Modeling and Perception of Cluster in Scatterplot
In the representation of information, design choices matter for effectively communicating information. Human perception plays an important role in what we infer from a visualization, and a better understanding of perception helps design the visualization in both a quantitative and a qualitative manner. Surveying the last decade of information visualization, many perception-based experiments have been carried out to understand the effectiveness of visualization with respect to viewers. Understanding a viewer’s ability to rapidly and accurately understand the clusters in a scatterplot is theme of this work. We present a rigorous empirical study on visual perception of clustering in scatterplots modeling around a topological data structure known as the merge tree. We tested our cluster identification model in scatterplots on a variety of multi-factor variables to understand the main and interaction effects.
Visualization Journey through Perception
Knowledge of human perception has long been incorporating into the design of visualizations to enhance their quality and effectiveness. The last decade has shown a particular increase in perception-based visualization research studies. In this paper, we provide a systematic and comprehensive report on experimentation, theory, and study of perception as it relates to visual- ization, in order to help readers understand and apply the principals of perception to their visualization designs. The taxonomy is divided into 3 parts: (1) Type of Perceptual Study—we have categorized the studies into 7 different classes based upon the perceptual objective of the study. (2) Type of Task—we categorize surveyed papers into 6 broad types of visual analytics tasks that are targeted by their research. (3) Visualization Method—we summarize the findings of perceptual studies on 9 commonly studied visualization types. Implementing the insights from the research summarized in this survey can lead to better visual- ization design and more effective utilization of graphical encodings. We concluded our report with potential pitfalls, areas of improvement, and open problems requiring additional research.
The Visual Analytics Science and Technology (VAST) Challenge - IEEE VIS 2017
The Visual Analytics Science and Technology (VAST) is an annual contest with the goal of advancing the field of visual analytics through competition. The VAST Challenge is designed to help researchers understand how their software would be used in a novel analytic task and determine if their data transformations, visualizations, and interactions would be beneficial for particular analytic tasks. In the Summer of 2017, We as a team of three (Sulav Malla , Anwesh Tuladhar) under guidance of Dr.Paul Rosen participated in three mini-challenges (MC1, MC2 and MC3) and submitted our work to IEEE VAST challenge Community. Our MC3 submission was awarded with Honorable Mention for Good Facilitation of Single Image Analysis.