Evaluation of the Visibility of Vessel Movement Features in Trajectory Visualizations

There are many visualizations that show the trajectory of a moving object to obtain insights in its behavior. In this user study, we test the performance of three of these visualizations with respect to three movement features that occur in vessel behavior. Our goal is to compare the recently presented vessel density by Willems et al. [ [WvdWvW09] ] with well‐known trajectory visualizations such as an animation of moving dots and the space‐time cube. We test these visualizations with common maritime analysis tasks by investigating the ability of users to find stopping objects, fast moving objects, and estimate the busiest routes in vessel trajectories. We test the robustness of the visualizations towards scalability and the influence of complex trajectories using small‐scale synthetic data sets. The performance is measured in terms of correctness and response time. The user test shows that each visualization type excels for correctness for a specific movement feature. Vessel density performs best for finding stopping objects, but does not perform significantly less than the remaining visualizations for the other features. Therefore, vessel density is a nice extension in the toolkit for analyzing trajectories of moving objects, in particular for vessel movements, since stops can be visualized better, and the performance for comparing lanes and finding fast movers is at a similar level as established trajectory visualizations.     

The Perception of Correlation in Scatterplots

We present a rigorous way to evaluate the visual perception of correlation in scatterplots, based on classical psychophysical methods originally developed for simple properties such as brightness. Although scatterplots are graphically complex, the quantity they convey is relatively simple. As such, it may be possible to assess the perception of correlation in a similar way. Scatterplots were each of 5.0° extent, containing 100 points with a bivariate normal distribution. Means were 0.5 of the range of the points, and standard deviations 0.2 of this range. Precision was determined via an adaptive algorithm to find the just noticeable differences (jnds) in correlation, i.e., the difference between two side‐by‐side scatterplots that could be discriminated 75% of the time. Accuracy was measured by direct estimation, using reference scatterplots with fixed upper and lower values, with a test scatterplot adjusted so that its correlation appeared to be halfway between these. This process was recursively applied to yield several further estimates. Results of the discrimination tests show jnd(r) = k (1/b – r), where r is the Pearson correlation, and parameters 0 < k, b < 1. Integration yields a subjective estimate of correlation g(r) = ln(1 – br) / ln(1 – b). The values of b found via discrimination closely match those found via direct estimation. As such, it appears that the perception of correlation in a scatterplot is completely described by two related performance curves, specified by two easily‐measured parameters.     

Centrality Based Visualization of Small World Graphs

Current graph drawing algorithms enable the creation of two dimensional node‐link diagrams of huge graphs. However, for graphs with low diameter (of which “small world” graphs are a subset) these techniques begin to break down visually even when the graph has only a few hundred nodes. Typical algorithms produce images where nodes clump together in the center of the screen, making it hard to discern structure and follow paths. This paper describes a solution to this problem, which uses a global edge metric to determine a subset of edges that capture the graph's intrinsic clustering structure. This structure is then used to create an embedding of the graph, after which the remaining edges are added back in. We demonstrate applications of this technique to a number of real world examples.     

The Undistort Lens

Detail‐in‐context lens techniques can be useful for exploring visualizations of data spaces that are too large or have too much detail to fit in regular displays. For example, by bending the space in the right way we can bring together details from two separate areas for easy comparison while roughly keeping the context that situates each area within the global space. While these techniques can be powerful tools, they also introduce distortions that need to be understood, and often the tools have to be disabled in order to have access to the undistorted data. We introduce the undistort lens, a complement to existing distortion‐based techniques that provides a local and separate presentation of the original geometry without affecting any distortion‐based lenses currently used in the presentation. The undistort lens is designed to allow interactive access to the underlying undistorted data within the context of the distorted space, and to enable a better understanding of the distortions. The paper describes the implementation of a generic back‐mapping mechanism that enables the implementation of undistort lenses for arbitrary distortion based techniques, including those presented in the lens literature. We also provide a series of use‐case scenarios that demonstrate the situations in which the technique can complement existing lenses.     

Sketching and Composing Widgets for 3D Manipulation

We present an interface for 3D object manipulation in which standard transformation tools are replaced with transient 3D widgets invoked by sketching context‐dependent strokes. The widgets are automatically aligned to axes and planes determined by the user's stroke. Sketched pivot‐points further expand the interaction vocabulary. Using gestural commands, these basic elements can be assembled into dynamic, user‐constructed 3D transformation systems. We supplement precise widget interaction with techniques for coarse object positioning and snapping. Our approach, which is implemented within a broader sketch‐based modeling system, also integrates an underlying “widget history” to enable the fluid transfer of widgets between objects. An evaluation indicates that users familiar with 3D manipulation concepts can be taught how to efficiently use our system in under an hour.     

Visual Inspection of Multivariate Graphs

Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes?” and, “how and from where can I reach specific types of nodes?” To validate our approach we contrast it with current practice. We also provide several examples where our method was used to study transition graphs that model real‐world systems.     

Extended Excentric Labeling

The paper presents an extension to the Excentric Labeling, a labeling technique to dynamically show labels around a movable lens. Each labels refers to one object within the lens and is connected to it through a line. The original implementation has several known limitations and potential improvements that we address in this work, like: high density areas, uneven density distributions, and summary statistics. We describe the implemented extensions and present a think-aloud user study. The study shows that users can naturally understand and easily operate the majority of the implemented function but label scrolling, which requires additional research. From the study we also gained unanticipated requirements and interesting directions for further research.     

Results of a User Study on 2D Hurricane Visualization

We present the results from a user study looking at the ability of observers to mentally integrate wind direction and magnitude over a vector field. The data set chosen for the study is an MM5 (PSU/NCAR Mesoscale Model) simulation of Hurricane Lili over the Gulf of Mexico as it approaches the southeastern United States. Nine observers participated in the study. This study investigates the effect of layering on the observer's ability to determine the magnitude and direction of a vector field. We found a tendency for observers to underestimate the magnitude of the vectors and a counter‐clockwise bias when determining the average direction of a vector field. We completed an additional study with two observers to try to uncover the source of the counter‐clockwise bias. These results have direct implications to atmospheric scientists, but may also be able to be applied to other fields that use 2D vector fields.     

Navigation and Exploration of Interconnected Pathways

Visualizing pathways, i. e. models of cellular functional networks, is a challenging task in computer assisted biomedicine. Pathways are represented as large collections of interwoven graphs, with complex structures present in both the individual graphs and their interconnections. This situation requires the development of novel visualization techniques to allow efficient visual exploration. We present the Caleydo framework, which incorporates a number of approaches to handle such pathways. Navigation in the network of pathways is facilitated by a hierarchical approach which dynamically selects a working set of individual pathways for closer inspection. These pathways are interactively rendered together with visual interconnections in a 2.5D view using graphics hardware acceleration. The layout of individual graphs is not computed automatically, but taken from the KEGG and BioCarta databases, which use layouts that life scientists are familiar with. Therefore they encode essential meta‐information. While the KEGG and BioCarta pathways use a pre‐defined layout, interactions such as linking+brushing, neighborhood search or detail on demand are still fully interactive in Caleydo. We have evaluated Caleydo with pathologists working on the determination of unknown gene functions. Informal experiences confirm that Caleydo is useful in both generating and validating such hypotheses. Even though the presented techniques are applied to medical pathways, the proposed way of interaction is not limited to cellular processes and therefore has the potential to open new possibilities in other fields of application.     

Tablorer – An Interactive Tree Visualization System for Tablet PCs

A variety of mobile devices are available today, but there is no dominant tree visualization system in the devices. This paper proposes Tablorer, a novel interactive tree visualization system for medium‐sized mobile devices, especially for tablet PCs. The system shows the hierarchical information with a compact way using an expandable table format. For efficient navigation, the system provides an integrated view of context and focus information. The experimental results show that Tablorer can reduce the search time by about 22%.     

Supporting Exploratory Analysis with the Select & Slice Table

In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select & Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select & Slice table during exploratory analysis of multivariate data.     

Visualization Techniques for Schedule Comparison

Project schedules are effectively represented by Gantt charts, but comparing multiple versions of a schedule is difficult. To compare versions with current methods, users must search and navigate through multiple large documents, making it difficult to identify differences. We present two novel visualization techniques to support the comparison of Gantt charts. First, we encode two Gantt charts in one view by overlapping them to show differences. Second, we designed an interactive visual technique, the 'TbarView', that allows users to compare multiple schedules within one single view. We evaluated the overlap and TbarView techniques via a user study. The study results showed that our design provided a quick overview of the variances among two or more schedules, and the techniques also improved efficiency by minimizing view switching. Our visual techniques for schedule comparison could be combined with other resource analysis tools to help project teams identify and resolve errors and problems in project schedules.     

Interactive Visual Workspaces with Dynamic Foveal Areas and Adaptive Composite Interfaces

This paper presents novel techniques and metaphors for on-demand visual workspaces in everyday office environments, providing space-efficient, flexible and highly interactive graphical user interfaces using projected displays. For increased resolution, contents personalization and interactive visualization, the users can augment the large-scale projections with dynamic high-resolution foveal enhancements using a pocket light metaphor. To further optimize the presentation at a given resolution, the design of the displays can be modified interactively, and like a jigsaw puzzle, the layout can be customized using an adaptive compositing approach which supports free-form focus-and-context rendering. With a unified intensity-based tracking approach, we allow for natural multi-touch interaction with the information space through bare hands, pointers and pens on arbitrary surfaces.     

Collaborative Brushing and Linking for Co-located Visual Analytics of Document Collections

Many real-world analysis tasks can benefit from the combined efforts of a group of people. Past research has shown that to design visualizations for collaborative visual analytics tasks, we need to support both individual as well as joint analysis activities. We present Cambiera, a tabletop visual analytics tool that supports individual and collaborative information foraging activities in large text document collections. We define collaborative brushing and linking as an awareness mechanism that enables analysts to follow their own hypotheses during collaborative sessions while still remaining aware of the group's activities. With Cambiera, users are able to collaboratively search through documents, maintaining awareness of each others' work and building on each others' findings.     

Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings

In addition to the choice of visual encodings, the effectiveness of a data visualization may vary with the analytical task being performed and the distribution of data values. To better assess these effects and create refined rankings of visual encodings, we conduct an experiment measuring subject performance across task types (e.g., comparing individual versus aggregate values) and data distributions (e.g., with varied cardinalities and entropies). We compare performance across 12 encoding specifications of trivariate data involving 1 categorical and 2 quantitative fields, including the use of x, y, color, size, and spatial subdivision (i.e., faceting). Our results extend existing models of encoding effectiveness and suggest improved approaches for automated design. For example, we find that colored scatterplots (with positionally‐coded quantities and color‐coded categories) perform well for comparing individual points, but perform poorly for summary tasks as the number of categories increases.     

Visualising Errors in Animal Pedigree Genotype Data

Genetic analysis of a breeding animal population involves determining the inheritance pattern of genotypes for multiple genetic markers across the individuals in the population pedigree structure. However, experimental pedigree genotype data invariably contains errors in both the pedigree structure and in the associated individual genotypes, introducing inconsistencies into the dataset, rendering them useless for further analysis. The resolution of these errors requires consideration of genotype inheritance patterns in the context of the pedigree structure. Existing pedigree visualisations are typically more suited to human pedigrees and are less suitable for large complex animal pedigrees which may exhibit cross generational inbreeding. Similarly, table‐based viewers of genotype marker data can highlight where errors become apparent but lack the functionality and interactive visual feedback to allow users to locate the origin of errors within the pedigree. In this paper, we detail a design study steered by biologists who work with pedigree data, and describe successive iterations through approaches and prototypes for viewing genotyping errors in the context of a displayed pedigree. We describe how each approach performs with real pedigree genotype data and why eventually we deemed them unsuitable. Finally, a novel prototype visualisation for pedigrees, which we term the ‘sandwich view’, is detailed and we demonstrate how the approach effectively communicates errors in the pedigree context, supporting the biologist in the error identification task.     

Evaluation of Cluster Identification Performance for Different PCP Variants

Parallel coordinate plots (PCPs) are a well‐known visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their information throughput. Most modifications pertain to the use of color and opacity, smooth curves, or the use of animation. Although many of these seem valid improvements, only few user studies have been performed to investigate this, especially with respect to cluster identification. We performed a user study to evaluate cluster identification performance – with respect to response time and correctness – of nine PCP variations, including standard PCPs. To generate the variations, we focused on covering existing techniques as well as possible while keeping testing feasible. This was done by adapting and merging techniques, which led to the following novel variations. The first is an effective way of embedding scatter plots into PCPs. The second is a technique for highlighting fuzzy clusters based on neighborhood density. The third is a spline‐based drawing technique to reduce ambiguity. The last is a pair of animation schemes for PCP rotation. We present an overview of the tested PCP variations and the results of our study. The most important result is that a fair number of the seemingly valid improvements, with the exception of scatter plots embedded into PCPs, do not result in significant performance gains.     

Constructing and Evaluating Visualisation Task Classifications: Process and Considerations

Categorising tasks is a common pursuit in the visualisation research community, with a wide variety of taxonomies, typologies, design spaces, and frameworks having been developed over the last three decades. While these classifications are universally purported to be useful in both the design and evaluation processes and in guiding future research, remarkably little attention has been paid to how these frameworks have—and can be—constructed and evaluated. In this paper we review the task classification literature and report on current practices in construction and evaluation. We consider the stages of task classification construction and identify the associated threats to validity arising at each stage and in response to the different methods employed. We provide guidance on suitable validation approaches in order to mitigate these threats. We also consider the appropriateness of evaluation strategies according to the different aspects of the classification which they evaluate. In so doing, we seek to provide guidance for developers of classifications in determining appropriate construction and evaluation strategies when developing a classification, and also for those selecting between competing classifications for use in the design and evaluation processes.