assess traffic flows and weather conditions in specific locations. In *archeology*, MR/AR superimposes historical reconstructions on real sites, facilitating in-situ exploration [14].

Despite their advantages, MR/AR systems require high-performance devices to efficiently render complex 3D models while respecting display constraints. What's more, aligning virtual data with real environments remains a major challenge, especially in dynamic environments. VR environments offer deeper immersion than MR/AR systems, giving users greater freedom of movement and a wider field of vision than traditional computer interfaces. This heightened engagement in VR, thanks in particular to total immersion, makes it a particularly powerful tool for ST data analysis, offering users greater freedom of movement and a wider field of vision than in traditional computer interfaces.
For example, Rzeszewski et al. [40] have developed a VR system that reconstructs historic urban environments, enabling users to explore inaccessible places while acquiring educational and cultural knowledge. In the field of *emergency planning*, Ren et al. [39] have designed a VR platform that simulates disasters, such as building fires, to improve evacuation strategies and safety protocols. In *biomedicine*, VR environments have been used to observe cell divisions during embryonic development, offering a unique perspective for research and education [16]. Similarly, Kloiber et al. [25] have developed a VR tool for the analysis of human behavior, integrating physiological and motion data, making it useful in experimental psychology and behavioral science.

In the field of *urban planning*, several VR visualization environments have been developed to facilitate trajectory analysis. For example, Zhang et al. [52] proposed a wall-like visualization, segmented by colors representing different vehicle speeds over time, to better understand traffic dynamics. Weidinger et al. [51] represented trajectories on automobile circuits to visualize driving amplitude, identify key events and optimize driver performance. Homps et al. [22] focused on large-scale trajectory exploration and filtering using 3D shapes, enabling immersive interaction with the data. Finally, in a similar approach, Wagner et al. [47] explored the use of an STC to analyze urban data, notably through the visualization of cab trajectories. These applications help urban planners to design cities that are adapted to people's needs. In addition, VR plays a crucial role in public participation by making urban projects more accessible and understandable, thus fostering collaboration between experts and local communities [19].

By offering an immersive experience and a natural environment, VR enables users to better perceive the complex relationships between space and time, these advances in immersive visualization suggest the great potential of VR to explore and understand ST data.

## 3.2 Interactive spatio-temporal filters

Previous work relied on predefined territorial partitions to support spatial selection and filtering [10, 31, 43, 47]. This involves displaying a map segmented according to administrative or custom divisions, allowing a specific region to be selected for display. Another approach is to select space using user-defined shapes within an immersive STC, such as drawing techniques or slicing tools [47]. In addition, the space can be segmented into customized tiles (e.g. voronoi, hexagons, etc.) that can be selected by the user [16, 42].

The temporal dimension is usually filtered by sliders, where the user can select a particular time unit [7, 11, 16, 23, 26, 31, 35, 38, 41, 45, 47]. Range sliders are also common and widely used techniques in VR, allowing the user to define a time period by selecting units of time [7, 25, 47]. To work with dates and time slots, the calendar is commonly used [11, 18, 43, 50]. For more conventional needs, there's the text field, which lets you directly enter the desired value [26], as well as the drop-down menu, which lets you select predefined periods [10, 17, 28, 42]. Finally, [29] proposed an innovative approach entitled "Lenses": a virtual cube acting as a three-dimensional filtering system, integrating the Z time axis in the form of a visual timeline, enabling users to explore data segments individually.

Interaction with the thematic dimension is usually via check-boxes, as we observe in [26, 35], where users can filter the data by selecting different ethnic groups. Other simple filtering techniques, such as drop-down menus and search fields, are also used [33].

In summary, the filters demonstrate strong adaptability across data types and application contexts. Their integration into immersive environments is particularly effective for spatial, temporal, and thematic filtering. Our use of spatial slicing with predefined shapes offers an accessible alternative that reduces the learning curve compared to drawing techniques. Similarly, the temporal filter employs a range slider, validated by prior immersive studies, enabling intuitive and efficient time period selection.

# 4 INSPIRE: Our Approach

In this section, we present our immersive approach to STC. We detail the data used as a case study, our visual mapping strategy enabling graphical representation, and the various interaction techniques implemented to facilitate data exploration and analysis.

## 4.1 Data

Human movement data typically consist of position records indicating an entity's geographic location at specific times [2]. These records may be continuous, such as GPS coordinates with frequent timestamps or discrete, like origin-destination points with irregular timestamps from surveys. The data often include semantic attributes, including travel motives, transport modes, and socio-demographic information. Structurally, movement data comprise three key elements: spatial locations, time units, and ST objects, which encompass moving entities and spatial events. A spatial event denotes a location at a particular time reflecting a temporal change, such as the trajectory of a tornado over French territory.

STC is used to analyze individual trajectories, whether of people or other moving objects. A trajectory corresponds to a sequence of events in space and time, produced by the movement of an individual or object, alternating between stops and displacements. A stop is an interval of time spent in a location performing an activity, while a move represents an ST line between two locations.

Although the composition described above is independent of the data source, the initial structure may vary depending on the origin of the data. To take this into account, we have proposed a hierarchical structure input data in a flexible and consistent way:

* **Id**: Unique identifier of the individual or object.

* **Values**: List of objects describing spatial events, described as follows:

- **Time**: Precise time of spatial event.

- **Space**: Spatial position, expressed either in geographic coordinates (longitude, latitude, altitude) or in cartesian coordinates (x, y, z), the region name.

- **Others**: Additional variables related to travel, such as activity, mode of transport, emotion, etc.

* **Etc.**: Other individuals or objects.

This data processing procedure transforms the input data to align with the visualization format.
<figure><img src="image_5.png" /><figcaption>Figure 2: Overview of the immersive STC. A) Interactive map. B) Vertical walls representing time. C) Data points. D) Our spatial filter, the GeoShadow. E) The temporal filter, called TimeSlider3D. F) The user's hands and controllers.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 3: Selecting objects and interfaces using joysticks. A) Close selection with a white indicator in front of each joystick. B) Remote, using raycast.</figcaption></figure>

## 4.2 Visual mapping

The visual encoding of data within our STC follows a common approach observed in previous work [10, 27, 32]. The spatial dimension is represented using a geographic map placed on the floor of the cube (Figure 2 A), while time is represented along the walls surrounding the data (Figure 2 B). Trajectories are represented by lines linking one spatio-temporal event to the next (Figure 1 C). In our case study, the thematic dimension defines trips and stops by considering both the reasons for travel and the modes of transport employed. Stops are represented by spheres, color-coded to indicate the trip purpose, i.e. the activity performed at that location. Journeys are symbolized by line segments, color-coded to reflect the mode of transport used. To facilitate an incremental exploration approach, the STC initially displays only stops in the form of colored spheres, representing the activities performed by persons over time.

<figure><img src="image_7.png" /><figcaption>Figure 4: How our filters work. A) The GeoShadow is inactive by default. B) It turns blue when the user moves it or changes its diameter. C) The TimeSlider3D is also inactive by default. D) It turns blue when its time period is being modified.</figcaption></figure>

## 4.3 Interaction

Our immersive STC is designed using an ego-centric approach, where the user is placed at the center of the STC, surrounded by the data. The user can physically move around the cube environment, adjusting their distance from the data and selecting the viewing angle that best suits their perception. Alternatively, the user can move and resize the map. Remote and close-up interactions are made possible by ray projection (Figure 3 B) and direct touch, via a sphere on the front of each joystick, enabling intuitive selection similar to that made with a finger (Figure 3 A).

We are following an overview, filter, details-on-demand approach, initially presenting all data records and allowing users to control the amount of information displayed. In the following, we present three key features of our approach: (i) spatial filters, (ii) temporal filters and (iii) data selectors.

**Spatial filters.** We support spatial filtering with two distinct techniques: (i) the **GeoShadow** and (ii) a **search field**. The former takes the form of a circular shadow superimposed on the geographic map (Figure 2 D). It filters the data set, displaying only data points located in selected spatial locations within the circle. GeoShadow is interactive, allowing users to resize and move it. When the user

manipulates it, it turns blue, as shown in Figures 4 A & B In addition, users can duplicate it to enable data comparison between two spatial selections (Figure 1 B). To facilitate user perception and task resolution, it includes a description of the selected perimeter in square kilometers, as well as a compass rose symbol for orientation. Secondly, to facilitate tasks requiring specific spatial locations, we have implemented a search function that allows users to find a geographic location without needing to know its exact position. After performing a search and selecting the place name, an animated marker appears at the chosen location to guide the user.

**Time filter.** Time filtering is supported by a **TimeSlider3D**, which takes the form of an interactive range slider allowing users to select a start and end time for analysis. It also turns blue when it is being modified, as shown in Figures 4 C & D It has two end handles to adjust the displayed time period and can be repositioned for user comfort.

**Data selectors.** Data selection is supported by two functions: (i) the **SelectionHoop** and (ii) the **contextual menus**. The first element is an interactive hoop designed for multiple selection of data points in space and time. Inspired by a butterfly net, this tool can be freely manipulated: the user can enlarge or shrink it to adjust the selection range. By default, the hoop is inactive and displayed in white (Figure 5 A). To activate the hoop, the user must grab it with one of the two joysticks, then click on the primary button of the joystick that grabbed it. The hoop's color then turns blue, indicating that selection is now possible. The user can then move it around the environment, collecting data points, which activate the contextual menus associated with these points (Figure 5 B).
<figure><img src="image_8.png" /><figcaption>Figure 5: Overview of how our SelectionHoop works. A) The user raises the hoop by pressing the primary button on the right-hand stick. B) Clicking and grasping the primary button enables the user to select multiple points.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 6: Contextual menu associated with a data point. A) Relationship. B) Path. C) Highlight trajectory. D) Locate point in space and time. E) Close.</figcaption></figure>

To interact with them, each data point is equipped with a contextual menu and a tooltip displaying the associated time and activity (Figure 6). When selected, the data point's sphere changes to a pin symbol, making it easier to identify and explore among other points. If this point has an option selected, then it will remain in the form of a pin. The menu includes buttons, each offering a different action:

* The **relation** button (Figure 6 A) highlights data points with thematic similarities. For example, if the selected point is associated with the activity “Home”, all other points with the same activity will be displayed clearly, while unrelated points will appear transparent, reducing the attention paid to irrelevant elements.

* The **trajectory** button (Figure 6 B) displays the trajectory of the selected point. Once activated, the system adds a **focus** button (Figure 6 C) that can be used to hide other trajectories, providing a focused view for analysis.

* The **localization** button (Figure 6 D) displays the data point's position in both space and time, drawing lines from the point to its geographical position on the map and its time unit on the STC walls.

* The **close** button (Figure 6 E) closes the contextual menu. When closed, the pin reverts to a sphere unless an option remains selected.

Finally, an additional menu gives users access to broader options, including: displaying the data legend, which explains the colors used in the scene (Figure 7 A), create a new spatial filter to enable multiple spatial selections (Figure 7 B), change the datasets

<figure><img src="image_10.png" /><figcaption>Figure 7: Overview of the main menu. A) Legend. B) Create a new spatial filter. C) Select multiple data points to apply the same options, e.g. to view multiple trajectories in parallel. D) Change dataset. E) Reset view. F) Parameters</figcaption></figure>

to be explored (Figure 7 D) and reset the view to its original state (Figure 7 E).

## 4.4 Implementation details

We developed INSPIRE using Unity, a well-known real-time 3D rendering engine commonly used to create immersive environments. Our tool was implemented under Unity version 2022.1.22f1 and is compatible with various VR headsets (Oculus, Vive, Pico) via an OpenXR implementation. To interact with the virtual environment, Meta Quest 3 controllers were used, and each specific button was configured to adapt to all the other controllers on the headset. To display the map of our STC, we used a dynamically zoomable map from the Maps SDK of Microsoft's Garage project, whose service ended on March 1, 2025.

## 5 User studies

We conducted user experiments to assess the usability and effectiveness of our approach to facilitating ST data exploration. We sought to answer the following research questions:

R1 How do the proposed ST filters support task resolution in a ST data exploration context?

R2 What is the usability of the proposed ST filters for ST data exploration?

### 5.1 Measures and Methods

To address our research questions, we evaluated user performance, usability, and interaction patterns. Performance metrics included response accuracy and task completion time, both manually recorded by the experimenter during each question. The experimenter confirmed participant readiness before starting the timer. Participants verbalized their answers to confirm decisions and stop the timer. For verification purposes, the entire experiment was audio and video-recorded.

System usability was assessed using two recognized questionnaires: the System Usability Scale (SUS) [6], and the AttrakDiff [21], the latter measuring both *pragmatic* (ergonomics, usefulness) and *heuristic* (pleasure, innovation) perceptions of an interactive system. In addition, user satisfaction was explored by asking them to mention three aspects of the experience they liked, as well as three elements they disliked.
Perceived task difficulty was also taken into account as a complementary indicator of usability, using a 5-level Likert scale. To gain a better understanding of how the system helps users to solve domain-specific tasks, we analyzed their interactions using the history of actions performed and video recordings of their field of vision. This analysis enabled us to identify the system elements actually mobilized during the task-solving process. All data collected either remained anonymous or were anonymized. The experimental protocol was validated by the ethics and research committee of the Université Côte d'Azur under process number 2024-071.

## 5.2 Experimental Configuration

The experiments were conducted in a 5 m² room, cleared of obstacles to maximize user mobility. Safety was ensured by the Meta Quest 3 headset's “Guardian” system, which alerted participants when nearing the virtual environment's boundaries. The headset was connected to the computer via a 5 meter cable, allowing unrestricted movement without entanglement risk. Two tables were arranged: one for the participant, equipped with a computer for pre and post-experiment questionnaires and refreshments, and one for the experimenter, holding a computer for running the Unity-based experiment, the experimental protocol, and a reference sheet with questions and instructions.

## 5.3 Protocol

A sociodemographic questionnaire was administered at the start of the study to better understand the participants' profiles and the factors influencing experimental results. This questionnaire collected demographic information (e.g. age, gender, occupation, vision problems) as well as details of previous experience (e.g. with VR, video games and visualization). Next, the experimenter helped the participant settle into the room, put on the VR headset and controllers. During the training phase, the participant followed a tutorial integrated into the VR environment, allowing participants to explore and learn how to interact with the system. The training session lasted around 15 minutes, followed by a 5-minute break to reduce fatigue. The experience phase began with four key interactions designed to refresh participants' navigation skills in the environment. These involved manipulating the GeoShadow, the TimeSlider3D, the location search field and the hoop. Participants were then asked to answer seven questions (see Section 5.4) based on the displayed data, asked aloud by the examiner. After each question, participants rated its difficulty on a scale of 1 (very easy) to 5 (very difficult). The entire session lasted approximately 35 minutes. Upon completion, participants filled out post-test questionnaires, including the SUS, AttrakDiff, and satisfaction surveys as described above.

## 5.4 Data and tasks

For this study, we used a individual trajectories dataset derived from data from a Household Travel Survey (EMD) carried out in Grenoble, France, in 2010 [32]. Trajectories consist of stays and trips defined by origin-destination locations, representing an individual's movements from 5a.m. to 5a.m. the next day.

We identified three task types: selection, navigation, and interpretation. Selection tasks involve choosing specific data points,

paths, or trajectories. Navigation tasks encompass actions like manipulating maps, applying ST filters, or physically moving within the visualization environment. Interpretation tasks combine selection and navigation to facilitate user conclusions. These tasks are further classified into four categories:

* **Spatio-temporal:** How many people are in <space> at <time>?

* **Spatial:** Identify the number of people currently present in <space>.

* **Temporal:** In the current area, who is at <time>?

* **Exploratory:** In which areas is there the greatest concentration of population?

For each analysis task, we can identify the types of interaction that the user will have to perform, in order to check whether our filters facilitate the completion of these specific tasks. We therefore defined two sets of tasks, described as follows:

* Calibration tasks :

(1) Can you find data near *Fountain*?
This task checks whether participants have understood how to move the GeoShadow.

(2) Can you visualize data within a perimeter of 2 km²?
This task assesses whether participants understand how to resize the GeoShadow to match the requested spatial area.

(3) Can you adjust the visualization to display data points from 11am to 5pm?
This task tests whether participants have understood how to select a time period with TimeSlider3D.

(4) Can you locate Roche Street?
This task shows whether participants have understood how to navigate on the map and/or how to use the search field for spatial locations.

* Experiment tasks :

(1) How many people are present in the park *Berges de l'Isère* from 2pm to 4pm, within a perimeter of 1.5 km²?
This task uses the search field to locate the indicated location, using TimeSlider3D and GeoShadow to define the relevant time period and spatial perimeter. And finally, count the right people in the targeted area.

(2) What is the activity carried out by this person [the one identified in the previous question]?
This task asks the user to inspect the data point, either by interpreting the color of the sphere, or by selecting it to view the associated tooltip.

(3) Can you describe the background of this person [the one identified in the previous question]?
This task requires the use of the context menu to display the associated trajectory, and its interpretation by inspecting each part of the trajectory.

(4) Within a perimeter of 2 km², how many people visited the *Lycée Champollion* for personal business between 9am and 12pm?
This task requires you to manipulate GeoShadow and TimeSlider3D, while identifying the activities represented in color in each data point.

(5) Within a perimeter of 2 km², how many people in the *Seyssinet-Pariset* area can be considered environmentally
friendly?

This task involves using GeoShadow to define the analysis area, as well as displaying trajectories, in order to identify individuals traveling by environmentally-friendly means of transport such as bicycles or public transport.

(6) What is the most active area throughout the day, excluding the "Home" activity, between the *Edouard Vaillant* college and the *Olympique* college?

This task requires you to manipulate the GeoShadow to create two comparison perimeters, one in each given location, or to move the GeoShadow between the two zones. The TimeSlider3D must also be manipulated to define the whole day as the analysis period.

(7) Which of the above two areas has the highest population concentration [spatial locations and time period from previous question]?

This task involves differentiating between the two zones by selecting all the points in each zone using our SelectionHoop tool, then displaying all the trajectories to count the number of unique people present in each zone.

## 5.5 Participants

Twenty-three volunteers took part in our study. They were mainly PhD students and IT engineers (9 women), recruited by e-mail from our university. Participants were paid €20 to take part in the experiment. They were mainly aged between 20 and 34 (median age = 25). Half of the participants reported that their vision was corrected by wearing glasses when using the head-mounted display (HMD). Twenty-two participants reported being right-handed. Most participants said they rarely or never experienced motion sickness, while 5 participants said they sometimes did. Half of the participants had previous experience of VR applications, such as gaming or development, and 9 participants had experience of 3D video games.

## 6 Results

In this section, we present the results divided into four categories: satisfaction and usability, patterns of interaction, response accuracy and problems encountered.

### 6.1 Satisfaction and Usability

The average SUS score for our tool is 72/100, implying that users experienced the system as user-friendly, in line with [5]. To further evaluate user experience, we employed the AttrakDiff questionnaire. Scores for *pragmatic quality* (PQ: 0.86, min: 0.1, max: 1.6) and *hedonic quality-identification* (HQ-I: 0.80, min: -0.5, max: 2.1) fall within the standard range (0–1), suggesting the system is functional and comprehensible, though with room for improvement in visual comfort and ergonomics. A high *hedonic quality-stimulation* score (HQ-S: 1.57, min: 0.2, max: 2.2) indicates strong user engagement and perceived innovation, probably due to the immersive nature of the system. The overall *attractiveness* score (ATT: 1.61, min: 1.3, max: 2.1) reflects a very positive user experience. Together, the high HQ-S and ATT scores suggest a pleasant, clear interface, while the above-average PQ supports the effective use and understanding of ST filters (R2), reinforcing their value in multidimensional data mining.

### 6.2 Patterns of interaction

In order to evaluate the use of our filters, we first defined a set of experimental tasks designed to implement them. Based on the available actions in the STC, we defined a sequence of necessary actions to accomplish each task.

To complete these tasks, users perform a series of interactions within the STC (e.g. resizing, moving, selecting, displaying) involving filters or data linked to spatial, temporal, or thematic dimensions. Based on these interactions, we categorized actions into three types: *mandatory* actions necessary for task completion; *errors*, mainly representing unnecessary repetitions; and *alternative* actions, which can substitute mandatory ones. For example, “Move GeoShadow” can be replaced by “Move Map,” and “Resize GeoShadow” by “Resize Map,” both enabling selection of a spatial area of interest.

We will compare the tasks based on the dimensions they engage, identifying common patterns between optimal action sequences (i.e. those without unnecessary repetition) and the sequences actually performed by users. The tasks were defined in terms of difficulty, from the easiest 1 to the most difficult 7.

The tasks 1 and 4 share a ST nature, involving the identification of one or more people based on both spatial and temporal information. To achieve this effectively, an optimal interaction sequence has been identified: first, the use of GeoShadow (moving and resizing to circumscribe the area of interest), then period adjustment using TimeSlider3D, and finally interaction with the displayed data points, including, if necessary, activation of trajectory visualization. This sequence thus follows a logic: Space → Time → Theme, which sequence was adapted by the majority of users (no errors considered) (Figure 8 Top group). Among the ten who followed this sequence, one person correctly answered Task 1, while the other nine did not. Conversely, among those who did not follow this sequence, only one person correctly answered task 4, suggesting that this pattern is not strictly necessary to arrive at a correct answer. For the latter question, 21 people followed the optimal sequence, but only three of them gave a correct answer. To find the number of people within the Lycée, it's worth noting that one person answered correctly without selecting any data points, demonstrating that alternative strategies can be effective, while the high incidence of interaction errors in this group may be attributed to the presence and handling of the TimeSlider3D, which led to some points being avoided.

The tasks 2, 3 and 7 have in common a focus on thematic elements. The first requires the identification of a specific activity, the second extends the analysis to all activities and transportation mode used, and the third requires counting people present within two perimeters. Although their objectives differ slightly, all three tasks rely primarily on an exploration of the thematic attributes associated with the data. The optimal interaction sequence varies according to the precise formulation of the question, but in all three cases it relies essentially on direct interaction with the data points. This results in a relatively simple interaction sequence, centered on the theme. Observed behavior confirms this logic: users have generally selected one or more data points, consistent with a “Theme”-oriented approach (Figure 8 Middle group). In the case of task 2, all participants provided the expected answer. Of these, only
three did not select any data points but preferred to rely on the leg-
end display, showing that an alternative approach can also lead to
the right result. In task 4, all participants followed the expected se-
quence of actions. However, only five provided the expected answer.
Finally, in task 7, 18 participants followed the optimal sequence. Of
these, nine provided the correct answer, while the remaining nine
did not. Of all the participants, seven used our group selection tool
(i.e. the “hoop”, used to select multiple data points at the same time),
while five took no action at all, relying solely on a visual estimate of
the number of spheres in the zones. This finding underlines the fact
that, in this context, adopting a strategy considered optimal does
not necessarily ensure correct interpretation of the instruction.

Tasks 5 and 6 are primarily spatial in nature, but also include
thematic elements. The distinction between the two lies in the fact
that the former makes no explicit reference to a thematic analysis,
while the latter makes it a central criterion, asking for a comparison
between two locations. Despite this difference, a similar interaction
structure emerges: users must first adjust the geographic area using
GeoShadow (by moving and resizing), then complete the analysis
by selecting the data displayed or displaying the legend to show
the corresponding activity/transportation mode. This combination
of actions constitutes a “Space → Theme” type sequence (Figure 8
Bottom group). The interactions observed confirm this logic: the
majority of users began by manipulating the GeoShadow, before
interacting with the data points, often also activating trajectory
visualization. For task 5, all participants followed the optimal se-
quence, resulting in only two errors. In contrast, Task 6 resulted in
a higher number of errors, with five incorrect responses, despite all
participants following the optimal sequence. These errors were due
to incorrect positioning or resizing of the shadow, which resulted in
several points being displayed outside the area originally intended
for the response. This suggests that, as with other tasks, strictly
following the ideal sequence does not necessarily guarantee cor-
rect understanding or interpretation of the instructions, but rather
requires precision in filter placement.

As we can see, despite the increasing difficulty depending on
the questions, there is an improvement in the learning of spatio-
temporal filters. We expected the last question was perceived as
difficult, due to a lack of clarity in its formulation and the omission
of our multiple selection tool. The sequences of actions observed,
often in line with the defined optimal paths, confirm the relevance
of the latter. In the majority of cases, users adopted logical and
efficient approaches, illustrating a good command of the tools, no-
tably GeoShadow and TimeSlider3D. The recurrence of the “Space
→ Time → Theme” or “Space → Theme” schema underlines the
interface’s consistency with the analytical needs associated with ST
tasks. However, some more specific features, such as the multiple
selection hoop, remain under-utilized, suggesting a need to remind
users of its use. In addition, the location search was used by users at
the start of each sequence when they needed to identify a location
on the map, demonstrating its usefulness when the geographical
space is unfamiliar to users.

6.3 Data interpretation & Fact-checking

The interpretation of data and understanding of the context thus
emerge as essential to successfully completing the task. Although

important, mastery of the ST filters alone is not sufficient: the user
must also demonstrate reasoning and discernment to arrive at the
correct answer. With this in mind, we will analyze the reasons why
some users were able to answer the questions correctly, while oth-
ers encountered difficulties. Two complementary dimensions were
therefore taken into account: on the one hand, fact-checking, which
aims to determine whether the given answer actually corresponds
to the elements visible within the participant’s field of view; on
the other hand, accuracy, which assesses whether the conveyed
information aligns with the expected response.

In Figure 9, we observe that the results across the different ques-
tions are mixed: Questions 1, 3, and 4 show success rates well below
25%, while a progression becomes evident over the course of the
experiment, peaking at 78% for Question 6, followed by a promising
56% for Question 7. This trend may reflect a learning effect or a grad-
ual adaptation by the participants. Although overall accuracy rates
remain low, it is possible to compare the provided answers with
the data visually represented within the participants’ field of view.
Most questions show a fact-checking rate above 80%, indicating a
certain consistency between the answers given and the perceived
elements. Only the final and most complex question requires users
to compare two zones, select all contained points to display the
trajectories, and count them. This is the question with the lowest
fact-checking rate, with a score of just 65%.

These results should be viewed in light of the interaction difficul-
ties users faced during the experiments. We believe the performance
gaps stem from the highly specific nature of the questions, rather
than from interaction or usability issues.

6.4 Challenges faced by users

During the experiments, we collected the audio from the experiment
of each user and transcribed it to understand their reasoning and
also the various issues they encountered that which we have listed
below:

*   **Manipulation of the interactive map:** *P2 no longer knew how to zoom out to recenter the map on a trajectory, while P16 found it confusing to move the map using the joystick instead of the GeoShadow.*

*   **ST filters:** Selection errors associated with our temporal filter were reported. *P6, P8, and P10 encountered difficulties when adjusting the time range via remote interaction, due to the ray not properly anchoring to the 3D object.*

*   **Menu:** Some users experienced readability issues caused by menus obstructing their view, especially when several contextual menus were opened at the same time, leading to visual occlusion. *P8 and P18 noted that their field of vision became cluttered by interface elements when selecting multiple points while keeping contextual menus open.*

*   **Tutorial:** The tutorial was perceived as dense and overly technical, particularly due to its use of specialized vocabulary. *P14 and P17 reported that they did not remember all the buttons, stating that the tutorial was too lengthy and included too much information to absorb effectively.*

*   **Miscellaneous:** Some functions were forgotten or accidentally activated by pressing the wrong buttons, notably the hoop tool. A few slowdowns within the application were
<figure><img src="image_11.png" /><figcaption>Figure 8: Chronogram illustrating the task groups, showing in succession the interaction patterns of successful users, followed by those who answered the question incorrectly.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Figure 9: Graph comparing the average accuracy percentage of users' responses with that obtained through fact-checking for each question.</figcaption></figure>

also observed during testing. P11 and P13 could no longer remember how to activate the hoop to select multiple points. Meanwhile, P3, P9, P10, P15, P18, and P22 triggered the hoop unintentionally while trying to interact with the temporal filter.

# 7 Discussion

This paper introduced a suite of interactive tools designed to support information retrieval and data exploration in an immersive STC. Our main contributions include the development of a set of ST filters, and a set of user studies evaluating their effectiveness and usability in realistic data exploration tasks. The research was guided by two key questions: (R1) How do the proposed ST filters support task resolution in an ST data exploration context? and (R2) What is the usability of the proposed ST filters for ST data exploration? On
the basis of our study, we will now discuss the main lessons we’ve learned from our results and user feedback.

Our system is based on an opportunistic exploration approach, which encourages a more exploratory posture and stimulates the user’s curiosity. We have adapted this approach into an incremental process, allowing users to progressively refine their analysis while being guided step by step. This strategy proved effective in mitigating visual occlusion, by offering a spatially and temporally filtered STC from the start of exploration. Unlike the work of [22], we first display spheres rather than full trajectories. This allows the user to start exploring without being overwhelmed by the visual overload of immediately displaying all the data. However, our approach is not entirely free of occlusion problems, particularly when selecting multiple points simultaneously or when viewing trajectories to compare different areas, as was highlighted in the responses to question 7. The overload partly results from contextual menus poorly suited for multiple selections. To address this, we propose adaptive contextual menus: first, distinct shapes (e.g. squares, triangles, or custom forms) would visually identify grouped points; second, rather than displaying a menu around each point, a single white shape representing the group would appear before the user, accompanied by one unified contextual menu. This approach would improve visualization clarity and enable smoother interaction when multiple items are selected simultaneously.

INSPIRE is an immersive, interactive system integrating ST fil-
ters with real world data, which is to analyse human movements
within a urban environment to support decision-making regarding
infrastructure, in transport or creation of leisure areas. Its usabil-
ity was assessed through user experiments. Consistent with prior
research, our ego-centric exploration approach reduces the inter-
actions needed to retrieve specific information. The high success
rate in our tests, despite some task-specific challenges (e.g. identify-
ing exact place names or precise counts), indicates users generally
interpreted the presented data accurately.

Participants generally appreciated the immersive interface, which
they found intuitive and ergonomic, as well as the clarity of the
visualizations. Interaction with the 3D environment using joysticks,
in particular the ability to resize the map, was well received. In-
teractive elements such as the legend and contextual menu were
also perceived as relevant and easily accessible. A few difficulties
were noted, notably with remote manipulation of the temporal
filter, whose interaction with the ray was felt to be very sensitive.
Other participants mentioned that the multiplicity of commands
sometimes made the interface confusing, increasing cognitive load
as well as the articulatory distance required to complete certain
tasks. Overall, the tool was deemed easy to use, as confirmed by a
satisfactory SUS score of 72 (R2).

Our study is limited by the specificity of the questions posed
during user testing. Future research will involve more exploratory
analyses of STC use to address domain-relevant questions, helping
to clarify whether observed discrepancies arise from the questions
themselves or from interaction issues. To improve user satisfaction
and confidence in decisions, other interaction methods could be
developed. In particular, some of the difficulties of interacting with
the ray are alleviated by updates to the latest integration toolbox,
which improve the manipulation of 3D objects. Future work will
also focus on new interaction techniques, such as voice commands,

to offer smoother, more natural data exploration, which could lead
to a smoother, more intuitive immersive experience.

Our current implementation of INSPIRE supports the exploration of trajectories derived from household travel surveys, but the approach is designed to be generic and applicable to any type of trajectory data, including those of individuals or vehicles. The proposed hierarchical data structure allows the flexible integration of various data sources to meet visualization needs. Although STC effectively represents several dimensions of spatio-temporal data, the use of the third dimension for time limits the inclusion of additional spatial variables, such as altitude. This limitation is particularly relevant in mobility analyses involving people with reduced mobility, where changes in elevation, such as stairs, slopes or ramps, can have a significant impact on accessibility and route planning. Future work will explore new visualization and interaction techniques to integrate a third spatial dimension without compromising the clarity of temporal information. Another perspective would be to adapt our approach to incorporate upstream data processing automation mechanisms, or to make user interfaces dynamic and adaptive with the help of AI, as outlined by [49]. For example, the positioning of interface elements could be automatically adjusted to avoid data occlusion or to keep the most relevant information in the field of vision, making interactions adaptive.

8 Conclusion

This paper explores a new method for analyzing and interacting with large datasets by presenting INSPIRE, an egocentric approach that allows users to interactively refine their queries using an incremental process based on customized ST filters within an STC. Through a user-centered study based on two questionnaires (SUS and AttrakDiff), we evaluated ST filters designed to facilitate the resolution of urban mobility-related tasks. The results show that these filters are globally perceived as usable, although difficulties were noted concerning selection with the temporal filter. We also explore an incremental approach aimed at reducing the visual clutter caused by the massive display of data, particularly during multiple trajectory selection. Finally, we examine the extent to which our STC can enrich the user experience by simplifying the exploration and interpretation of multidimensional data. The use of INSPIRE has been demonstrated within an immersive visualization of ST data. Although it represents a promising tool, it requires technical adjustments, notably to correct certain interaction errors noted during the test phases. We hope that this approach will benefit the community and serve as a basis for future projects.

Acknowledgments

This work was supported by the project 5G for Metropolis, Local and Regional Authorities (Project 101181316), co-funded by European Union.

References

[1] Fouad Shoie Alallah, Shariff AM Faleel, Yumiko Sakamoto, Bradley Rey, and Pourang Irani. 2022. SSCA: Situated Space-time Cube Analytics. In *EuroVis (Short Papers)*. 25–29.

[2] Gennady Andrienko, Natalia Andrienko, Peter Bak, Daniel Keim, Slava Kisilevich, and Stefan Wrobel. 2011. A conceptual framework and taxonomy of techniques for analyzing movement. *Journal of Visual Languages & Computing* 22, 3 (2011), 213–232.
[3] Natalia Andrienko, Gennady Andrienko, and Peter Gatalsky. 2003. Exploratory spatio-temporal visualization: an analytical review. *Journal of Visual Languages & Computing* 14, 6 (2003), 503–541.

[4] Benjamin Bach, Pierre Dragicevic, Daniel Archambault, Christophe Hurter, and Sheelagh Carpendale. 2017. A descriptive framework for temporal data visualizations based on generalized space-time cubes. In *Computer graphics forum*, Vol. 36. Wiley Online Library, 36–61.

[5] Aaron Bangor, Philip Kortum, and James Miller. 2009. Determining what individual SUS scores mean: Adding an adjective rating scale. *Journal of usability studies* 4, 3 (2009), 114–123.

[6] J Brooke. 1996. SUS: A quick and dirty usability scale. *Usability Evaluation in Industry* (1996).

[7] Wolfgang Büschel, Anke Lehmann, and Raimund Dachselt. 2021. Miria: A mixed reality toolkit for the in-situ visualization and analysis of spatio-temporal interaction data. In *Proceedings of the 2021 CHI conference on human factors in computing systems*. 1–15.

[8] Mehmet Buyukdemcioglu and Sultan Kocaman. 2022. Development of a smart city concept in virtual reality environment. *International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences* 43 (2022), 51–58.

[9] Davide Ceneda, Theresa Gschwandtner, Thorsten May, Silvia Miksch, Hans-Jörg Schulz, Marc Streit, and Christian Tominski. 2016. Characterizing guidance in visual analytics. *IEEE transactions on visualization and computer graphics* 23, 1 (2016), 111–120.

[10] Jie Chen, Shih-Lung Shaw, Hongbo Yu, Feng Lu, Yanwei Chai, and Qinglei Jia. 2011. Exploratory data analysis of activity diary data: a space–time GIS approach. *Journal of Transport Geography* 19, 3 (2011), 394–404.

[11] Wei Chen, Zhaosong Huang, Feiran Wu, Minfeng Zhu, Huihua Guan, and Ross Maciejewski. 2017. VAUD: A visual analysis approach for exploring spatio-temporal urban data. *IEEE transactions on visualization and computer graphics* 24, 9 (2017), 2636–2648.

[12] Rutheva Cheheang, Patrick Saalfeld, Fabian Joeres, Christian Boedecker, Tobias Huber, Florentine Huettl, Hauke Lang, Bernhard Preim, and Christian Hansen. 2021. A collaborative virtual reality environment for liver surgery planning. *Computers & Graphics* 99 (2021), 234–246.

[13] Henrique Galvan Debarba, Marcelo Elias De Oliveira, Alexandre Lädermann, Sylvain Chagué, and Caecilia Charbonnier. 2018. Augmented reality visualization of joint movements for rehabilitation and sports medicine. In *2018 20th Symposium on Virtual and Augmented Reality (SVR)*. IEEE, 114–121.

[14] Miguel Angel Dilena and Marie Soressi. 2020. Reconstructive archaeology: In situ visualisation of previously excavated finds and features through an ongoing mixed reality process. *Applied Sciences* 10, 21 (2020), 7803.

[15] Samiha Fadloun, Yacine Morakeb, Erick Cuenca, and Kheireddine Choutri. 2022. TrajectoryVis: a visual approach to explore movement trajectories. *Social Network Analysis and Mining* 12, 1 (2022), 53.

[16] Gwendal Fouché, Ferran Argelaguet, Emmanuel Faure, and Charles Kervrann. 2023. Immersive and interactive visualization of 3D spatio-temporal data using a space time hypercube: Application to cell division and morphogenesis analysis. *Frontiers in Bioinformatics* 3 (2023), 99891.

[17] Song Gao. 2015. Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age. *Spatial Cognition & Computation* 15, 2 (2015), 86–114.

[18] Flavio Gortana, Sebastian Kaim, Martin von Lupin, and Till Nagel. 2014. Isoscope-Visualizing temporal mobility variance with isochrone maps. *Poster Abstracts of IEEE VIS 2014* (2014).

[19] Martin Guler, Valmir Bekiri, Matthias Baldauf, and Hans-Dieter Zimmermann. 2024. Virtual Reality for Home-Based Citizen Participation in Urban Planning–An Exploratory User Study. In *International Conference on Human-Computer Interaction*. Springer, 38–49.

[20] Torsten Hägerstrand. 1970. What about people in regional science. *Transport Sociology: Social aspects of transport planning* (1970), 143–158.

[21] Hassenzahl, Marc and Burmester, Michael and Koller, Franz. 2025. AttrakDiff – Measuring User Experience. https://www.attrakdiff.de/index-en.html.

[22] François Homps, Yohan Beugin, and Romain Vuillemot. 2020. Revid: Exploration and filtering of trajectories in an immersive environment using 3d shapes. In *2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)*. IEEE, 729–737.

[23] Sebastian Hubenschmid, Jonathan Wieland, Daniel Immanuel Fink, Andrea Batch, Johannes Zagermann, Niklas Elmqvist, and Harald Reiterer. 2022. Relive: Bridging in-situ and ex-situ visual analytics for analyzing mixed reality user studies. In *Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems*. 1–20.

[24] Paul Janecek and Pearl Pu. 2005. An evaluation of semantic fisheye views for opportunistic search in an annotated image collection. *International Journal on Digital Libraries* 5 (2005), 42–56.

[25] Simon Kloiber, Peter Collins, Eva Eggeling, Reinhold Preiner, and Tobias Schreck. 2022. VR-based Exploration of Participant Movement in Experimental Psychology. In *2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)*. IEEE, 391–396.

[26] Irma Kveladze, Menno-Jan Kraak, and Corné PJM Van Elzakker. 2015. The space-time cube as part of a GeoVisual analytics environment to support the understanding of movement data. *International Journal of Geographical Information Science* 29, 11 (2015), 2001–2016.

[27] Kai Liu, Yuji Murayama, and Toshiaki Ichinose. 2021. A multi-view of the daily urban rhythms of human mobility in the Tokyo metropolitan area. *Journal of Transport Geography* 91 (2021), 102985.

[28] Xinyi Liu, Qunying Huang, Zhenlong Li, and Meiliu Wu. 2017. The impact of MTUP to explore online trajectories for human mobility studies. In *Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility*. 1–9.

[29] Weizhou Luo, Zhongyuan Yu, Rufat Rzayev, Marc Satkowski, Stefan Gumhold, Matthew McGinity, and Raimund Dachselt. 2023. PEARL: Physical environment based augmented reality lenses for in-situ human movement analysis. In *Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems*. 1–15.

[30] Gonzalo Gabriel Méndez and Miguel A Nacenta. 2016. Opportunistic visualization with iVoLVER. In *2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)*. IEEE, 234–235.

[31] Aline Menin, Sonia Chardonnel, Paule-Annick Davoine, and Luciana Nedel. 2019. ESTIME: Towards an all-in-one geovisualization environment for daily mobility analysis. In *2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)*. IEEE, 39–46.

[32] Aline Menin, Sonia Chardonnel, Paule-Annick Davoine, Michael Ortega, Etienne Duble, and Luciana Nedel. 2020. eSTIME: une approche visuelle, interactive et modulable pour l'analyse multi-points de vue des mobilités quotidiennes. *Geomatica* 74, 3 (2020), 65–86.

[33] Aline Menin, Clément Quere, Jorge Wagner, Sonia Chardonnel, Paule-Annick Davoine, Wolfgang Stuerzlinger, Carla Maria Dal Sasso Freitas, Luciana Nedel, and Marco Winckler. 2025. What about thematic information? An analysis of the multidimensional visualization of individual mobility. *Visual Informatics* (Feb. 2025). doi:10.1016/j.visinf.2025.02.002 Publisher: Elsevier.

[34] Jeremy L Mennis, Donna J Peuquet, and Liujian Qian. 2000. A conceptual framework for incorporating cognitive principles into geographical database representation. *International Journal of Geographical Information Science* 14, 6 (2000), 501–520.

[35] Nelusa Pathmanathan, Seyda Öney, Michael Becher, Michael Sedlmair, Daniel Weiskopf, and Kuno Kurzhals. 2023. Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments. In *Computer graphics forum*, Vol. 42. Wiley Online Library, 385–396.

[36] Mattias Persson. 2020. A survey of methods for visualizing spatio-temporal data.

[37] Donna J Peuquet. 1994. It's about time: A conceptual framework for the representation of temporal dynamics in geographic information systems. *Annals of the Association of American Geographers* 84, 4 (1994), 441–461.

[38] Patrick Reipschläger, Frederik Brudy, Raimund Dachselt, Justin Matejka, George Fitzmaurice, and Fraser Anderson. 2022. Avatar: An immersive analysis environment for human motion data combining interactive 3d avatars and trajectories. In *Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems*. 1–15.

[39] Aizhu Ren, Chi Chen, and Yuan Luo. 2008. Simulation of emergency evacuation in virtual reality. *Tsinghua Science and Technology* 13, 5 (2008), 674–680.

[40] Michel Rzeszewski and Matuez Orylski. 2021. Usability of WebXR visualizations in urban planning. *ISPRS International Journal of Geo-Information* 10, 11 (2021), 721.

[41] Disha Sardana, Sampanna Yashwant Kahu, Denis Gračanin, and Krešimir Matković. 2021. Multi-modal data exploration in a mixed reality environment using coordinated multiple views. In *International Conference on Human-Computer Interaction*. Springer, 337–356.

[42] Hansi Senaratne, Manuel Mueller, Michael Behrisch, Felipe Lalanne, Javier Bustos-Jiménez, Jörn Schneidewind, Daniel Keim, and Tobias Schreck. 2017. Urban mobility analysis with mobile network data: A visual analytics approach. *IEEE Transactions on Intelligent Transportation Systems* 19, 5 (2017), 1537–1546.

[43] Lei Shi, ZC Guo, Tao Jiang, Ruiyu Fang, Yang Chen, Ye Zhao, Xiatian Zhang, and Min Dai. 2021. Visual analysis of steady-state human mobility in cities. *Human-Centr Comput Inform Sci* 11 (2021), 31.

[44] Ben Shneiderman. 2003. The eyes have it: A task by data type taxonomy for information visualizations. In *The craft of information visualization*. Elsevier, 364–371.

[45] Pedro Silva, Catarina Maças, Joao Correia, Penousal Machado, and Evgheni Polisciuc. 2022. ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data.. In *VISIGRAPP (3: IVAPP)*. 88–100.

[46] Seung Youb Ssin, James A Walsh, Ross T Smith, Andrew Cunningham, and Bruce H Thomas. 2019. Geogate: Correlating geo-temporal datasets using an augmented reality space-time cube and tangible interactions. In *2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)*. IEEE, 210–219.

[47] Jorge Wagner, Claudio T Silva, Wolfgang Stuerzlinger, and Luciana Nedel. 2024. Reimagining TaxiVis through an Immersive Space-Time Cube metaphor and reflecting on potential benefits of Immersive Analytics for urban data exploration. In *2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)*. IEEE, 827–838.
[48] Jorge A Wagner Filho, Wolfgang Stuerzlinger, and Luciana Nedel. 2019. Evaluat-
ing an immersive space-time cube geovisualization for intuitive trajectory data
exploration. *IEEE transactions on visualization and computer graphics* 26, 1 (2019),
514–524.

[49] Chaoming Wang, Valeria Garro, Veronica Sundstedt, Yan Hu, and Prashant Goswami. 2025. Immersive analytics meets artificial intelligence: A systematic review. *Computational Visual Media* (2025).

[50] Fei Wang, Wei Chen, Ye Zhao, Tianyu Gu, Siyuan Gao, and Hujun Bao. 2017. Adaptively exploring population mobility patterns in flow visualization. *IEEE Transactions on Intelligent Transportation Systems* 18, 8 (2017), 2250–2259.

[51] Nicole Weidinger, Neven El Sayed, Granit Luzhnica, Tobias Schreck, and Eduardo Veas. 2023. Immersive Analysis of Spatiotemporal Racing Data. In *Proceedings of the 2023 ACM Symposium on Spatial User Interaction*. 1–12.

[52] Meng-Jia Zhang, Jie Li, and Kang Zhang. 2015. Using virtual reality technique to enhance experience of exploring 3D trajectory visualizations. In *Proceedings of the 8th International Symposium on Visual Information Communication and Interaction*. 168–169.

[53] Ying Zhang, Karsten Klein, Falk Schreiber, and Kamran Safi. 2023. Beyond the horizon: immersive developments for animal ecology research. *Visual Computing for Industry, Biomedicine, and Art* 6, 1 (2023), 11.