VIMTEX: A Visualization Interface for Multivariate,Time‐Varying,Geological Data Exploration

Observing interactions among chemical species and microorganisms in the earth's sub‐surface is a common task in the field of geology. Bioremediation experiments constitute one such class of interactions which focus on getting rid of pollutants through processes such as carbon sequestration. The main goal of scientists’ observations is to analyze the dynamics of the chemical reactions and understand how they collectively affect the carbon content of the soil. In our work, we extract the high‐level goals of geologists and propose a visual analytics solution which helps scientists in deriving insights about multivariate, temporal behavior of these chemical species. Specifically, our key contributions are the following: i) characterization of the domain‐specific goals and their translation to exploratory data analysis tasks, ii) developing an analytical abstraction in the form of perceptually motivated screen‐space metrics for bridging the gap between the tasks and the visualization, and iii) realization of the tasks and metrics in the form of VIMTEX, which is a set of coordinated multiple views for letting scientists observe multivariate, temporal relationships in the data. We provide several examples and case studies along with expert feedback for demonstrating the efficacy of our solution.     

The shaping of information by visual metaphors

The nature of an information visualization can be considered to lie in the visual metaphors it uses to structure information. The process of understanding a visualization therefore involves an interaction between these external visual metaphors and the user's internal knowledge representations. To investigate this claim, we conducted an experiment to test the effects of visual metaphor and verbal metaphor on the understanding of tree visualizations. Participants answered simple data comprehension questions while viewing either a treemap or a node-link diagram. Questions were worded to reflect a verbal metaphor that was either compatible or incompatible with the visualization a participant was using. The results suggest that the visual metaphor indeed affects how a user derives information from a visualization. Additionally, we found that the degree to which a user is affected by the metaphor is strongly correlated with the user's ability to answer task questions correctly. These findings are a first step towards illuminating how visual metaphors shape user understanding, and have significant implications for the evaluation, application, and theory of visualization.     

Finding a Clear Path: Structuring Strategies for Visualization Sequences

Little is known about how people structure sets of visualizations to support sequential viewing. We contribute findings from several studies examining visualization sequencing and reception. In our first study, people made decisions between various possible structures as they ordered a set of related visualizations (consisting of either bar charts or thematic maps) into what they considered the clearest sequence for showing the data. We find that most people structure visualization sequences hierarchically: they create high level groupings based on shared data properties like time period, measure, level of aggregation, and spatial region, then order the views within these groupings. We also observe a tendency for certain types of similarities between views, like a common spatial region or aggregation level, to be seen as more appropriate categories for organizing views in a sequence than others, like a common time period or measure. In a second study, we find that viewers’ perceptions of the quality and intention of different sequences are largely consistent with the perceptions of the users who created them. The understanding of sequence preferences and perceptions that emerges from our studies has implications for the development of visualization authoring tools and sequence recommendations for guided analysis.     

Measuring Privacy and Utility in Privacy‐Preserving Visualization

In previous work, we proposed a technique for preserving the privacy of quasi‐identifiers in sensitive data when visualized using parallel coordinates. This paper builds on that work by introducing a number of metrics that can be used to assess both the level of privacy and the amount of utility that can be gained from the resulting visualizations. We also generalize our approach beyond parallel coordinates to scatter plots and other visualization techniques. Privacy preservation generally entails a trade‐off between privacy and utility: the more the data are protected, the less useful the visualization. Using a visually‐oriented approach, we can provide a higher amount of utility than directly applying data anonymization techniques used in data mining. To demonstrate this, we use the visual uncertainty framework for systematically defining metrics based on cluster artifacts and information theoretic principles. In a case study, we demonstrate the effectiveness of our technique as compared to standard data‐based clustering in the context of privacy‐preserving visualization.     

Visualization Criticism

Criticism is a vital part of the practice of design, architecture, and art. It's extremely difficult to establish rules for making good art or designing a new object. Criticizing a work means applying the standards to identify weaknesses and suggest improvements. Critiquing can be a useful tool for teaching, developing better techniques, and deeper thinking about visualization.     

Visualization viewpoints

User studies offer a scientifically sound method to measure a visualization's performance. Reasons abound for pursuing user studies, particularly when evaluating the strengths and weaknesses of different visualization techniques. A good starting point in any study is the scientific or visual design question to be examined. This drives the process of experimental design. A poorly designed experiment will yield results of only limited value. Although a comprehensive discussion of experimental design is beyond the scope of the article, we offer suggestions and lessons learned. We also describe how we designed experiments to answer important questions from our own research.     

Aging Induces Tissue-Specific Changes in Cholesterol Metabolism in Rat Brain and Liver

Disturbance of cholesterol homeostasis in the brain is coupled to age-related brain dysfunction. In the present work, we studied the relationship between aging and cholesterol metabolism in two brain regions, the cortex and hippocampus, as well as in the sera and liver of 6-, 12-, 18- and 24-month-old male Wistar rats. Using gas chromatography-mass spectrometry, we undertook a comparative analysis of the concentrations of cholesterol, its precursors and metabolites, as well as dietary-derived phytosterols. During aging, the concentrations of the three cholesterol precursors examined (lanosterol, lathosterol and desmosterol) were unchanged in the cortex, except for desmosterol which decreased (44 %) in 18-month-old rats. In the hippocampus, aging was associated with a significant reduction in lanosterol and lathosterol concentrations at 24 months (28 and 25 %, respectively), as well as by a significant decrease of desmosterol concentration at 18 and 24 months (36 and 51 %, respectively). In contrast, in the liver we detected age-induced increases in lanosterol and lathosterol concentrations, and no change in desmosterol concentration. The amounts of these sterols were lower than in the brain regions. In the cortex and hippocampus, desmosterol was the predominant cholesterol precursor. In the liver, lathosterol was the most abundant precursor. This ratio remained stable during aging. The most striking effect of aging observed in our study was a significant decrease in desmosterol concentration in the hippocampus which could reflect age-related reduced synaptic plasticity, thus representing one of the detrimental effects of advanced age.     

Parallel sets: interactive exploration and visual analysis of categorical data

Categorical data dimensions appear in many real-world data sets, but few visualization methods exist that properly deal with them. Parallel Sets are a new method for the visualization and interactive exploration of categorical data that shows data frequencies instead of the individual data points. The method is based on the axis layout of parallel coordinates, with boxes representing the categories and parallelograms between the axes showing the relations between categories. In addition to the visual representation, we designed a rich set of interactions. Parallel Sets allow the user to interactively remap the data to new categorizations and, thus, to consider more data dimensions during exploration and analysis than usually possible. At the same time, a metalevel, semantic representation of the data is built. Common procedures, like building the cross product of two or more dimensions, can be performed automatically, thus complementing the interactive visualization. We demonstrate Parallel Sets by analyzing a large CRM data set, as well as investigating housing data from two US states.     

Laws of attraction: from perceptual forces to conceptual similarity

Many of the pressing questions in information visualization deal with how exactly a user reads a collection of visual marks as information about relationships between entities. Previous research has suggested that people see parts of a visualization as objects, and may metaphorically interpret apparent physical relationships between these objects as suggestive of data relationships. We explored this hypothesis in detail in a series of user experiments. Inspired by the concept of implied dynamics in psychology, we first studied whether perceived gravity acting on a mark in a scatterplot can lead to errors in a participant's recall of the mark's position. The results of this study suggested that such position errors exist, but may be more strongly influenced by attraction between marks. We hypothesized that such apparent attraction may be influenced by elements used to suggest relationship between objects, such as connecting lines, grouping elements, and visual similarity. We further studied what visual elements are most likely to cause this attraction effect, and whether the elements that best predicted attraction errors were also those which suggested conceptual relationships most strongly. Our findings show a correlation between attraction errors and intuitions about relatedness, pointing towards a possible mechanism by which the perception of visual marks becomes an interpretation of data relationships.     

Pargnostics: screen-space metrics for parallel coordinates

Interactive visualization requires the translation of data into a screen space of limited resolution. While currently ignored by most visualization models, this translation entails a loss of information and the introduction of a number of artifacts that can be useful, (e.g., aggregation, structures) or distracting (e.g., over-plotting, clutter) for the analysis. This phenomenon is observed in parallel coordinates, where overlapping lines between adjacent axes form distinct patterns, representing the relation between variables they connect. However, even for a small number of dimensions, the challenge is to effectively convey the relationships for all combinations of dimensions. The size of the dataset and a large number of dimensions only add to the complexity of this problem. To address these issues, we propose Pargnostics, parallel coordinates diagnostics, a model based on screen-space metrics that quantify the different visual structures. Pargnostics metrics are calculated for pairs of axes and take into account the resolution of the display as well as potential axis inversions. Metrics include the number of line crossings, crossing angles, convergence, overplotting, etc. To construct a visualization view, the user can pick from a ranked display showing pairs of coordinate axes and the structures between them, or examine all possible combinations of axes at once in a matrix display. Picking the best axes layout is an NP-complete problem in general, but we provide a way of automatically optimizing the display according to the user’s preferences based on our metrics and model.