Chris Weaver

Building Highly-Coordinated Visualizations in Improvise

Improvise is a fully-implemented system in which users build and browse multiview visualizations interactively using a simple shared-object coordination mechanism coupled with a flexible, expression-based visual abstraction language. By coupling visual abstraction with coordination, users gain precise control over how navigation and selection in the visualization affects the appearance of data in individual views. As a result, it is practical to build visualizations with more views and richer coordination in Improvise than in other visualization systems. Building and browsing activities are integrated in a single, live user interface that lets users alter visualizations quickly and incrementally during data exploration

Research directions in data wrangling: visuatizations and transformations for usable and credible data

In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time diagnosing data quality issues and manipulating data into a usable form. This process of ‘data wrangling’ often constitutes the most tedious and time-consuming aspect of analysis. Though data cleaning and integration arelongstanding issues in the database community, relatively little research has explored how interactive visualization can advance the state of the art. In this article, we review the challenges and opportunities associated with addressing data quality issues. We argue that analysts might more effectively wrangle data through new interactive systems that integrate data verification, transformation, and visualization. We identify a number of outstanding research questions, including how appropriate visual encodings can facilitate apprehension of missing data, discrepant values, and uncertainty; how interactive visualizations might facilitate data transform specification; and how recorded provenance and social interaction might enable wider reuse, verification, and modification of data transformations.

Creation and Collaboration: Engaging New Audiences for Information Visualization

In recent years we have seen information visualization technology move from an advanced research topic to mainstream adoption in both commercial and personal use. This move is in part due to many businesses recognizing the need for more effective tools for extracting knowledge from the data warehouses they are gathering. Increased mainstream interest is also a result of more exposure to advanced interfaces in contemporary online media. The adoption of information visualization technologies by lay users --- as opposed to the traditional information visualization audience of scientists and analysts --- has important implications for visualization research, design and development. Since we cannot expect each of these lay users to design their own visualizations, we have to provide them tools that make it easy to create and deploy visualizations of their datasets.

Visual Analysis of Conflicting Opinions

Understanding the nature and dynamics of conflicting opinions is a profound and challenging issue. In this paper we address several aspects of the issue through a study of more than 3,000 Amazon customer reviews of the controversial bestseller The Da Vinci Code, including 1,738 positive and 918 negative reviews. The study is motivated by critical questions such as: what are the differences between positive and negative reviews? What is the origin of a particular opinion? How do these opinions change over time? To what extent can differentiating features be identified from unstructured text? How accurately can these features predict the category of a review? We first analyze terminology variations in these reviews in terms of syntactic, semantic, and statistic associations identified by TermWatch and use term variation patterns to depict underlying topics. We then select the most predictive terms based on log likelihood tests and demonstrate that this small set of terms classifies over 70% of the conflicting reviews correctly. This feature selection process reduces the dimensionality of the feature space from more than 20,000 dimensions to a couple of hundreds. We utilize automatically generated decision trees to facilitate the understanding of conflicting opinions in terms of these highly predictive terms. This study also uses a number of visualization and modeling tools to identify not only what positive and negative reviews have in common, but also they differ and evolve over time

Cross-Filtered Views for Multidimensional Visual Analysis

Analysis of multidimensional data often requires careful examination of relationships across dimensions. Coordinated multiple view approaches have become commonplace in visual analysis tools because they directly support expression of complex multidimensional queries using simple interactions. However, generating such tools remains difficult because of the need to map domain-specific data structures and semantics into the idiosyncratic combinations of interdependent data and visual abstractions needed to reveal particular patterns and distributions in cross-dimensional relationships. This paper describes: 1) a method for interactively expressing sequences of multidimensional set queries by cross-filtering data values across pairs of views and 2) design strategies for constructing coordinated multiple view interfaces for cross-filtered visual analysis of multidimensional data sets. Using examples of cross-filtered visualizations of data from several different domains, we describe how cross-filtering can be modularized and reused across designs, flexibly customized with respect to data types across multiple dimensions, and incorporated into more wide-ranging multiple view designs. We also identify several important limitations of the approach. The demonstrated analytic utility of these examples suggests that cross-filtering is a suitable design pattern for instantiation in a wide variety of visual analysis tools.

Multidimensional visual analysis using cross-filtered views

Analysis of multidimensional data often requires careful examination of relationships across dimensions. Coordinated multiple view approaches have become commonplace in visual analysis tools because they directly support expression of complex multidimensional queries using simple interactions. However, generating such tools remains difficult because of the need to map domain-specific data structures and semantics into the idiosyncratic combinations of interdependent data and visual abstractions needed to reveal particular patterns and distributions in cross-dimensional relationships. This paper describes: (1) a method for interactively expressing sequences of multidimensional set queries by cross-filtering data values across pairs of views, and (2) design strategies for constructing coordinated multiple view interfaces for cross-filtered visual analysis of multidimensional data sets. Using examples of cross-filtered visualizations of data from several different domains, we describe how cross-filtering can be modularized and reused across designs, flexibly customized with respect to data types across multiple dimensions, and incorporated into more wide-ranging multiple view designs. The demonstrated analytic utility of these examples suggest that cross-filtering is a suitable design pattern for instantiation in a wide variety of visual analysis tools.

Star Plots: How Shape Characteristics Influence Classification Tasks

Our research addresses the question of how to design interfaces for spatial analysis such that they support cognitive processes. In this paper we specifically target the question of map symbol design for the analysis of multivariate data, which is a common problem in cartography and related fields. We focus on star plots and the largely unaddressed question of how to assign variables to rays in a star plot and which consequences specific shapes have—as the result of data characteristics and the assignment of variables to rays—on interpretation and classification. We conducted an experiment with two conditions that were designed to shed light on the question: Does the shape of a star plot influence the interpretation (meaning) of the data it represents in a classification task? While previous research on multivariate point symbols has addressed this question for Chernoff faces, for example, few connections have been made to the shape of a star plot and its potential influence on meaning. We found that certain salient shape characteristics induced by variations along the horizontal and vertical axis increase the classification speed. However, we also found that salient shapes, such as has one spike, introduce a perceptual similarity that overrides the assumed similarities in the meaning of the represented data.

Visual Analysis of Historic Hotel Visitation Patterns

Understanding the space and time characteristics of human interaction in complex social networks is a critical component of visual tools for intelligence analysis, consumer behavior analysis, and human geography. Visual identification and comparison of patterns of recurring events is an essential feature of such tools. In this paper, we describe a tool for exploring hotel visitation patterns in and around Rebersburg, Pennsylvania from 1898-1900. The tool uses a wrapping spreadsheet technique, called reruns, to display cyclic patterns of geographic events in multiple overlapping natural and artificial calendars. Implemented as an improvise visualization, the tool is in active development through a iterative process of data collection, hypothesis, design, discovery, and evaluation in close collaboration with historical geographers. Several discoveries have inspired ongoing data collection and plans to expand exploration to include historic weather records and railroad schedules. Distributed online evaluations of usability and usefulness have resulted in numerous feature and design recommendations

Visualizing coordination in situ

Exploratory visualization environments allow users to build and browse coordinated multiview visualizations interactively. As the number of views and amount of coordination increases, conceptualizing coordination structure becomes more and more important for successful data exploration. Integrated metavisualization is exploratory visualization of coordination and other interactive structure directly inside a visualizations own user interface. This paper presents a model of integrated metavisualization, describes the problem of capturing dynamic interface structure as visualizable data, and outlines three general approaches to integration. Metavisualization has been implemented in improvise, using views, lenses, and embedding to reveal the dynamic structure of its own highly coordinated visualizations.

Watch this: A taxonomy for dynamic data visualization

Visualizations embody design choices about data access, data transformation, visual representation, and interaction. To interpret a static visualization, a person must identify the correspondences between the visual representation and the underlying data. These correspondences become moving targets when a visualization is dynamic. Dynamics may be introduced in a visualization at any point in the analysis and visualization process. For example, the data itself may be streaming, shifting subsets may be selected, visual representations may be animated, and interaction may modify presentation. In this paper, we focus on the impact of dynamic data. We present a taxonomy and conceptual framework for understanding how data changes influence the interpretability of visual representations. Visualization techniques are organized into categories at various levels of abstraction. The salient characteristics of each category and task suitability are discussed through examples from the scientific literature and popular practices. Examining the implications of dynamically updating visualizations warrants attention because it directly impacts the interpretability (and thus utility) of visualizations. The taxonomy presented provides a reference point for further exploration of dynamic data visualization techniques.