We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.

The increasing deployment of digital infrastructures in cities highlights challenges in how people shape the conditions of data production that shape their cities and lives. As such, the need to centre data governance (DG) models around people is amplified. This paper unpacks and reassesses how people-centredness materialises at the level of DG in cities by conducting a scoping review of the literature on people-centred data governance (PCDG) in cities. Utilising twelve extraction categories framed by the conceptualisation of DG as a socio-technical system, this review synthesises identified themes and outlines six archetypes. PCDG is characterised by people-centred values; the inclusion of people as agents, beneficiaries, or enablers; the employment of mechanisms for engaging people; or the pursuit of people-centred goals. These coalesce into diverse PCDG archetypes including compensation, rights-based, civic deliberation, civic representation, data donations, and community-driven models. The paper proposes a nuanced reassessment of what constitutes PCDG, focusing on whether DG models include people in the emergent benefits of data or merely legitimise their exclusion, the extent to which embedded power dynamics reflect people’s perspectives, the extent to which participation influences decision-making, and the model’s capacity to balance power asymmetries underpinning the landscape in which it is situated.

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.

Collaborative causal loop diagrams (C-CLDs) help decision makers to model complex systems and processes, but existing tools offer little support for documenting the model-building process or capturing the provenance of stakeholder contributions. In this paper, we map the C-CLD design space to derive concrete requirements for documentation and transparency. We introduce Perspectiva, an interactive prototype shaped by those requirements and refined through iterative feedback. Perspectiva enables side-by-side navigation and comparison of CLDs, codifies changes and conflicting relationships, and preserves term provenance and contributor attri bution. Its core features include anchored nodes for topological consistency, node interaction, hover-activated provenance pop-ups, and colour-coded encodings. In user studies with domain and visualisation experts, participants reported that Perspectiva improved navigation, comparison, and provenance tracking relative to static diagrams as well as highlighting opportunities for enhancement and future research.

Data is moving beyond the scientific community, flooding communication channels and addressing issues of importance to all aspects of daily life. This highlights the need for rich and expressive data representations to communicate the science on which society rests and must act. However, current visualization techniques often lack the broad visual vocabulary needed to accommodate the explosion in data scale, diversity, and audience perspectives. While previous work has mined artistic and design knowledge for color maps and shape affordances (glyphs) in visualization, line encoding has received little attention. In this paper, we report on an exploration of visual properties that extend the vocabulary of the line, particularly for categorical encoding. We describe the creation of a corpus of lines motivated by artistic practice, Gestalt theory, and design principles, and present initial results from a study of how different visual properties influence how people associate these into sets of similar lines. While very preliminary, the findings suggest that a rich set of line attributes will support both association and categorical hierarchies, as well as provoke further inquiry into how and why line encoding can be more expressive in encoding multivariate, multidimensional data.

Smart city (SC) studies that have drawn on socio-technical transitions literature do not explicitly consider data governance (DG) as a significant component of smart city transitions (SCTs). This article works towards this gap by bridging the literature on DG and socio-technical transitions in SCs. We aim to answer two overarching questions: (1) What can SCTs learn from DG?; and (2) What can DG learn from SCTs? Against this backdrop, we outline the contours of a new research agenda that could bring together both fields in productive dialogue.

In Canada, community and policy leaders have issued urgent calls to collect, analyze, and mobilize disaggregated data to inform equity-oriented initiatives aimed at addressing systemic racism and gender inequity, as well as other social inequities. This essay presents critical reflections from a national Roundtable discussion regarding how meaningful community engagement within academia–community–government research collaborations offers the opportunity to harness disaggregated data and advanced analytics to centre and address the priorities of equity-deserving and sovereignty-seeking groups. Participants emphasized four key priorities: (1) Building equitable and engaged partnerships that centre community-driven priorities and address structural barriers to community engagement; (2) Co-creating ethical data governance policies and infrastructure to support community data ownership and access; (3) Stimulating innovation and pursuing community involvement to create contextualized, advanced analyses and effective visualizations of disaggregated data; and (4) Building the capacity of all partners to effectively contribute to partnership goals. Capacity building was viewed as a bridge across a diversity of lived and professional expertise, enabling intersectoral research teams to collaborate in culturally safe and respectful ways. Beyond identifying key structural barriers impeding the promise of disaggregated data, we present practical opportunities for innovation in community-engaged scholarship to address social justice challenges in Canada.

Ambiguity is pervasive in the complex sensemaking domains of risk assessment and prediction but there remains little research on how to design visual analytics tools to accommodate it. We report on findings from a qualitative study based on a conceptual framework of sensemaking processes to investigate how both new visual analytics designs and existing tools, primarily data tables, support the cognitive work demanded in avalanche forecasting. While both systems yielded similar analytic outcomes we observed differences in ambiguous sensemaking and the analytic actions either afforded. Our findings challenge conventional visualization design guidance in both perceptual and interaction design, highlighting the need for data interfaces that encourage reflection, provoke alternative interpretations, and support the inherently ambiguous nature of sensemaking in this critical application. We review how different visual and interactive forms support or impede analytic processes and introduce "gisting" as a significant yet unexplored analytic action for visual analytics research. We conclude with design implications for enabling ambiguity in visual analytics tools to scaffold sensemaking in risk assessment.

We report a study investigating the viability of using interactive visualizations to aid architectural design with building codes. While visualizations have been used to support general architectural design exploration, existing computational solutions treat building codes as separate from, rather than part of, the design process, creating challenges for architects. Through a series of participatory design studies with professional architects, we found that interactive visualizations have promising potential to aid design exploration and sensemaking in early stages of architectural design by providing feedback about potential allowances and consequences of design decisions. However, implementing a visualization system necessitates addressing the complexity and ambiguity inherent in building codes. To tackle these challenges, we propose various user-driven knowledge management mechanisms for integrating, negotiating, interpreting, and documenting building code rules.

Dashboards are the ubiquitous means of data communication within organizations. Yet we have limited understanding of how they factor into data practices in the workplace, particularly for data workers who do not self-identify as professional analysts. We focus on data workers who use dashboards as a primary interface to data, reporting on an interview study that characterizes their data practices and the accompanying barriers to seamless data interaction. While dashboards are typically designed for data consumption, our findings show that dashboard users have far more diverse needs. To capture these activities, we frame data workers' practices as data conversations: conversations with data capture classic analysis (asking and answering data questions), while conversations through and around data involve constructing representations and narratives for sharing and communication. Dashboard users faced substantial barriers in their data conversations: their engagement with data was often intermittent, dependent on experts, and involved an awkward assembly of tools. We challenge the visualization and analytics community to embrace dashboard users as a population and design tools that blend seamlessly into their work contexts.

Ambiguity, the state in which alternative interpretations are plausible or even desirable, is an inexorable part of complex sensemaking. Its challenges are compounded when analysis involves risk, is constrained, and needs to be shared with others. We report on several studies with avalanche forecasters that illuminated these challenges and identified how visualization designs can better support ambiguity. Like many complex analysis domains, avalanche forecasting relies on highly heterogeneous and incomplete data where the relevance and meaning of such data is context-sensitive, dependant on the knowledge and experiences of the observer, and mediated by the complexities of communication and collaboration. In this paper, we characterize challenges of ambiguous interpretation emerging from data , analytic processes , and collaboration and communication and describe several management strategies for ambiguity. Our findings suggest several visual analytics design approaches that explicitly address ambiguity in complex sensemaking around risk.

As scientific data continues to grow in size, complexity, and density, the representation scope of three-dimensional spaces, data sampling methods, and transfer functions have improved in parallel, allowing visualization practitioners to produce richer multidimensional encodings. Glyphs, in particular, have become an essential encoding tool due to their versatile applications in co-located multivariate volumetric datasets. While prior work has been conducted investigating the perceptual attributes of computationally-generated three-dimensional glyph-forms for scientific visualization, their affective and expressive qualities have yet to be examined. Further, our prior work has demonstrated the benefits of artist hand-created glyph forms in contrast to commonly-used synthetic forms in increasing visual diversity, discrimination, and expressive association in complex environmental datasets. In order to begin to address this gap, we establish preliminary groundwork for an affective design space for hand-created glyph forms, produce a novel set of glyph-forms based on this design space, describe a non-verbal method for discovering affective classifications of glyph-forms adopted from current affect theory, and report the results of two studies that explore how these three-dimensional forms produce consistent affective responses across assorted study cohorts.

Abstract Data governance is an emerging field of study concerned with how a range of actors can successfully manage data assets according to rules of engagement, decision rights, and accountabilities. Urban studies scholarship has continued to demonstrate and criticize lack of community engagement in smart city development and urban data governance projects, including in local sustainability initiatives. However, few move beyond critique to unpack in more detail what community engagement should look like. To overcome this gap, we develop and test a participatory methodology to identify approaches to empowering community engagement in data governance in the context of the Monash Net Zero Precinct in Melbourne, Australia. Our approach uses design for social innovation to enable a small group of “precinct citizens” to co-design prototypes and multicriteria mapping as a participatory appraisal method to open up and reveal a diversity of perspectives and uncertainties on data governance approaches. The findings reveal the importance of creating deliberative spaces for pluralising community engagement in data governance that consider the diverse values and interests of precinct citizens. This research points toward new ways to conceptualize and design enabling processes of community engagement in data governance and reflects on implementation strategies attuned to the politics of participation to support the embedding of these innovations within specific socio-institutional contexts.

To facilitate engaging and nuanced conversations around data, we contribute a touchless approach to interacting directly with visualization in remote presentations. We combine dynamic charts overlaid on a presenter's webcam feed with continuous bimanual hand tracking, demonstrating interactions that highlight and manipulate chart elements appearing in the foreground. These interactions are simultaneously functional and deictic, and some allow for the addition of "rhetorical flourish", or expressive movement used when speaking about quantities, categories, and time intervals. We evaluated our approach in two studies with professionals who routinely deliver and attend presentations about data. The first study considered the presenter perspective, where 12 participants delivered presentations to a remote audience using a presentation environment incorporating our approach. The second study considered the audience experience of 17 participants who attended presentations supported by our environment. Finally, we reflect on observations from these studies and discuss related implications for engaging remote audiences in conversations about data.

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets - the quintessential table tool - remain a critical part of their information ecosystem, allowing them to interact with their data in ways that are hidden or abstracted in more complex tools. This is particularly true for data workers [61], people who work with data as part of their job but do not identify as professional analysts or data scientists. We report on a qualitative study of how these workers interact with and reason about their data. Our findings show that data tables serve a broader purpose beyond data cleanup at the initial stage of a linear analytic flow: users want to see and "get their hands on" the underlying data throughout the analytics process, reshaping and augmenting it to support sensemaking. They reorganize, mark up, layer on levels of detail, and spawn alternatives within the context of the base data. These direct interactions and human-readable table representations form a rich and cognitively important part of building understanding of what the data mean and what they can do with it. We argue that interactive tables are an important visualization idiom in their own right; that the direct data interaction they afford offers a fertile design space for visual analytics; and that sense making can be enriched by more flexible human-data interaction than is currently supported in visual analytics tools.

Eco-feedback aims at increasing awareness of resource use to encourage conservation. A growing area of concern in sustainable living is food waste, and many new institutional waste receptacles incorporate waste sorting and recycling instructions for waste management. However, little attention has been paid to the design of encouraging awareness of waste in the home, particularly at the point of food waste. We explored the design challenges and effectiveness of novel eco-feedback techniques at the point of food waste through an in-situ study in a university residence. Our E-COmate system captures and visualizes domestic food waste data for more readily comprehensible and accessible information within a home environment embedded in an existing waste bin. Four E-COmate smart bins were introduced, deployed and evaluated for 8 weeks at a student residence in Canada. The aim of the study was to see whether a system like E-COmate could impact food waste patterns and awareness, and if so, to what extent it engages consumers. To explore its impact, a mix of methods was adopted. Waste audits were conducted to explore waste changes. Retrospective interviews were carried out to gain insights in residences' reflections and motivations. We show that E-COmate had a positive impact on participants' awareness of and behavior toward their food waste. Participants who had E-COmate installed in their kitchens showed overall a significant decrease in food waste and in particular a decrease of almost 32% in edible or once edible food waste, and a 69% decrease in generated compost waste during the last 2 weeks compared to the first 2 baseline weeks. Furthermore, while our control group showed an increase of 244% of waste of starches and grains toward the last 2 weeks (i.e., the end of term) compared to the 2 baseline weeks, the intervention group only showed an increase of 4.5% in waste of grains and starches. Eco-feedback further engaged residences in reducing food waste practices starting at the grocery store (e.g., by buying in smaller portions). In sum, eco-feedback as provided by E-COmate had positive impacts on reducing food waste. These findings are a result of increased awareness, the constant presence and immediacy of E-COmate served as a reminder, and their understanding of how much they actually waste as a group. Their awareness was reflected in how they adapted their shopping behavior as one way to reduce waste at home.

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.

Risk assessment and follow-up of oral potentially malignant disorders in patients with mild or moderate oral epithelial dysplasia is an ongoing challenge for improved oral cancer prevention. Part of the challenge is a lack of understanding of how observable features of such dysplasia, gathered as data by clinicians during follow-up, relate to underlying biological processes driving progression. Current research is at an exploratory phase where the precise questions to ask are not known. While traditional statistical and the newer machine learning and artificial intelligence methods are effective in well-defined problem spaces with large datasets, these are not the circumstances we face currently. We argue that the field is in need of exploratory methods that can better integrate clinical and scientific knowledge into analysis to iteratively generate viable hypotheses. In this perspective, we propose that visual analytics presents a set of methods well-suited to these needs. We illustrate how visual analytics excels at generating viable research hypotheses by describing our experiences using visual analytics to explore temporal shifts in the clinical presentation of epithelial dysplasia. Visual analytics complements existing methods and fulfills a critical and at-present neglected need in the formative stages of inquiry we are facing.