Single view vs. multiple views scatterplots

Among all the available visualization tools, the scatterplot has been deeply analyzed through the years and many researchers investigated how to improve this tool to face new challenges. The scatterplot visualization diagram is considered one of the most functional among the variety of data visual representations, due to its relative simplicity compared to other multivariable visualization techniques. Even so, one of the most significant and unsolved challenge in data visualization consists in effectively displaying datasets with many attributes or dimensions, such as multidimensional or multivariate ones. The focus of this research is to compare the single view and the multiple views visualization paradigms for displaying multivariable dataset using scatterplots. A multivariable scatterplot has been developed as a web application to provide the single view tool, whereas for the multiple views visualization, the ScatterDice web app has been slightly modified and adopted as a traditional, yet interactive, scatterplot matrix. Finally, a taxonomy of tasks for visualization tools has been chosen to define the use case and the tests to compare the two paradigms

Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions

Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs) are also experiencing information overload while interacting with students‟ data, because of its multidimensionality. This constraints decision making processes, and therefore requires a domain-specific InfoVis conceptual design framework which will birth the domain‟s InfoVis tool. This study therefore aims to design HEI Students‟ data-focused InfoVis (HSDI) conceptual design framework which addresses the content delivery techniques and the systematic processes in actualizing the domain specific InfoVis. The study involved four phases: 1) a users‟ study to investigate, elicit and prioritize the students‟ data-related explicit knowledge preferences of HEI domain policy. The corresponding students‟ data dimensions are then categorised, 2) exploratory study through content analysis of InfoVis design literatures, and subsequent mapping with findings from the users‟ study, to propose the appropriate visualization, interaction and distortion techniques for delivering the domain‟s explicit knowledge preferences, 3) conceptual development of the design framework which integrates the techniques‟ model with its design process–as identified from adaptation of software engineering and InfoVis design models, 4) evaluation of the proposed framework through expert review, prototyping, heuristics evaluation, and users‟ experience evaluation. For an InfoVis that will appropriately present and represent the domain explicit knowledge preferences, support the students‟ data multidimensionality and the decision making processes, the study found that: 1) mouse-on, mouse-on-click, mouse on-drag, drop down menu, push button, check boxes, and dynamics cursor hinting are the appropriate interaction techniques, 2) zooming, overview with details, scrolling, and exploration are the appropriate distortion techniques, and 3) line chart, scatter plot, map view, bar chart and pie chart are the appropriate visualization techniques. The theoretical support to the proposed framework suggests that dictates of preattentive processing theory, cognitive-fit theory, and normative and descriptive theories must be followed for InfoVis to aid perception, cognition and decision making respectively. This study contributes to the area of InfoVis, data-driven decision making process, and HEI students‟ data usage process

Enhancing scatter plots using ellipsoid pixel placement and shading

Scatter plots are one of the most powerful techniques for visualizing relationships between two continuous variables. Using scatter plots, it is easy to find how one variable is affected by another. However, scatter plots may have a high degree of overlap, and therefore, important local patterns and trends may be hidden. Generalized scatter plots provide overlap-distortion optimized views, but the point repositioning algorithm used for avoiding overlap does not take the local structure of the data into account. In this paper, we enhance scatter plots using ellipsoid point placement and a cluster shading algorithm. In particular, we use local correlations to compute the rotation and aspect ratios of the ellipsoids used for the point placement, and add shading to the point clusters to visually encode the points' original locations. The effect of the shading and lighting can be controlled by the user

Applications of technology and large data in statistics education and statistical graphics

This dissertation is a composite of research preformed in the fields of statistics education and statistical graphics. The three body chapters stand as the pillars of this work; tied together by the common theme of overcoming challenges and grasping opportunities that are posed by emerging technologies and prodigious data sources. In Chapter 1 a review of the literature is conducted to lay the foundation upon which the work of the main body chapters is built. Chapter 2 is an educational experiment comparing the learning outcomes from simulation-based and traditional statistical inference curricula. Chapter 3 studies the development of a shiny application to connect students to large data. Chapter 4 contributes to research on binned scatterplots as a graphical tool for visualizing large data. Each body chapter in this dissertation is intended be submitted for publication individually; therefore, more detailed abstracts may be found at the beginning of each chapter