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In fall-risk assessment, clinical experts have to provide accurate assessment of high-risk individuals using vast amounts of collected data. In this article, we propose an interactive visualization approach for clinical experts to improve their interpretation of assessment scores and facilitate the decision-making process. Fall-risk assessment data on a total of 356 community-dwelling elders were collated. The Short-Form Berg Balance Scale and 3-Meter Timed Up and Go test were used to screen elderly people with high fall risks. A series of interactive visualization techniques were conducted. After grouping by the literature and a statistical 5% outlier method, some disputed elderly people were examined through interactive visualization. Finally, receiver operating characteristic analysis was conducted using previous fall experience (faller or non-faller) and the three methods. Receiver operating characteristic analysis revealed that the area under the curve was the highest (0.87, 95% confidence interval: 0.80–0.94) for the interactive visualization process compared to the other methods (literature, 0.81 (95% confidence interval: 0.71–0.90); statistical 5% outlier, 0.80 (95% confidence interval: 0.70–0.90)). Through the interactive visualization approach, the clinical experts were able to determine the screening results and their relationship with the decision boundary more rapidly and accurately, demonstrating that this approach is useful for risk assessment in the medical domain. [ABSTRACT FROM AUTHOR]

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science , where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on. [ABSTRACT FROM AUTHOR]

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This article presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords. [ABSTRACT FROM AUTHOR]

In coastal regions, water, wind, gravitation, vegetation, and human activity continuously alter landscape surfaces. Visualizations are important for understanding coastal landscape evolution and its driving processes. Visualizing change in highly dynamic coastal terrain poses a formidable challenge; the combination of natural and anthropogenic forces leads to cycles of retreat and recovery and complex morphology of landforms. In recent years, repeated high-resolution laser terrain scans have generated a time series of point cloud data that represent landscapes at snapshots in time, including the impacts of major storms. In this article, we build on existing approaches for visualizing spatial–temporal data to create a collection of perceptual visualizations to support coastal terrain evolution analysis. We extract terrain features and track their migration; we derive temporal summary maps and heat graphs that quantify the pattern of elevation change and sediment redistribution and use the space–time cube concept to create visualizations of terrain evolution. The space–time cube approach allows us to represent shoreline evolution as an isosurface extracted from a voxel model created by stacking time series of digital elevation models. We illustrate our approach on a series of Light Detection and Ranging surveys of sandy North Carolina barrier islands. Our results reveal terrain changes of shoreline and dune ridge migration, dune breaches and overwash, the formation of new dune ridges, and the construction and destruction of homes, changes which are due to erosion and accretion, hurricanes, and human activities. These events are all visualized within their geographic and temporal contexts. [ABSTRACT FROM PUBLISHER]

Abstract: In this study, we investigated the horizontal visual search ability and pattern of horizontal visual search in a large space performed by patients with unilateral spatial neglect (USN). Subjects included nine patients with right hemisphere damage caused by cerebrovascular disease showing left USN, nine patients with right hemisphere damage but no USN, and six healthy individuals with no history of brain damage who were age-matched to the groups with brain right hemisphere damage. The number of visual search tasks accomplished was recorded in the first experiment. Neck rotation angle was continuously measured during the task and quantitative data of the measurements were collected. There was a strong correlation between the number of visual search tasks accomplished and the total Behavioral Inattention Test Conventional Subtest (BITC) score in subjects with right hemisphere damage. In both USN and control groups, the head position during the visual search task showed a balanced bell-shaped distribution from the central point on the field to the left and right sides. Our results indicate that compensatory strategies, including cervical rotation, may improve visual search capability and achieve balance on the neglected side. [Copyright &y& Elsevier]

High-dimensional data sets containing hundreds of variables are difficult to explore, as traditional visualization methods often are unable to represent such data effectively. This is commonly addressed by employing dimensionality reduction prior to visualization. Numerous dimensionality reduction methods are available. However, few reduction approaches take the importance of several structures into account and few provide an overview of structures existing in the full high-dimensional data set. For exploratory analysis, as well as for many other tasks, several structures may be of interest. Exploration of the full high-dimensional data set without reduction may also be desirable. This paper presents flexible methods for exploratory analysis and interactive dimensionality reduction. Automated methods are employed to analyse the variables, using a range of quality metrics, providing one or more measures of ‘interestingness’ for individual variables. Through ranking, a single value of interestingness is obtained, based on several quality metrics, that is usable as a threshold for the most interesting variables. An interactive environment is presented in which the user is provided with many possibilities to explore and gain understanding of the high-dimensional data set. Guided by this, the analyst can explore the high-dimensional data set and interactively select a subset of the potentially most interesting variables, employing various methods for dimensionality reduction. The system is demonstrated through a use-case analysing data from a DNA sequence-based study of bacterial populations. [ABSTRACT FROM AUTHOR]

Abstract: Because previous studies of attention in autism spectrum disorders (ASD) have been restricted in age range examined, little is known about how these processes develop over the course of childhood. In this study we examined cross-sectional age effects on patterns of visual attention to social and nonsocial information in 43 typically developing children and 51 children with ASD ranging in age from 2 to 18. Results indicated a sharp increase in visual exploration with age and a decrease in perseverative and detail-focused attention for both groups of children. However, increased age was associated with greater increases in visual exploration for typically developing children than for those children with ASD. The developmental differences were most pronounced for attention to certain nonsocial stimuli as children with ASD demonstrated a disproportionate attentional bias for these stimuli from very early in life. Disproportionate visual attention to certain nonsocial objects relative to social stimuli in ASD spanned from early to late childhood, and thus may represent both an early and a persistent characteristic of the disorder. [Copyright &y& Elsevier]

In recent years, the necessity of the incorporation of semantic information into three-dimensional city models (3DCMs) has become a consensus in 3D GIS field. In order to provide practical support for visual applications concerned with semantics, this paper firstly presents an extended semantic model based on the CityGML standard, which was worked out for the general storage and representation of semantics. In this model, concepts like Room, Corridor and Stair are all derived from concept Space which corresponds to the concept of Room in CityGML. This extension will benefit the indoor structure representation. Geological feature is also supported by the model for the underground analysis. Next, for the promotion of semantic modeling by this model, a semi-automatic process of semantic enrichment is implemented in a data integration tool. It provides an adaptive way to link semantics with pure geometry. Finally, two typical cases of visual exploration are illustrated to prove the model's practicability in a national 3D GIS project of China. One is indoor routing, which adopts this model to extract the geometric path and thus enrich traditional semantic-enhanced navigation routine; another case is unified profiler, where semantics are intergrated in order to fill up the cross section correctly and ensure the topological and semantic consistency. [ABSTRACT FROM AUTHOR]