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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.

Modeling and Perception of Multi-Factor Cluster in Scatterplot  Using Merge Tree (Under writing  for  CHI 2019)

 

You Can’t Publish Replication Studies (and How to Anyways)

Reproducibility has been increasingly encouraged by communities of science in order to validate experimental conclusions, and replication studies represent a significant opportunity to vision scientists wishing contribute new perceptual models, methods, or insights to the visualization community. Unfortunately, the notion of replication of previous studies does not lend itself to how we communicate research findings. Simple put, studies that re-conduct and confirm earlier results do not hold any novelty, a key element to the modern research publication system. Nevertheless, savvy researchers have discovered ways to produce replication studies by embedding them into other sufficiently novel studies. In this position paper, we define three methods---re-evaluation, expansion, and specialization---for embedding a replication study into a novel published work. Within this context, we provide a non-exhaustive case study on replications of Cleveland and McGill's seminal work on graphical perception. As it turns out, numerous replication studies have been carried out based on that work, which have both confirmed prior findings and shined new light on our understanding of human perception. Finally, we discuss how publishing a true replication study should be avoided, while providing suggestions for how vision scientists and others can still use replication studies as a vehicle to producing visualization research publications.

You Can’t Publish Replication Studies (and How to Anyways)

Ghulam J Quadri, Paul Rosen In Proceedings of VIS'19: IEEE Conference on Visualization. Vancouver, BC, Canada, Oct 20-25. (PDF)

 

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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.

State of  the Art Report: Visualization Journey Through Perception. (Under Writing)

 

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.

  • Visual Analytic Design for Charactering Air-Sampling Performance and Operation. Video  PDF
  • Multi-Spectral Satellite Image Analysis for Feature Identification and Change Detection. Video  PDF
  • Data Aggregation and Visualization Technique for Traffic Sensor Data. Video  PDF