We explore new interactions for wayfinding in complex, hazardous environments through the concrete case of multi-pitch rock climbing. Working with seven expert climbers, we derived design principles for navigation tools that support both pre-climb planning and on-the-wall decision making with limited physical and mental bandwidth. We then demonstrate these principles in practice through the creation of a mobile application that incorporates high-fidelity vertical terrain models, spatially-anchored multi-modal annotations, integrated human-scale avatars, and adaptable one-handed interactions. Based on our expert feedback and experiences developing the tool, as well as results from a 3-week public deployment and responses from 16 climbers, we highlight new opportunities for interactive tools for rock climbing as well as other hazardous, high-focus activities.

We explore the potential for subtle on-hand gesture and microgesture interactions for map navigation with augmented reality (AR) devices. We describe a design exercise and follow-up elicitation study in which we identified on-hand gestures for cartographic interaction primitives. Microgestures and on-hand interactions are a promising space for AR map navigation as they offers always-available, tactile, and memorable spaces for interaction. Our findings show a clear set of microgesture interaction patterns that are well suited for supporting map navigation and manipulation. In particular, we highlight how the properties of various microgestures align with particular cartographic interaction tasks. We also describe our experience creating an exploratory proof-of-concept AR map prototype which helped us identify new opportunities and practical challenges for microgesture control. Finally, we discuss how future AR map systems could benefit from on-hand and microgesture input schemes.

We examine input visualizations, visual representations that are designed to collect (and represent) new data rather than encode preexisting datasets. Information visualization is commonly used to reveal insights and stories within existing data. As a result, most contemporary visualization approaches assume existing datasets as the starting point for design, through which that data is mapped to visual encodings. Meanwhile, the implications of visualizations as inputs and as data sources have received little attention—despite the existence of visual and physical examples stretching back centuries. In this paper, we present a design space of 50 input visualizations analyzing their visual representation, data, artifact, context, and input. Based on this, we identify input modalities, purposes of input visualizations, and a set of design considerations. Finally, we discuss the relationship between input visualization and traditional visualization design and suggest opportunities for future research to better understand these visual representations and their potential.

We explore the potential of generative AI text-to-image models to help designers efficiently craft unique, representative, and demographically diverse anthropographics that visualize data about people. Currently, creating data-driven iconic images to represent individuals in a dataset often requires considerable design effort. Generative text-to-image models can streamline the process of creating these images, but risk perpetuating designer biases in addition to stereotypes latent in the models. In response, we outline a conceptual workflow for crafting anthropographic assets for visualizations, highlighting possible sources of risk and bias as well as opportunities for reflection and refinement by a human designer. Using an implementation of this workflow with Stable Diffusion and Google Colab, we illustrate a variety of new anthropographic designs that showcase the visual expressiveness and scalability of these generative approaches. Based on our experiments, we also identify challenges and research opportunities for new AI-enabled anthropographic visualization tools.

Despite an abundance of open data initiatives aimed to inform and empower "general" audiences, we still know little about the ways people outside of traditional data analysis communities experience and engage with public data and visualizations. To investigate this gap, we present results from an in-depth qualitative interview study with 19 participants from diverse ethnic, occupational, and demographic backgrounds. Our findings characterize a set of lived experiences with open data and visualizations in the domain of energy consumption, production, and transmission. This work exposes information receptivity - an individual's transient state of willingness or openness to receive information -as a blind spot for the data visualization community, complementary to but distinct from previous notions of data visualization literacy and engagement. We observed four clusters of receptivity responses to data- and visualization-based rhetoric: Information-Avoidant, Data-Cautious, Data-Enthusiastic, and Domain-Grounded. Based on our findings, we highlight research opportunities for the visualization community. This exploratory work identifies the existence of diverse receptivity responses, highlighting the need to consider audiences with varying levels of openness to new information. Our findings also suggest new approaches for improving the accessibility and inclusivity of open data and visualization initiatives targeted at broad audiences. A free copy of this paper and all supplemental materials are available at https://OSF.IO/MPQ32.

Anthropographics are human-shaped visualizations that aim to emphasize the human importance of datasets and the people behind them. However, current anthropographics tend to employ homogeneous human shapes to encode data about diverse demographic groups. Such anthropographics can obscure important differences between groups and contemporary designs exemplify the lack of inclusive approaches for representing human diversity in visualizations. In response, we explore the creation of demographically diverse anthropographics that communicate the visible diversity of demographically distinct populations. Building on previous anthropographics research, we explore strategies for visualizing datasets about people in ways that explicitly encode diversity—illustrating these approaches with examples in a variety of visual styles. We also critically reflect on strategies for creating diverse anthropographics, identifying social and technical challenges that can result in harmful representations. Finally, we highlight a set of forward-looking research opportunities for advancing the design and understanding of diverse anthropographics.

We present a video-based approach for collecting feedback on virtual reality (VR) prototypes. While developing a high-fidelity VR prototype to help neurologists analyze seizure propagation information for brain surgery planning, our neurologist collaborators’ limited availability reduced opportunities for them to give feedback on critical design decisions. In response, we developed a remote feedback process in which developers created videos of the VR design process and used these to ground iterative input from neurologist collaborators. We describe our approach and detail opportunities and challenges for video-based feedback to play a role in future VR prototyping.

We explore the use of cinematic “pre-visualization” (previs) techniques as a rapid ideation and design futuring method for human computer interaction (HCI) research. Previs approaches, which are widely used in animation and film production, use digital design tools to create medium-fidelity videos that capture richer interaction, motion, and context than sketches or static illustrations. When used as a design futuring method, previs can facilitate rapid, iterative discussions that reveal tensions, challenges, and opportunities for new research. We performed eight one-week design futuring sprints, in which individual HCI researchers collaborated with a lead designer to produce concept sketches, storyboards, and videos that examined future applications of their research. From these experiences, we identify recurring themes and challenges and present a One Week Futuring Workbook that other researchers can use to guide their own futuring sprints. We also highlight how variations of our approach could support other speculative design practices.

We explore the design and utility of situated manual self-tracking visualizations on dedicated displays that integrate data tracking into existing practices and physical environments. Situating self-tracking tools in relevant locations is a promising approach to enable reflection on and awareness of data without needing to rely on sensorized tracking or personal devices. In both a long-term autobiographical design process and a co-design study with six participants, we rapidly prototyped and deployed 30 situated self-tracking applications over a ten month period. Grounded in the experience of designing and living with these trackers, we contribute findings on logging and data entry, the use of situated displays, and the visual design and customization of trackers. Our results demonstrate the potential of customizable dedicated self-tracking visualizations that are situated in relevant physical spaces, and suggest future research opportunities and new potential applications for situated visualizations.

We present EvoIsland, a scalable interactive evolutionary user interface framework inspired by the spatially isolated land masses seen on Earth. Our generalizable interaction system encourages creators to spatially explore a wide range of design possibilities through the combination, separation, and rearrangement of hexagonal tiles on a grid. As these tiles are grouped into islandlike clusters, localized populations of designs form through an underlying evolutionary system. The interactions that take place within EvoIsland provide content creators with new ways to shape, display and assess populations in evolutionary systems that produce a wide range of solutions with visual phenotype outputs.

We compare physical and virtual reality (VR) versions of simple data visualizations and explore how the addition of virtual annotation and filtering tools affects how viewers solve basic data analysis tasks. We report on two studies, inspired by previous examinations of data physicalizations. The first study examines differences in how viewers interact with physical hand-scale, virtual hand-scale, and virtual table-scale visualizations and the impact that the different forms had on viewer's problem solving behavior. A second study examines how interactive annotation and filtering tools might support new modes of use that transcend the limitations of physical representations. Our results highlight challenges associated with virtual reality representations and hint at the potential of interactive annotation and filtering tools in VR visualizations.

We explore how the lens of fictional superpowers can help characterize how visualizations empower people and provide inspiration for new visualization systems. Researchers and practitioners often tout visualizations' ability to "make the invisible visible" and to "enhance cognitive abilities." Meanwhile superhero comics and other modern fiction often depict characters with similarly fantastic abilities that allow them to see and interpret the world in ways that transcend traditional human perception. We investigate the intersection of these domains, and show how the language of superpowers can be used to characterize existing visualization systems and suggest opportunities for new and empowering ones. We introduce two frameworks: The first characterizes seven underlying mechanisms that form the basis for a variety of visual superpowers portrayed in fiction. The second identifies seven ways in which visualization tools and interfaces can instill a sense of empowerment in the people who use them. Building on these observations, we illustrate a diverse set of "visualization superpowers" and highlight opportunities for the visualization community to create new systems and interactions that empower new experiences with data Material and illustrations are available under CC-BY 4.0 at osf.io/8yhfz.

We present I/O Bits, a prototype personal informatics system that explores the potential for user-driven and situated self-tracking. With simple tactile inputs and small e-paper visualizations, I/O Bits are dedicated physical devices that allow individuals to track and visualize different kinds of personal activities in-situ. This is in contrast to most self-tracking systems, which automate data collection, centralize information displays, or integrate into multi-purpose devices like smartwatches or mobile phones. We report findings from an e-paper visualization workshop and a prototype deployment where participants constructed their own I/O Bits and used them to track a range of personal data. Based on these experiences, we contribute insights and opportunities for situated and user-driven personal informatics.

Worlds-in-Miniature (WiMs) are interactive worlds within a world and combine the advantages of an input space, a cartographic map, and an overview+detail interface. They have been used across the extended virtuality spectrum for a variety of applications. Building on an analysis of examples of WiMs from the research literature we contribute a design space for WiMs based on seven design dimensions. Further, we expand upon existing definitions of WiMs to provide a definition that applies across the extended reality spectrum. We identify the design dimensions of size-scope-scale, abstraction, geometry, reference frame, links, multiples, and virtuality. Using our framework we describe existing Worlds-in-Miniature from the research literature and reveal unexplored research areas. Finally, we generate new examples of WiMs using our framework to fill some of these gaps. With our findings, we identify opportunities that can guide future research into WiMs.

Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and human-computer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics.

We present Activity River, a personal visualization tool which enables individuals to plan, log, and reflect on their self-defined activities. We are interested in supporting this type of reflective practice as prior work has shown that reflection can help people plan and manage their time effectively. Hence, we designed Activity River based on five design goals (visualize historical and contextual data, facilitate comparison of goals and achievements, engage viewers with delightful visuals, support authorship, and enable flexible planning and logging) which we distilled from the Information Visualization and Human-Computer Interaction literature. To explore our approach's strengths and limitations, we conducted a qualitative study of Activity River using a role-playing method. Through this qualitative exploration, we illustrate how our participants envisioned using our visualization to perform dynamic and continuous reflection on their activities. We observed that they were able to assess their progress towards their plans and adapt to unforeseen circumstances using our tool.

We introduce Corsican Twin, a tool for authoring augmented reality data visualisations in virtual reality using digital twins. The system provides users with the contextual information necessary to design embedded and situated data visualisations in a safe and convenient remote setting. We created system via a co-design process which involved people with little or no programming experience. Using the system, we illustrate three potential use cases for situated visualizations in the context of building maintenance, including: (1) on-site equipment debugging and diagnosis; (2) remote incident playback; and (3) operations simulations for future buildings. From feedback gathered during formative evaluations of our prototype tool with domain experts, we discuss implications, opportunities, and challenges for future in situ visualisation design tools.

We describe a design space of view manipulation interactions for small data-driven contextual visualizations (word-scale visualizations). These interaction techniques support an active reading experience and engage readers through exploration of embedded visualizations whose placement and content connect them to specific terms in a document. A reader could, for example, use our proposed interaction techniques to explore word-scale visualizations of stock market trends for companies listed in a market overview article. When readers wish to engage more deeply with the data, they can collect, arrange, compare, and navigate the document using the embedded word-scale visualizations, permitting more visualization-centric analyses. We support our design space with a concrete implementation, illustrate it with examples from three application domains, and report results from two experiments. The experiments show how view manipulation interactions helped readers examine embedded visualizations more quickly and with less scrolling and yielded qualitative feedback on usability and future opportunities.

Complex data visualization design projects often entail collaboration between people with different visualization-related skills. For example, many teams include both designers who create new visualization designs and developers who implement the resulting visualization software. We identify gaps between data characterization tools, visualization design tools, and development platforms that pose challenges for designer-developer teams working to create new data visualizations. While it is common for commercial interaction design tools to support collaboration between designers and developers, creating data visualizations poses several unique challenges that are not supported by current tools. In particular, visualization designers must characterize and build an understanding of the underlying data, then specify layouts, data encodings, and other data-driven parameters that will be robust across many different data values. In larger teams, designers must also clearly communicate these mappings and their dependencies to developers, clients, and other collaborators. We report observations and reflections from five large multidisciplinary visualization design projects and highlight six data-specific visualization challenges for design specification and handoff. These challenges include adapting to changing data, anticipating edge cases in data, understanding technical challenges, articulating data-dependent interactions, communicating data mappings, and preserving the integrity of data mappings across iterations. Based on these observations, we identify opportunities for future tools for prototyping, testing, and communicating data-driven designs, which might contribute to more successful and collaborative data visualization design.

We report on findings from seven design workshops that used ideation and sketching activities to prototype new situated visualizations - representations of data that are displayed in proximity to the physical referents (such as people, objects, and locations) to which the data is related. Designing situated visualizations requires a fine-grained understanding of the context in which the visualizations are placed, as well as an exploration of different options for placement and form factors, which existing methods for visualization design do not account for. Focusing on small displays as a target platform, we reflect on our experiences of using a diverse range of sketching activities, materials, and prompts. Based on these observations, we identify challenges and opportunities for sketching and ideating situated visualizations. We also outline the space of design activities for situated visualization and highlight promising methods for both designers and researchers.

We present results from two studies comparing the performance of four different navigation techniques (flight, teleportation, world-in-miniature, and 3D cone-drag) and their combinations in large virtual reality map environments. While prior work has individually examined each of these techniques in other settings, our study presents the first direct comparison between them in large open environments, as well as one of the first comparisons in the context of current-generation virtual reality hardware. Our first study compared common techniques (flight, teleportation, and world-in-miniature) for search and navigation tasks. A follow-up study compared these techniques against 3D cone drag, a direct-manipulation navigation technique used in contemporary tools like Google Earth VR. Our results show the strength of flight as a stand-alone navigation technique, but also highlight five specific ways in which viewers can combine teleportation, world-in-miniature, and 3D cone drag with flight, drawing on the relative strengths of each technique to compensate for the weaknesses of others.

We introduce three lightweight interactive camera control techniques for 3D terrain maps on touch devices based on a look-from metaphor (Discrete Look-From-At, Continuous Look-From-Forwards, and Continuous Look-From-Towards). These techniques complement traditional touch screen pan, zoom, rotate, and pitch controls allowing viewers to quickly transition between top-down, oblique, and ground-level views. We present the results of a study in which we asked participants to perform elevation comparison and line-of-sight determination tasks using each technique. Our results highlight how look-from techniques can be integrated on top of current direct manipulation navigation approaches by combining several direct manipulation operations into a single look-from operation. Additionally, they show how look-from techniques help viewers complete a variety of common and challenging map-based tasks.

Freehand gesture interaction has long been proposed as a `natural' input method for Augmented Reality (AR) applications, yet has been little explored for intensive applications like multiscale navigation. In multiscale navigation, such as digital map navigation, pan and zoom are the predominant interactions. A position-based input mapping (e.g. grabbing metaphor) is intuitive for such interactions, but is prone to arm fatigue. This work focuses on improving digital map navigation in AR with mid-air hand gestures, using a horizontal intangible map display. First, we conducted a user study to explore the effects of handedness (unimanual and bimanual) and input mapping (position-based and rate-based). From these findings we designed DiveZoom and TerraceZoom, two novel hybrid techniques that smoothly transition between position- and rate-based mappings. A second user study evaluated these designs. Our results indicate that the introduced input-mapping transitions can reduce perceived arm fatigue with limited impact on performance.

We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality.

Immersive analytics uses augmented and virtual reality technologies to better understand and interact with multi-dimensional data within a physical space. NiwViw is an application that allows non-technical users to create their own immersive visualizations. NiwViw currently supports the iPad, Microsoft HoloLens, and the HTC Vive.

In this paper we consider various genres of citizen science from the perspective of citizen participants. As a mode of scientific inquiry, citizen science has the potential to "scale up" scientific data collection efforts and increase lay engagement with science. However, current technological directions risk losing sight of the ways in which citizen science is actually practiced. As citizen science is increasingly used to describe a wide range of activities, we begin by presenting a framework of citizen science genres. We then present findings from four interlocking qualitative studies and technological interventions of community air quality monitoring efforts, examining the motivations and capacities of citizen participants and characterizing their alignment with different types of citizen science. Based on these studies, we suggest that data acquisition involves complex multi-dimensional tradeoffs, and the commonly held view that citizen science systems are a win-win for citizens and science may be overstated.

In this paper we analyze the role of physical scale models in the architectural design process and apply insights from architecture for the creation and use of data physicalizations. Based on a survey of the architecture literature on model making and ten interviews with practicing architects, we describe the role of physical models as a tool for exploration and communication. From these observations, we identify trends in the use of physical models in architecture, which have the potential to inform the design of data physicalizations. We identify four functions of architectural modeling that can be directly adapted for use in the process of building rich data models. Finally, we discuss how the visualization community can apply observations from architecture to the design of new data physicalizations.

We contribute an investigation of the design and function of word-scale graphics and visualizations embedded in text documents. Word-scale graphics include both data-driven representations such as word-scale visualizations and sparklines, and non-data-driven visual marks. Their design, function, and use has so far received little research attention. We present the results of an open ended exploratory study with nine graphic designers. The study resulted in a rich collection of different types of graphics, data provenance, and relationships between text, graphics, and data. Based on this corpus, we present a systematic overview of word-scale graphic designs, and examine how designers used them. We also discuss the designers' goals in creating their graphics, and characterize how they used word-scale graphics to visualize data, add emphasis, and create alternative narratives. Building on these examples, we discuss implications for the design of authoring tools for word-scale graphics and visualizations, and explore how new authoring environments could make it easier for designers to integrate them into documents.

We introduce embedded data representations, the use of visual and physical representations of data that are deeply integrated with the physical spaces, objects, and entities to which the data refers. Technologies like lightweight wireless displays, mixed reality hardware, and autonomous vehicles are making it increasingly easier to display data in-context. While researchers and artists have already begun to create embedded data representations, the benefits, trade-offs, and even the language necessary to describe and compare these approaches remain unexplored. In this paper, we formalize the notion of physical data referents - the real-world entities and spaces to which data corresponds - and examine the relationship between referents and the visual and physical representations of their data. We differentiate situated representations, which display data in proximity to data referents, and embedded representations, which display data so that it spatially coincides with data referents. Drawing on examples from visualization, ubiquitous computing, and art, we explore the role of spatial indirection, scale, and interaction for embedded representations. We also examine the tradeoffs between non-situated, situated, and embedded data displays, including both visualizations and physicalizations. Based on our observations, we identify a variety of design challenges for embedded data representation, and suggest opportunities for future research and applications.

Makers participate in remixing culture by drawing inspiration from, combining, and adapting designs for physical objects. To examine how makers remix each others' designs on a community scale, we analyzed metadata from over 175,000 digital designs from Thingiverse, the largest online design community for digital fabrication. Remixed designs on Thingiverse are predominantly generated designs from Customizer a built-in web app for adjusting parametric designs. However, we find that these designs do not elicit subsequent user activity and the authors who generate them tend not to contribute additional content to Thingiverse. Outside of Customizer, influential sources of remixing include complex assemblies and design primitives, as well as non-physical resources posing as physical designs. Building on our findings, we discuss ways in which online maker communities could become more than just design repositories and better support collaborative remixing.

We explore interactive relief shearing, a set of non-intrusive, direct manipulation interactions that expose depth and shape information in terrain maps using ephemeral animations. Reading and interpreting topography and relief on terrain maps is an important aspect of map use, but extracting depth information from 2D maps is notoriously difficult. Modern mapping software attempts to alleviate this limitation by presenting digital terrain using 3D views. However, 3D views introduce occlusion, complicate distance estimations, and typically require more complex interactions. In contrast, our approach reveals depth information via shearing animations on 2D maps, and can be paired with existing interactions such as pan and zoom. We examine explicit, integrated, and hybrid interactions for triggering relief shearing and present a version that uses device tilt to control depth effects. Our evaluation shows that these interactive techniques improve depth perception when compared to standard 2D and perspective views.

We present an exploration and a design space that characterize the usage and placement of word-scale visualizations within text documents. Word-scale visualizations are a more general version of sparklines--small, word-sized data graphics that allow meta-information to be visually presented in-line with document text. In accordance with Edward Tufte's definition, sparklines are traditionally placed directly before or after words in the text. We describe alternative placements that permit a wider range of word-scale graphics and more flexible integration with text layouts. These alternative placements include positioning visualizations between lines, within additional vertical and horizontal space in the document, and as interactive overlays on top of the text. Each strategy changes the dimensions of the space available to display the visualizations, as well as the degree to which the text must be adjusted or reflowed to accommodate them. We provide an illustrated design space of placement options for word-scale visualizations and identify six important variables that control the placement of the graphics and the level of disruption of the source text. We also contribute a quantitative analysis that highlights the effect of different placements on readability and text disruption. Finally, we use this analysis to propose guidelines to support the design and placement of word-scale visualizations.

We present a first investigation into hybrid-image visualization for data analysis in large-scale viewing environments. Hybrid-image visualizations blend two different visual representations into a single static view, such that each representation can be perceived at a different viewing distance. Our work is motivated by data analysis scenarios that incorporate one or more displays with sufficiently large size and resolution to be comfortably viewed by different people from various distances. Hybrid-image visualizations can be used, in particular, to enhance overview tasks from a distance and detail-in-context tasks when standing close to the display. By using a perception-based blending approach, hybrid-image visualizations make two full-screen visualizations accessible without tracking viewers in front of a display. We contribute a design space, discuss the perceptual rationale for our work, provide examples, and introduce a set of techniques and tools to aid the design of hybrid-image visualizations.

We present a system that lets analysts use paid crowd workers to explore data sets and helps analysts interactively examine and build upon workers' insights. We take advantage of the fact that, for many types of data, independent crowd workers can readily perform basic analysis tasks like examining views and generating explanations for trends and patterns. However, workers operating in parallel can often generate redundant explanations. Moreover, because workers have different competencies and domain knowledge, some responses are likely to be more plausible than others. To efficiently utilize the crowd's work, analysts must be able to quickly identify and consolidate redundant responses and determine which explanations are the most plausible. In this paper, we demonstrate several crowd-assisted techniques to help analysts make better use of crowdsourced explanations: (1) We explore crowd-assisted strategies that utilize multiple workers to detect redundant explanations. We introduce color clustering with representative selection--a strategy in which multiple workers cluster explanations and we automatically select the most-representative result--and show that it generates clusterings that are as good as those produced by experts. (2) We capture explanation provenance by introducing highlighting tasks and capturing workers' browsing behavior via an embedded web browser, and refine that provenance information via source-review tasks. We expose this information in an explanation-management interface that allows analysts to interactively filter and sort responses, select the most plausible explanations, and decide which to explore further.

Web-based social data analysis tools that rely on public discussion to produce hypotheses or explanations of the patterns and trends in data, rarely yield high-quality results in practice. Crowdsourcing offers an alternative approach in which an analyst pays workers to generate such explanations. Yet, asking workers with varying skills, backgrounds and motivations to simply "Explain why a chart is interesting" can result in irrelevant, unclear or speculative explanations of variable quality. To address these problems, we contribute seven strategies for improving the quality and diversity of worker-generated explanations. Our experiments show that using (S1) feature-oriented prompts, providing (S2) good examples, and including (S3) reference gathering, (S4) chart reading, and (S5) annotation subtasks increases the quality of responses by 28% for US workers and 196% for non-US workers. Feature-oriented prompts improve explanation quality by 69% to 236% depending on the prompt. We also show that (S6) pre-annotating charts can focus workers' attention on relevant details, and demonstrate that (S7) generating explanations iteratively increases explanation diversity without increasing worker attrition. We used our techniques to generate 910 explanations for 16 datasets, and found that 63% were of high quality. These results demonstrate that paid crowd workers can reliably generate diverse, high-quality explanations that support the analysis of specific datasets.

Collaborative visual analysis tools can enhance sensemaking by facilitating social interpretation and parallelization of effort. These systems enable distributed exploration and evidence gathering, allowing many users to pool their effort as they discuss and analyze the data. We explore how adding lightweight tag and link structure to comments can aid this analysis process. We present CommentSpace, a collaborative system in which analysts comment on visualizations and websites and then use tags and links to organize findings and identify others'" contributions. In a pair of studies comparing CommentSpace to a system without support for tags and links, we find that a small, fixed vocabulary of tags (question, hypothesis, to-do) and links (evidence-for, evidence-against) helps analysts more consistently and accurately classify evidence and establish common ground. We also find that managing and incentivizing participation is important for analysts to progress from exploratory analysis to deeper analytical tasks. Finally, we demonstrate that tags and links can help teams complete evidence gathering and synthesis tasks and that organizing comments using tags and links improves analytic results.

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.

Selection is a fundamental task in interactive applications, typically performed by clicking or lassoing items of interest. However, users may require more nuanced forms of selection. Selecting regions or attributes may be more important than selecting individual items. Selections may be over dynamic items and selections might be more easily created by relaxing simpler selections (e.g., "select all items like this one"). Creating such selections requires that interfaces model the declarative structure of the selection, not just individually selected items. We present direct manipulation techniques that couple declarative selection queries with a query relaxation engine that enables users to interactively generalize their selections. We apply our selection techniques in both information visualization and graphics editing applications, enabling generalized selection over both static and dynamic interface objects. A controlled study finds that users create more accurate selection queries when using our generalization techniques.

This paper presents scented widgets, graphical user interface controls enhanced with embedded visualizations that facilitate navigation in information spaces. We describe design guidelines for adding visual cues to common user interface widgets such as radio buttons, sliders, and combo boxes and contribute a general software framework for applying scented widgets within applications with minimal modifications to existing source code. We provide a number of example applications and describe a controlled experiment which finds that users exploring unfamiliar data make up to twice as many unique discoveries using widgets imbued with social navigation data. However, these differences equalize as familiarity with the data increases.