Decision-making in energy control rooms relies on visualising complex, dynamic networks through single-line diagrams (SLDs), yet the tasks these views support remain under-characterised. From a systematic review of 42 papers, we coded 202 tasks using Munzner’s WHY–WHAT–HOW framework and Lee et al.’s graph-task taxonomy. Analysis identified six task themes coalescing into five archetypes: topology exploration using shape/orientation; line-attribute comparison through size/alignment; temporal evolution via animation; alarm triage using colour/luminance; and overview synthesis through coordinated views. Assessment with 42 field tasks from real control room observations demonstrated that the archetypes show coverage, clean boundaries, and distinctive visual channels. Our findings reveal critical needs for semantic zoom, temporal continuity, and adaptive view coordination in control room design. Beyond infrastructure domains, this work highlights fundamental visualisation challenges in representing dynamic topologies, encoding multiple attributes in dense configurations, and managing attention across views. Further study should balance geographic reality with operational clarity.

With the increasing emphasis on real-world applications of Extended Reality (XR), the field faces growing challenges in creating meaningful, user-centred, and ethical experiences. Understanding how human values have been considered in prior research is essential for ensuring that future XR experiences are designed with a deep awareness of human needs, behaviours, and cultural contexts. This study presents a systematic review of human values in the extant XR literature. It aims to address which values have been explored, how they have been studied, and why they have been considered in XR contexts. We report findings on authors' research motivations, approaches to studying values, application domains, forms of technology, target groups and associated values, as well as identified directions for future research. Based on these findings, we propose a structured analysis that synthesises key trends and gaps, providing a foundation for future research to advance more inclusive, ethically grounded, and context-aware XR design.

Figure 1: Overview of the research study.Left: A multi-sensory artefact combining contrasting spatialised audio and haptic feedback supports BLV dancers to align with a teacher's movement.Right: Three reflexive themes illustrate how the artefact shaped learning, teaching, and multi-sensory adaptation.

Dance teachers rely primarily on verbal instructions and visual demonstrations to convey key dance concepts and movement. These techniques, however, have limitations in supporting students who are blind or have low vision (BLV). This work explores the role technology can play in supporting instruction for BLV students, as well as improvisation with their instructor. Through a series of design workshops with dance instructors and BLV students, ideas were generated by physically engaging with probes featuring diverse modalities including tactile objects, a body tracked sound and musical probe, and a body tracked controller with vibrational feedback. Implications for the design of supporting technologies were discovered for four contemporary dance learning goals: learning a phrase; improvising; collaborating through movement; and awareness of body and movement qualities. We discuss the potential of numerous multi-sensory methods and artefacts, and present design considerations for technologies to support meaningful dance instruction and participation.

Despite people’s significant role in generating data in cities, their involvement in data governance (DG) remains limited, failing to address the inherent complexity of DG and undermining their ’right to the city’. We propose a collaborative systems thinking approach as a scoping tool for co-design, enabling researchers and designers to involve people in co-creating an understanding of the systemic structures underpinning DG in cities and developing prototypes and solutions informed by these structures. Using causal loop diagrams, we facilitated the development of a conceptual model of DG. Participants, representing diverse perspectives, created individual causal loop diagrams that were merged into a collaborative causal loop diagram (C-CLD). This C-CLD was employed in an interactive workshop to identify intervention points and develop targeted solutions. Our findings demonstrate how C-CLDs can accommodate multiplicity, foster agonism, and enable participants to challenge political dimensions and existing systemic structures. Moreover, the engagement process revealed the complexity of DG in the city, as perceived by the collective of participants, resulting in three key submodules that highlight tensions between citizen sensitisation to data collection, the private sector’s role in fulfilling citizens’ needs, and the struggles faced by local governments. This work draws on and extends HCI research that engages with systems thinking ontologies, contributing to an HCI that includes the political, moves beyond solutionism, and advances social justice-oriented approaches.

Wearable eye-tracking in field studies presents challenges in synchronising gaze data with dynamic stimuli and integrating observational notes from multiple observers. Existing tools often struggle to visualise eye-tracking patterns in complex, real-world environments with frequently changing areas of interest (AOIs). To address this, we propose a streamlined workflow that simplifies analysis preparation by integrating real-time observer notes with eye-tracking data with enhanced timestamp-based synchronisation, improving data mapping, and automating AOI detection with an energy control room use case. This workflow makes eye-tracking tools like Gazealytics more practical for complex field studies. By streamlining data preparation and automation, our method enhances the scalability and usability of eye-tracking analysis in complex environments, enabling more efficient and accurate visual analysis of real-world decision-making.

Cycling has gained popularity due to growing interest in healthy and sustainable lifestyles. Simultaneously, Augmented Reality (AR) Head-Mounted Displays (HMDs) can assist cyclists by presenting notifications within their field of view without diverting their attention to external devices. While previous studies have investigated these advantages, safety concerns have primarily limited them to lab settings, creating a notable gap in understanding their real-world feasibility. We conducted a user study with 20 participants on a shared-use outdoor path and explored the impact of text-based HMD notification placement (top, right, bottom), on attentional tunneling and cyclists' experiences. Our results suggested that while the bottom placement received higher scores for perceived safety and noticeability, HMD notifications induced attentional tunneling, regardless of placement. We discuss our findings and present design insights for future HMD systems aimed at enhancing cyclists' safety and experience.

Current mixed reality (MR) designs predominantly prioritise functionality and usability, often overlooking individual's diverse value needs. To create more meaningful MR experiences, this paper aims to address this gap by exploring how human values can be integrated into MR design. We propose a values-based design process and evaluate it through three design workshops considering a remote collaborative learning scenario. By comparing our approach to an existing MR application for collaborative learning, we demonstrate how embedding human values into MR design can lead to more ethical and human-centred outcomes. Our findings contribute to advancing the design and application of MR technologies to be more aligned with people's diverse needs and values.

The rapid development of immersive technologies is heralding a shift from purely physical environments to one that seamlessly mixes the physical and the digital. As these Mixed Reality (MR) worlds develop quickly we need to reflect on how human values are incorporated into the design and deployment of the technology. Human values map our perception of the world, reflect attitudes, guide behaviours, and provide us with social and moral grounding. However, there is limited research on incorporating values in the design of MR technologies. This research has three contributions: (1) a playful values-driven workshop design, (2) insights into the values of different groups of people in diverse MR scenarios, and (3) recommendations for incorporating human values for future MR design and application. This work will contribute to improving the ethical and responsible development of current and future MR applications.

OBJECTIVE: The Australian Cancer Atlas (ACA) aims to provide small-area estimates of cancer incidence and survival in Australia to help identify and address geographical health disparities. We report on the 21-month user-centered design study to visualize the data, in particular, the visualization of the estimate uncertainty for multiple audiences. MATERIALS AND METHODS: The preliminary phases included a scoping study, literature review, and target audience focus groups. Several methods were used to reach the wide target audience. The design and development stage included digital prototyping in parallel with Bayesian model development. Feedback was sought from multiple workshops, audience focus groups, and regular meetings throughout with an expert external advisory group. RESULTS: The initial scoping identified 4 target audience groups: the general public, researchers, health practitioners, and policy makers. These target groups were consulted throughout the project to ensure the developed model and uncertainty visualizations were effective for communication. In this paper, we detail ACA features and design iterations, including the 3 complementary ways in which uncertainty is communicated: the wave plot, the v-plot, and color transparency. DISCUSSION: We reflect on the methods, design iterations, decision-making process, and document lessons learned for future atlases. CONCLUSION: The ACA has been hugely successful since launching in 2018. It has received over 62 000 individual users from over 100 countries and across all target audiences. It has been replicated in other countries and the second version of the ACA was launched in May 2024. This paper provides rich documentation for future projects.

Actively participating in body movement such as dance, sports, and fitness activities is challenging for people who are blind or have low vision (BLV). Teachers primarily rely on verbal instructions and physical demonstrations with limited accessibility. Recent work shows that technology can support body movement education for BLV people. However, there is limited involvement with the BLV community and their teachers to understand their needs. By conducting a series of two surveys, 23 interviews and four focus groups, we gather the voices and perspectives of BLV people and their teachers. This provides a rich understanding of the challenges of body movement education. We identify ten major themes, four key design challenges, and propose potential solutions. We encourage the assistive technologies community to co-design potential solutions to these identified design challenges promoting the quality of life of BLV people and supporting the teachers in the provision of inclusive education.

Recent studies have explored how users of immersive visualisation systems arrange data representations in the space around them. Generally, these have focused on placement centred at eye-level in absolute room coordinates. However, work in HCI exploring full-body interaction has identified zones relative to the user’s body with different roles. We encapsulate the possibilities for visualisation view management into a design space (called “DataDancing”). From this design space we extrapolate a variety of view management prototypes, each demonstrating a different combination of interaction techniques and space use. The prototypes are enabled by a full-body tracking system including novel devices for torso and foot interaction. We explore four of these prototypes, encompassing standard wall and table-style interaction as well as novel foot interaction, in depth through a qualitative user study. Learning from the results, we improve the interaction techniques and propose two hybrid interfaces that demonstrate interaction possibilities of the design space.

We present a novel, web-based visual eye-tracking analytics tool called Gazealytics. Our open-source toolkit features a unified combination of gaze analytics features that support flexible exploratory analysis, along with annotation of areas of interest (AOI) and filter options based on multiple criteria to visually analyse eye tracking data across time and space. Gazealytics features coordinated views unifying spatiotemporal exploration of fixations and scanpaths for various analytical tasks. A novel matrix representation allows analysis of relationships between such spatial or temporal features. Data can be grouped across samples, user-defined AOIs or time windows of interest (TWIs) to support aggregate or filtered analysis of gaze activity. This approach exceeds the capabilities of existing systems by supporting flexible comparison between and within subjects, hypothesis generation, data analysis and communication of insights. We demonstrate in a walkthrough that Gazealytics supports multiple types of eye tracking datasets and analytical tasks.

Immersive analytics research has explored how embodied data representations and interactions can be used to engage users in sensemaking. Prior research has broadly overlooked the potential of immersive space for supporting analytic provenance, the understanding of sensemaking processes through users’ interaction histories. We propose the concept of embodied provenance, the use of three-dimensional space and embodied interactions in supporting recalling, reproducing, annotating and sharing analysis history in immersive environments. We design a conceptual framework for embodied provenance by highlighting a set of design criteria for analytic provenance drawn from prior work and identifying essential properties for embodied provenance. We develop a prototype system in virtual reality to demonstrate the concept and support the conceptual framework by providing multiple data views and embodied interaction metaphors in a large virtual space. We present a use case scenario of energy consumption analysis and evaluated the system through a qualitative evaluation with 17 participants, which show the system’s potential for assisting analytic provenance using embodiment. Our exploration of embodied provenance through this prototype provides lessons learnt to guide the design of immersive analytic tools for embodied provenance.

We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis.

Eye tracking is growing in popularity for multiple application areas, yet analysing and exploring the large volume of complex data remains difficult for most users. We present a comprehensive eye tracking visual analytics system to enable the exploration and presentation of eye-tracking data across time and space in an efficient manner. The application allows the user to gain an overview of general patterns and perform deep visual analysis of local gaze exploration. The ability to link directly to the video of the underlying scene allows the visualisation insights to be verified on the fly. The system was motivated by the need to analyse eye-tracking data collected from an ‘in the wild’ study with energy network operators and has been further evaluated via interviews with 14 eye-tracking experts in multiple domains. Results suggest that, thanks to state-of-the-art visualisation techniques and by providing context with videos, our system could enable an improved analysis of eye-tracking data through interactive exploration, facilitating comparison between different participants or conditions, thus enhancing the presentation of complex data analysis to non-experts. This research paper provides four contributions: (1) analysis of a motivational use case demonstrating the need for rich visual-analytics workflow tools for eye-tracking data; (2) a highly dynamic system to visually explore and present complex eye-tracking data; (3) insights from our applied use case evaluation and interviews with experienced users demonstrating the potential for the system and visual analytics for the wider eye-tracking community.

Meta-Reinforcement Learning (RL) algorithms promise to leverage prior task experience to quickly learn new unseen tasks. Unfortunately, evaluating meta-RL algorithms is complicated by a lack of suitable benchmarks. In this paper we propose adapting a challenging real-world heating, ventilation and air-conditioning (HVAC) control benchmark for meta-RL. Unlike existing benchmark problems, HVAC control has a broader task distribution, and sources of exogenous stochasticity from price and weather predictions which can be shared across task definitions. This can enable greater differentiation between the performance of current meta-RL approaches, and open the way for future research into algorithms that can adapt to entirely new tasks not sampled from the current task distribution.

Effective use of data is an essential asset to modern cities. Visualization as a tool for analysis, exploration, and communication has become a driving force in the task of unravelling our complex urban fabrics. This paper outlines the findings from a series of three workshops from 2018-2020 bringing together experts in urban data visualization with the aim of exploring multidisciplinary perspectives from the human-centric lens. Based on the rich and detailed workshop discussions identifying challenges and opportunities for urban data visualization research, we outline major human-centric themes and considerations fundamental for CityVis design and introduce a framework for an urban visualization design space.

Increased reliance on data in decision-making has highlighted the importance of conveying uncertainty in data visualisations. Yet developing visualisation techniques that clearly and accurately convey uncertainty in data is an open challenge across a variety of fields. This is especially the case when visualising temporal uncertainty. To facilitate the development of innovative and accessible temporal uncertainty visualisation techniques and respond to an identified gap in the literature, we propose the first-ever survey of over 50 temporal uncertainty visualisation techniques deployed in numerous fields. Our paper offers two contributions. First, we propose a novel taxonomy to be applied when classifying temporal uncertainty visualisation techniques. This takes into account the visualisation's intended audience, as well as its level of discreteness in representing uncertainty. Second, we urge researchers and practitioners to use a greater variety of visualisations which differ in terms of their discreteness. In doing so, we believe that a more robust evaluation of visualisation techniques can be achieved.

The HCI community has a rich history of finding new ways to engage people with data beyond the screen. With our work, we aim to expand the scope of how interaction design can engage people, arguing that “eating data” has the potential to allow people to experience “data as delight”. With reference to prior work and our design research findings, we discuss the advantages and the challenges of this approach to integrating data and food. We then identify four themes to guide the design of engagements with data through food: food form, food commensality, food ephemerality, and emotional response to food. Within these design themes, we articulate twelve insights for interaction designers to use when working on serving data as delight.

Data visualisation has proven to help support engagement and understanding of complex processes; however, few visualisations of microgrid energy systems exist. In this research we explore the area of microgrid visualisation, motivated by the establishment of a microgrid at Monash University, Australia. Using the Monash Microgrid as a case study, we follow a user-centred visualisation design process. Interviewing diverse microgrid stakeholders enabled the gathering of data and visualisation requirements and allowed us to identify key visual tasks. These were prioritised based on feasibility. A digital prototype was designed and developed for a large screen display. The prototype was formally evaluated with stakeholder participants and suggestions for future work were collected. The microgrid experts deemed the proposed prototype effective for their needs, with further potential in the energy domain.

Collaborative visual analytics leverages social interaction to support data exploration and sensemaking. These processes are typically imagined as formalised, extended activities, between groups of dedicated experts, requiring expertise with sophisticated data analysis tools. However, there are many professional domains that benefit from support for short 'bursts' of data exploration between a subset of stakeholders with a diverse breadth of knowledge. Such 'casual collaborative' scenarios will require engaging features to draw users' attention, with intuitive, 'walk-up and use' interfaces. This paper presents Uplift, a novel prototype system to support 'casual collaborative visual analytics' for a campus microgrid, co-designed with local stakeholders. An elicitation workshop with key members of the building management team revealed relevant knowledge is distributed among multiple experts in their team, each using bespoke analysis tools. Uplift combines an engaging 3D model on a central tabletop display with intuitive tangible interaction, as well as augmented-reality, mid-air data visualisation, in order to support casual collaborative visual analytics for this complex domain. Evaluations with expert stakeholders from the building management and energy domains were conducted during and following our prototype development and indicate that Uplift is successful as an engaging backdrop for casual collaboration. Experts see high potential in such a system to bring together diverse knowledge holders and reveal complex interactions between structural, operational, and financial aspects of their domain. Such systems have further potential in other domains that require collaborative discussion or demonstration of models, forecasts, or cost-benefit analyses to high-level stakeholders.

We introduce Tilt Map, a novel interaction technique for intuitively transitioning between 2D and 3D map visualisations in immersive environments. Our focus is visualising data associated with areal features on maps, for example, population density by state. Tilt Map transitions from 2D choropleth maps to 3D prism maps to 2D bar charts to overcome the limitations of each. Our article includes two user studies. The first study compares subjects' task performance interpreting population density data using 2D choropleth maps and 3D prism maps in virtual reality (VR). We observed greater task accuracy with prism maps, but faster response times with choropleth maps. The complementarity of these views inspired our hybrid Tilt Map design. Our second study compares Tilt Map to: a side-by-side arrangement of the various views; and interactive toggling between views. The results indicate benefits for Tilt Map in user preference; and accuracy (versus side-by-side) and time (versus toggle).

This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia (Monash IA Lab). The lab has been influential with contributions in algorithms, interaction techniques and experimental results in Network Visualisation, Interactive Optimisation and Geographic and Cartographic visualisation. It has also been a leader in the emerging topic of Immersive Analytics, which explores natural interactions and immersive display technologies in support of data analytics. We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly.

In this paper, we present our analysis of five expert interviews, each from a different application domain. Such analysis is crucial to understanding the real-world scenarios of analysing geographically-embedded flow data. The results of our analysis show that similar high-level tasks were conducted in different domains. To better describe the targets of these tasks, we proposed three flow-targets for analysing geographically-embedded flow data: single flow, total flow and regional flow.

With increasing electricity demand, implementing smart energy saving strategies in residential houses becomes more important than ever before. Real-time and context-aware recommendation systems can provide residents with useful information to monitor their energy consumption, predict future usage, and recommend shifting their load to another time period. This study aims to improve the management of residential loads at the consumer level while at the same time providing energy providers with an overview of energy usage at the household and substation levels. This includes real time, historical and predicted usage. In this paper we introduce a Context-Aware Smart Energy Recommender (CASER) that consists of a client-side mobile app and a backend web portal. The implementation uses alternative visualization techniques to provide electricity usage information and recommendations for the consumers and the energy providers. The accuracy of our context-aware prediction was evaluated using publicly available smart meter data.

Applied visualization researchers often work closely with domain collaborators to explore new and useful applications of visualization. The early stages of collaborations are typically time consuming for all stakeholders as researchers piece together an understanding of domain challenges from disparate discussions and meetings. A number of recent projects, however, report on the use of creative visualization-opportunities (CVO) workshops to accelerate the early stages of applied work, eliciting a wealth of requirements in a few days of focused work. Yet, there is no established guidance for how to use such workshops effectively. In this paper, we present the results of a 2-year collaboration in which we analyzed the use of 17 workshops in 10 visualization contexts. Its primary contribution is a framework for CVO workshops that: 1) identifies a process model for using workshops; 2) describes a structure of what happens within effective workshops; 3) recommends 25 actionable guidelines for future workshops; and 4) presents an example workshop and workshop methods. The creation of this framework exemplifies the use of critical reflection to learn about visualization in practice from diverse studies and experience.

Constraint programming allows difficult combinatorial problems to be modelled declaratively and solved automatically. Advances in solver technologies over recent years have allowed the successful use of constraint programming in many application areas. However, when a particular solver's search for a solution takes too long, the complexity of the constraint program execution hinders the programmer's ability to profile that search and understand how it relates to their model. Therefore, effective tools to support such profiling and allow users of constraint programming technologies to refine their model or experiment with different search parameters are essential. This paper details the first user-centred design process for visual profiling tools in this domain. We report on: our insights and opportunities identified through an on-line questionnaire and a creativity workshop with domain experts carried out to elicit requirements for analytical and visual profiling techniques; our designs and functional prototypes realising such techniques; and case studies demonstrating how these techniques shed light on the behaviour of the solvers in practice.

Showing flows of people and resources between multiple geographic locations is a challenging visualisation problem. We conducted two quantitative user studies to evaluate different visual representations for such dense many-to-many flows. In our first study we compared a bundled node-link flow map representation and OD Maps [37] with a new visualisation we call MapTrix. Like OD Maps, MapTrix overcomes the clutter associated with a traditional flow map while providing geographic embedding that is missing in standard OD matrix representations. We found that OD Maps and MapTrix had similar performance while bundled node-link flow map representations did not scale at all well. Our second study compared participant performance with OD Maps and MapTrix on larger data sets. Again performance was remarkably similar.

We present an efficient technique for topology-preserving map deformation and apply it to the visualization of dissimilarity data in a geographic context. Map deformation techniques such as value-by-area cartograms are well studied. However, using deformation to highlight (dis)similarity between locations on a map in terms of their underlying data attributes is novel. We also identify an alternative way to represent dissimilarities on a map through the use of visual overlays. These overlays are complementary to deformation techniques and enable us to assess the quality of the deformation as well as to explore the design space of blending the two methods. Finally, we demonstrate how these techniques can be useful in several-quite different-applied contexts: travel-time visualization, social demographics research and understanding energy flowing in a wide-area power-grid.

Comparing multiple variables to select those that effectively characterize complex entities is important in a wide variety of domains - geodemographics for example. Identifying variables that correlate is a common practice to remove redundancy, but correlation varies across space, with scale and over time, and the frequently used global statistics hide potentially important differentiating local variation. For more comprehensive and robust insights into multivariate relations, these local correlations need to be assessed through various means of defining locality. We explore the geography of this issue, and use novel interactive visualization to identify interdependencies in multivariate data sets to support geographically informed multivariate analysis. We offer terminology for considering scale and locality, visual techniques for establishing the effects of scale on correlation and a theoretical framework through which variation in geographic correlation with scale and locality are addressed explicitly. Prototype software demonstrates how these contributions act together. These techniques enable multiple variables and their geographic characteristics to be considered concurrently as we extend visual parameter space analysis (vPSA) to the spatial domain. We find variable correlations to be sensitive to scale and geography to varying degrees in the context of energy-based geodemographics. This sensitivity depends upon the calculation of locality as well as the geographical and statistical structure of the variable.

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open - enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design.