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Kristin Potter

Bio: Kristin Potter is an academic researcher from University of Utah. The author has contributed to research in topics: Visualization & Data visualization. The author has an hindex of 15, co-authored 31 publications receiving 1280 citations. Previous affiliations of Kristin Potter include Scientific Computing and Imaging Institute & University of Oregon.

Papers
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Proceedings ArticleDOI
06 Dec 2009
TL;DR: This article argues that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis, and demonstrates the framework using driving problems from climate modeling and meteorology and discusses generalizations to other fields.
Abstract: Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. In this article, we present Ensemble-Vis, a framework consisting of a collection of overview and statistical displays linked through a high level of interactivity. Ensemble-Vis allows scientists to gain key scientific insight into the distribution of simulation results as well as the uncertainty associated with the scientific data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate our framework using driving problems from climate modeling and meteorology and discuss generalizations to other fields.

233 citations

Journal ArticleDOI
01 Jun 2011
TL;DR: This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG) to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources.
Abstract: Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.

214 citations

Book ChapterDOI
01 Jan 2014
TL;DR: This chapter outlines sources and models of uncertainty, gives an overview of the state-of-the-art, provides general guidelines, outline small exemplary applications, and discusses open problems in uncertainty visualization.
Abstract: The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.

189 citations

Book ChapterDOI
01 Jan 2012
TL;DR: This paper identifies frequently occurring types of uncertainty and connects those uncertainty representations to ones commonly used in visualization, and looks at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionalities of the uncertainty.
Abstract: Quantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community.

174 citations

Journal ArticleDOI
09 Jun 2010
TL;DR: This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty, as well as an extension of the summary plot to two dimensional distributions.
Abstract: The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use-case of these new plots is presented, demonstrating their ability to present high-level overviews as well as detailed insight into the salient features of the underlying data distribution.

144 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: The book describes clearly and intuitively the differences between exploratory and confirmatory factor analysis, and discusses how to construct, validate, and assess the goodness of fit of a measurement model in SEM by confirmatory factors analysis.
Abstract: Examples are discussed to show the differences among discriminant analysis, logistic regression, and multiple regression. Chapter 6, “Multivariate Analysis of Variance,” presents advantages of multivariate analysis of variance (MANOVA) over univariate analysis of variance (ANOVA), discusses assumptions of MANOVA, and assesses validations of MANOVA assumptions and model estimation. The authors also discuss post hoc tests of MANOVA and multivariate analysis of covariance. Chapter 7, “Conjoint Analysis,” explains what conjoint analysis does and how it is different from other multivariate techniques. Guidelines of selecting attributes, models, and methods of data collection are presented. Chapter 8, “Cluster Analysis,” studies objectives, roles, and limitations of cluster analysis. Two basic concepts: similarity and distance are discussed. The authors also discuss details of five most popular hierarchical algorithms (singlelinkage, complete-linkage, average-linkage, centroid method, Ward’s method) and three nonhierarchical algorithms (the sequential threshold method, the parallel threshold method, and the optimizing procedure). Profiles of clusters and guidelines for cluster validation are studied as well. Chapter 9, “Multidimensional Scaling and Correspondence Analysis,” introduces two interdependence techniques to display the relationships in the data. The book describes clearly and intuitively the differences between the two techniques and how these two techniques are performed. Chapters 10–12 cover topics in SEM. Chapter 10, “Structural Equation Modeling: An Introduction,” introduces SEM and related concepts such as exogenous, endogenous constructs, and so on, points out the differences between SEM and other multivariate techniques, overviews the decision process of SEM. Chapter 11, “Confirmatory Factor Analysis,” explains the differences between exploratory and confirmatory factor analysis, discusses how to construct, validate, and assess the goodness of fit of a measurement model in SEM by confirmatory factor analysis. Chapter 12, “Testing a Structural Model,” presents some methods of SEM in examining the relationships between latent constructs. The book is an excellent book for people in management and marketing. For the Technometrics audience, this book does not have much flavor of physical, chemical, and engineering sciences. For example, partial least squares, a very popular method in Chemometrics, is discussed but not as detailed as other techniques in the book. Furthermore, due to the amount of materials covered in the book, it might be inappropriate for someone who is new to multivariate analysis.

497 citations

Journal ArticleDOI
TL;DR: This paper investigates physiological decision processes while participants undertook a choice task designed to elicit preferences for a product, and provides a way to quantify the importance of different cracker features that contribute to the product design based on mutual information.
Abstract: Application of neuroscience methods to analyze and understand human behavior related to markets and marketing exchange has recently gained research attention. The basic aim is to guide design and presentation of products to optimize them to be as compatible as possible with consumer preferences. This paper investigates physiological decision processes while participants undertook a choice task designed to elicit preferences for a product. The task required participants to choose their preferred crackers described by shape (square, triangle, round), flavor (wheat, dark rye, plain) and topping (salt, poppy, no topping). The two main research objectives were (1) to observe and evaluate the cortical activity of the different brain regions and the interdependencies among the Electroencephalogram (EEG) signals from these regions; and (2) unlike most research in this area that has focused mainly on liking/disliking certain products, we provide a way to quantify the importance of different cracker features that contribute to the product design based on mutual information. We used the commercial Emotiv EPOC wireless EEG headset with 14 channels to collect EEG signals from participants. We also used a Tobii-Studio eye tracker system to relate the EEG data to the specific choice options (crackers). Subjects were shown 57 choice sets; each choice set described three choice options (crackers). The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0-4Hz), Theta (3-7Hz), Alpha (8-12Hz), Beta (13-30Hz), and Gamma (30-40Hz). There was a clear phase synchronization between the left and right frontal and occipital regions indicating interhemispheric communications during the chosen task for the 18 participants. Results also indicated that there was a clear and significant change (p<0.01) in the EEG power spectral activities taking a place mainly in the frontal (delta, alpha and beta across F3, F4, FC5 and FC6), temporal (alpha, beta, gamma across T7), and occipital (theta, alpha, and beta across O1) regions when participants indicated their preferences for their preferred crackers. Additionally, our mutual information analysis indicated that the various cracker flavors and toppings of the crackers were more important factors affecting the buying decision than the shapes of the crackers.

377 citations

Journal ArticleDOI
TL;DR: This survey studies existing methods for visualization and interactive visual analysis of multifaceted scientific data and suggests new solutions for multirun and multimodel data as well as techniques that support a multitude of facets.
Abstract: Visualization and visual analysis play important roles in exploring, analyzing, and presenting scientific data. In many disciplines, data and model scenarios are becoming multifaceted: data are often spatiotemporal and multivariate; they stem from different data sources (multimodal data), from multiple simulation runs (multirun/ensemble data), or from multiphysics simulations of interacting phenomena (multimodel data resulting from coupled simulation models). Also, data can be of different dimensionality or structured on various types of grids that need to be related or fused in the visualization. This heterogeneity of data characteristics presents new opportunities as well as technical challenges for visualization research. Visualization and interaction techniques are thus often combined with computational analysis. In this survey, we study existing methods for visualization and interactive visual analysis of multifaceted scientific data. Based on a thorough literature review, a categorization of approaches is proposed. We cover a wide range of fields and discuss to which degree the different challenges are matched with existing solutions for visualization and visual analysis. This leads to conclusions with respect to promising research directions, for instance, to pursue new solutions for multirun and multimodel data as well as techniques that support a multitude of facets.

359 citations