Visualizing Temporal Topic Embeddings with a Compass.

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

Investigating Professional Analyst Strategies in Immersive Space to Think.

Existing research on sensemaking in immersive analytics systems primarily focuses on understanding how users complete analysis within these systems with quantitative and qualitative datasets. However, these user studies mainly concentrate on understanding analysis styles and methodologies from a predominantly novice user study population. While this approach provides excellent initial insights into what users may do within IA systems, it fails to address how professionals may utilize an immersive analytic system for analysis tasks. In our work, we build upon an existing immersive analytics concept - "Immersive Space to Think" to understand how professional user populations differ from novice users in immersive analytic system usage. We conducted a user study with 11 professional intelligence analysts who completed three analysis sessions each. Using our results from this study, we provide deep analysis into how professional users complete sensemaking within immersive analytic systems, compare our findings to previously published findings with a novice user population, and provide insights into how to develop better IA systems to support the professional analyst's strategies within these systems.

Systematic investigation of inadequate food access at a large southeastern land grant university.

Over the past decade, the impact of low food security on student well-being and academic performance has become a growing concern at institutions of higher education across the U.S. This mixed methods study adds to the growing body of evidence on the association between student socio-demographic and economic characteristics and food security. An online survey covering food access, student well-being, and housing security was sent to 35,337 undergraduate and graduate students at a large southeastern land grant university. A total of 2,116 complete responses were received; a 6% response rate. The survey responses also included 176 written statements by students. The survey found that 16% of both undergraduate and graduate students had low or very low food security, as defined by a modified USDA food security measure. The socio-demographic and economic characteristics that were linked to a higher likelihood of low food security included: having a GPA of less than 3.0, having a disability, being an international student, being a first-generation student, being a transfer student, going into debt to pay for food, being a Black or African American student, having poor mental health, having uncertain living arrangements, and having no medical insurance. Recommendations for enhancing student access to food, housing, and mental health services are discussed.

Multiple Monitors or Single Canvas? Evaluating Window Management and Layout Strategies on Virtual Displays.

Virtual displays enabled through head-worn augmented reality have unique characteristics that can yield extensive amounts of screen space. Existing research has shown that increasing the space on a computer screen can enhance usability. Since virtual displays offer the unique ability to present content without rigid physical space constraints, they provide various new design possibilities. Therefore, we must understand the trade-offs of layout choices when structuring that space. We propose a single Canvas approach that eliminates boundaries from traditional multi-monitor approaches and instead places windows in one large, unified space. Our user study compared this approach against a multi-monitor setup, and we considered both purely virtual systems and hybrid systems that included a physical monitor. We looked into usability factors such as performance, accuracy, and overall window management. Results show that Canvas displays can cause users to compact window layouts more than multiple monitors with snapping behavior, even though such optimizations may not lead to longer window management times. We did not find conclusive evidence of either setup providing a better user experience. Multi-Monitor displays offer quick window management with snapping and a structured layout through subdivisions. However, Canvas displays allow for more control in placement and size, lowering the amount of space used and, thus, head rotation. Multi-Monitor benefits were more prominent in the hybrid configuration, while the Canvas display was more beneficial in the purely virtual configuration.

This is the Table I Want! Interactive Data Transformation on Desktop and in Virtual Reality.

Data transformation is an essential step in data science. While experts primarily use programming to transform their data, there is an increasing need to support non-programmers with user interface-based tools. With the rapid development in interaction techniques and computing environments, we report our empirical findings about the effects of interaction techniques and environments on performing data transformation tasks. Specifically, we studied the potential benefits of direct interaction and virtual reality (VR) for data transformation. We compared gesture interaction versus a standard WIMP user interface, each on the desktop and in VR. With the tested data and tasks, we found time performance was similar between desktop and VR. Meanwhile, VR demonstrates preliminary evidence to better support provenance and sense-making throughout the data transformation process. Our exploration of performing data transformation in VR also provides initial affirmation for enabling an iterative and fully immersive data science workflow.

Exploring the Evolution of Sensemaking Strategies in Immersive Space to Think.

Existing research on immersive analytics to support the sensemaking process focuses on single-session sensemaking tasks. However, in the wild, sensemaking can take days or months to complete. In order to understand the full benefits of immersive analytic systems, we need to understand how immersive analytic systems provide flexibility for the dynamic nature of the sensemaking process. In our work, we build upon an existing immersive analytic system - Immersive Space to Think, to evaluate how immersive analytic systems can support sensemaking tasks over time. We conducted a user study with eight participants with three separate analysis sessions each. We found significant differences between analysis strategies between sessions one, two, and three, which suggest that immersive space to think can benefit analysts during multiple stages in the sensemaking process.

An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration.

How do analysts think about grouping and spatial operations? This overarching research question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.

Immersive Analytics: Theory and Research Agenda.

Advances in a variety of computing fields, including "big data," machine learning, visualization, and augmented/mixed/virtual reality, have combined to give rise to the emerging field of immersive analytics, which investigates how these new technologies support analysis and decision making. Thus far, we feel that immersive analytics research has been somewhat ad hoc, possibly owing to the fact that there is not yet an organizing framework for immersive analytics research. In this paper, we address this lack by proposing a definition for immersive analytics and identifying some general research areas and specific research questions that will be important for the development of this field. We also present three case studies that, while all being examples of what we would consider immersive analytics, present different challenges, and opportunities. These serve to demonstrate the breadth of immersive analytics and illustrate how the framework proposed in this paper applies to practical research.

Smooth, Efficient, and Interruptible Zooming and Panning.

This paper introduces a novel technique for smooth and efficient zooming and panning based on dynamical systems in hyperbolic space. Unlike the technique of van Wijk and Nuij, the animations produced by our technique are smooth at the endpoints and when interrupted by a change of target. To analyze the results of our technique, we introduce world/screen diagrams, a novel technique for visualizing zooming and panning animations.

The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs.

Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences.

SIRIUS: Dual, Symmetric, Interactive Dimension Reductions.

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.

Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics.

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.

The PiTSTOP study: a feasibility cluster randomized trial of delirium prevention in care homes for older people.

BACKGROUND AND OBJECTIVES: delirium is a distressing but potentially preventable condition common in older people in long-term care. It is associated with increased morbidity, mortality, functional decline, hospitalization and significant healthcare costs. Multicomponent interventions, addressing delirium risk factors, have been shown to reduce delirium by one-third in hospitals. It is not known whether this approach is also effective in long-term care. In previous work, we designed a bespoke delirium prevention intervention, called 'Stop Delirium!' In preparation for a definitive trial of Stop Delirium, we sought to address key aspects of trial design for the particular circumstances of care homes. DESIGN: a cluster randomized feasibility study with an embedded process evaluation. SETTING AND PARTICIPANTS: residents of 14 care homes for older people in one metropolitan district in the UK. INTERVENTION: Stop Delirium!: a 16-month-enhanced educational package to support care home staff to address key delirium risk factors. Control homes received usual care. MEASUREMENTS: we collected data to determine the following: recruitment and attrition; delirium rates and variability between homes; feasibility of measuring delirium, resource use, quality of life, hospital admissions and falls; and intervention implementation and adherence. RESULTS: two-thirds (215) of eligible care home residents were recruited. One-month delirium prevalence was 4.0% in intervention and 7.1% in control homes. Proposed outcome measurements were feasible, although our approach appeared to underestimate delirium. Health economic evaluation was feasible using routinely collected data. CONCLUSION: a definitive trial of delirium prevention in long-term care is needed but will require some further design modifications and pilot work.

Comprehensive geriatric assessment of a mental health service user with safeguarding needs.

This is the final article in a short series that presents case study examples of the use of comprehensive geriatric assessment (CGA) in different clinical settings. CGA is a holistic model that is used to determine frail older people's medical and mental health status, as well as any functional, social and environmental issues that might affect their care. When undertaken by nurses, it can enable individualised planning for health, safety and wellbeing. This article explores the case of an older woman living in the community who was receiving support from a number of health and social care services and who had significant safeguarding needs. It highlights the complexity of caring for patients with physical and mental health conditions. CGA can link these conditions and needs together to allow a better understanding of their effects on the patient. The risks of significant transitions in care are also highlighted, along with recommendations for the provision of multidisciplinary care in community settings.

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, "in-between", to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.

Pilot trial of Stop Delirium! (PiTStop)--a complex intervention to prevent delirium in care homes for older people: study protocol for a cluster randomised controlled trial.

BACKGROUND: Delirium (or acute confusion) is a serious illness common in older people, in which a person's thinking and perceptions may be affected. Reducing delirium is important because of the considerable distress it causes, and the poor outcomes associated with it, such as increased admissions to hospital, falls, mortality and costs to the National Health Service (NHS). Preventing delirium is possible using multicomponent interventions; successful interventions in hospitals have reduced it by one-third. However, there is little research to guide practice in care homes, where it is common because of the clustering of known risk factors (older age, frailty, and dementia). In previous work we developed a multicomponent intervention to prevent delirium in care homes, called Stop Delirium! The intervention was based upon evidence from the research literature relating to the prevention of delirium and on strategies to change professional practice. Before starting a large costly trial of Stop Delirium!, this pilot study will test and help improve the design and feasibility of the trial protocol. METHODS/DESIGN: We plan to conduct a cluster randomised pilot trial in 14 care homes (independent residential and nursing). Following recruitment of residents (over 60 years, consenting or with consultee agreement, able to communicate in English, and not in palliative care) participating homes will be randomised, stratified by size of home and proportion of residents with dementia. Stop Delirium! will be delivered to intervention homes over 16 months, with controls receiving usual care. The primary outcome measure will be the presence of delirium on any day during a one-month post-intervention period.We will collect data to determine 1) recruitment and attrition rates, 2) feasibility of various outcomes measurements, and 3) feasibility of capturing health resource use (resident diaries and by examining health records). We will estimate the between-cluster variation for the primary outcome, delirium occurrence. DISCUSSION: This pilot study will refine methods for the definitive trial. The lessons learnt will also contribute to implementing National Institute for Health and Clinical Excellence (NICE) delirium guidelines, which recommend multicomponent interventions for delirium prevention. TRIAL REGISTRATION: ISRCTN27972532.

A Five-Level Design Framework for Bicluster Visualizations.

Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.

A Survey of Software Frameworks for Cluster-Based Large High-Resolution Displays.

Large high-resolution displays (LHRD) enable visualization of extremely large-scale data sets with high resolution, large physical size, scalable rendering performance, advanced interaction methods, and collaboration. Despite the advantages, applications for LHRD can be developed only by a select group of researchers and programmers, since its software implementation requires design and development paradigms different from typical desktop environments. It is critical for developers to understand and take advantage of appropriate software tools and methods for developing their LHRD applications. In this paper, we present a survey of the state-of-the-art software frameworks and applications for cluster-based LHRD, highlighting a three-aspect taxonomy. This survey can aid LHRD application and framework developers in choosing more suitable development techniques and software environments for new LHRD applications, and guide LHRD researchers to open needs in LHRD software frameworks.

Semantics of directly manipulating spatializations.

When high-dimensional data is visualized in a 2D plane by using parametric projection algorithms, users may wish to manipulate the layout of the data points to better reflect their domain knowledge or to explore alternative structures. However, few users are well-versed in the algorithms behind the visualizations, making parameter tweaking more of a guessing game than a series of decisive interactions. Translating user interactions into algorithmic input is a key component of Visual to Parametric Interaction (V2PI) [13]. Instead of adjusting parameters, users directly move data points on the screen, which then updates the underlying statistical model. However, we have found that some data points that are not moved by the user are just as important in the interactions as the data points that are moved. Users frequently move some data points with respect to some other 'unmoved' data points that they consider as spatially contextual. However, in current V2PI interactions, these points are not explicitly identified when directly manipulating the moved points. We design a richer set of interactions that makes this context more explicit, and a new algorithm and sophisticated weighting scheme that incorporates the importance of these unmoved data points into V2PI.

The impact of physical navigation on spatial organization for sensemaking.

Spatial organization has been proposed as a compelling approach to externalizing the sensemaking process. However, there are two ways in which space can be provided to the user: by creating a physical workspace that the user can interact with directly, such as can be provided by a large, high-resolution display, or through the use of a virtual workspace that the user navigates using virtual navigation techniques such as zoom and pan. In this study we explicitly examined the use of spatial sensemaking techniques within these two environments. The results demonstrate that these two approaches to providing sensemaking space are not equivalent, and that the greater embodiment afforded by the physical workspace changes how the space is perceived and used, leading to increased externalization of the sensemaking process.