Pathways for Theoretical Advances in Visualization IEEE VIS 2016 Panel

Total Page:16

File Type:pdf, Size:1020Kb

Pathways for Theoretical Advances in Visualization IEEE VIS 2016 Panel Pathways for Theoretical Advances in Visualization IEEE VIS 2016 Panel Min Chen∗ Georges Grinstein† Chris R. Johnson‡ University of Oxford, UK University of Massachusetts Lowell, USA University of Utah, USA Jessie Kennedy§ Tamara Munzner¶ Melanie Toryk Edinburgh Napier University, UK University of British Columbia, Canada Tableau Software, USA ABSTRACT • Taxonomies and Ontologies: In scientific and scholarly dis- ciplines, a collection of concepts are commonly organized There is little doubt that having a theoretic foundation will benefit into a taxonomy or ontology. In the former, concepts are the field of visualization, including its main subfields: information known as taxa, which are typically arranged hierarchically visualization, scientific visualization and visual analytics, as well as using a tree structure. In the latter, concepts, often in con- many domain-specific applications such as software visualization, junction with their instances, attributes, and other entities, are biomedical visualization, and so on. Since there has been a substan- organized into a schematic network, where edges represent tial amount of work on taxonomies and conceptual models in the various relations and rules. visualization literature, and some recent work on theoretic frame- works, such a theoretic foundation is not merely an airy-fairy am- • Principles and Guidelines: A principle is a law or rule that bition. In this panel, the panellists will focus on the question “How has to be followed, and is usually expressed in a qualitative can we build a theoretic foundation for visualization collectively as description. A guideline describes a process or a set of actions a community?” In particular, the panellists will envision the path- that may lead to a desired outcome, or actions to be avoided ways in four different aspects of a theoretic foundation, namely (i) in order to prevent an undesired outcome. The former usually taxonomies and ontologies, (ii) principles and guidelines, (iii) con- implies a high degree of generality and certainty of the causal- ceptual models and theoretic frameworks, and (iv) quantitative laws ity concerned, while the latter suggests that a causal relation and theoretic systems. may be subject to specific conditions. Keywords. Theory of visualization, information visualization, sci- • Conceptual Models and Theoretic Frameworks: The terms entific visualization, visual analytics, theoretical foundation, con- of frameworks and models have broad interpretations. Here cept, taxonomy, ontology, principle, guideline, model, measure- we consider that a conceptual model is an abstract represen- ment, framework, quantitative law, theoretic system. tation of a real-world phenomenon, process, or system, fea- turing different functional components and their interactions. 1 INTRODUCTION AND MOTIVATION A theoretic framework provides a collection of measurements and basic operators and functions for working with these mea- More than a decade ago, Johnson proposed “Theory of Visual- surements. The former provides a description of complex ization” as one of the top research problems in visualization [7]. causal relations in real world in a tentative manner, while the Since then there have been several focused events, including two latter provides a basis for evaluating different models quanti- panels in 2010 and 2011 respectively [18, 1], two workshops in tatively. 2010 (https://eagereyes.org/blog/2010/infovis-theory-workshop), and 2016 (https://sites.google.com/site/drminchen/home/events) • Quantitative Laws and Theoretic Systems: A quantitative respectively. However, it is still a common notion that “Theory of law describes a causal relation of concepts using a set of mea- Visualization” is a problem for a few individual researchers, and its surements and a computable function, which is confirmed un- solution, perhaps in the forms of some theorems or laws, may be der a theoretic framework. Under a theoretic framework, a too distant from practice to be useful. conceptual model can be transformed to a theoretic system through axioms (postulated quantitative principles) and theo- During the recent ATI Symposium on Theoretical Foundation of rems (confirmed quantitative laws). Unconfirmed guidelines Visual Analytics, the discussion on the need for building a theoret- are thus conjectures, and contradictory guidelines are para- ical foundation attracted a wide range of opinions, from “Visual- doxes. ization should not be physics-envy” to “It is irresponsible for aca- demics not to try.” Gradually, the attendees converged to a common understanding that a theoretical foundation consisted of several as- In the field of visualization, it is estimated that there are currently pects, and every visualization researcher should be able to make some thirty papers on taxonomies and ontologies; several hundreds direct contributions to the theoretical foundation of visualization. of principles and guidelines recommended by various books, re- In particular, the event identified four major aspects (cf. [8]): search papers, and online media; more than ten conceptual mod- els; a few theoretic frameworks; and a few quantitative laws that ∗e-mail: [email protected] are mathematically confirmed guidelines. There is little doubt that †e-mail: [email protected] much more effort will be required to address the problem of “The- ‡e-mail: [email protected] ory of Visualization” [7]. §e-mail: [email protected] Using the two questions (as shown in Figure 1) posed in the ATI ¶e-mail: [email protected] Symposium as an example, one may ask (i) what taxonomy would ke-mail: [email protected] encompass all concepts relevant to the two questions; (ii) what guideline could be followed by users in answering such a question in practice; (iii) what model could be used to represent and simulate a real world problem involving statistics, visualization, computa- tion, and interaction; or (iv) what quantitative measurement would £11.00 £10.50 Share Price of Company X £10.00 £9.50 classifications (taxonomies) arise over time due to differing opin- £9.00 When do we pause £8.50 £8.00 £7.50 ions of the importance of differentiating characteristics used in cre- £7.00 statistics and start £6.50 £6.00 £5.50 ating the concepts (taxa). This is usually as a result of new infor- £5.00 visualization? £4.50 2002 2003 2004 mation becoming available, often through technological advances. down sampling This can result in the same organism being classified according to different taxonomic opinions and subsequently have several alter- 100 101 102 103 104 105 106 107 108 109 native names, leading to miscommunication. Newer classifications number of data points in a time series are usually improvements on previous ones, but sometimes the ex- istence of alternative classifications reflects the fact that there is dis- agreement as to how to interpret the data on which the classification When do we pause is based. algorithmic computing Taxonomies play an increasing role in understanding in the field and start interaction? of visualization and provide a useful means for bringing order to the adding complexity range of existing visualization systems, tools, and techniques and are frequently adopted in literature surveys to categorise existing 100 101 102 103 104 105 106 107 108 109 work. There have been many taxonomies proposed in the visual- number of lines (or other units of complexity measurement) ization field focusing on different criteria, including taxonomies by data type/model, task (visual/user), visual representation/encoding, Figure 1: Two questions posed at the beginning of the ATI Sympo- interaction mechanism, user characteristics and combinations of sium on Theoretical Foundation of Visual Analytics. these often specialized by domain. The taxonomies have been de- veloped for aiding the design, selection or evaluation of visualiza- tion tools and techniques. I will present an overview of taxonomies enable us to estimate an optimal point of “when” in the two ques- in visualization and argue that we should consider taxonomy as an tions. These questions clearly suggest that a theoretical foundation investigative science, with taxonomic classifications representing needs more than a few theorems or laws, and the problem of “The- partial and evolving hypotheses rather than static identifications of ory of Visualization” should be solved through a collective effort of absolute taxa. Taxonomies are therefore fundamental tools to aid the VIS community, instead of a few individuals’ endeavour. our understanding and assist in the communication and develop- ment of the field of visualization. Main Topic. In this panel, we bring together visualization scien- tists with expertise in these four aspects. They will provide their Tamara Munzner on assessment of the state of the art in each aspect, identify gaps and Principles and Guidelines challenges, and outline opportunities and pathways. In comparison with the two previous panels (in 2010 and 2011), the panel will The depth and richness of the set of principles and guidelines that move on from the question about “why theory”, and will not dwell have been articulated within the visualization literature continues on the question of “which theory”. Instead, the panel will focus on to grow as the community matures. Writing a book allows a large “how to make new theoretical advances as a community”. scope for synthesizing and codifying knowledge [9, 12, 16, 17], and despite a profusion of many recent efforts we still need far more 2 PANEL FORMAT AND SCHEDULE of them
Recommended publications
  • The 2008 Visualization Career Award
    The 2008 Visualization Career Award Lawrence J. Rosenblum The 2008 Visualization Career Award goes to Lawrence (Larry) Rosenblum, in recognition of early technical contributions and unselfish work to nurture and sustain the field of visuali- zation. In the 1980s and early 1990s Larry developed visualization techniques that produced scientific advances in physical oceanography, ocean acoustics, ocean geophysics, and ocean engineering. He also initiated numerous activities to develop visualization as a recognized research field. Subsequent research by his group has advanced VR/AR, graphics, and visual analytics while he has continued to perform significant service to organizations and confer- ences in visualization and VR/AR. As a Program Officer at NSF and ONR, Larry devel- oped new visualization research programs. For his outstanding contributions in research and in governmental program development, and for his pioneering work to nurture and sustain Lawrence Rosenblum the field of visualization, the IEEE VGTC is pleased to award Larry Rosenblum the 2008 Visualization Career Award. Award Recipient 2008 Biography Larry Rosenblum is Director of the Virtual Reality and was used in many of the early visualization courses in Laboratory at the U.S. Naval Research Laboratory (NRL). academia. He is currently detailed to the U.S. National Science Returning to NRL, Larry focused primarily on virtual Foundation (NSF), where he is Program Director for reality research, including seminal work in U.S. Responsive Graphics and Visualization. Majoring in Mathematics, Workbench technology with encouragement from Wolfgang he received his BA from Queens College (CUNY) and his Krueger, and on augmented reality (AR) systems research. MS and PhD (in Number Theory) from The Ohio State His group’s research into uncertainty visualization produced University.
    [Show full text]
  • Arxiv:2009.03390V1 [Cs.DL] 7 Sep 2020 Meta-Analytical Work Around Geospatial Analytics and Geovisualiza- Tion That May Shed Light on Opportunities for Innovation
    A Review of Geospatial Content in IEEE Visualization Publications Alexander Yoshizumi* Megan M. Coffer† Elyssa L. Collins† Mollie D. Gaines† Xiaojie Gao† Kate Jones† Ian R. McGregor† Katie A. McQuillan† Vinicius Perin† Laura M. Tomkins† Thom Worm† Laura Tateosian* Center for Geospatial Analytics, North Carolina State University Figure 1: Attributes of 94 IEEE VIS papers from years 2017-2019 found to have geospatial content. From top to bottom: data domain, geospatial nature of the paper (GEO), and VIS Conference paper type and track (TRK) are shown for each paper. Percentages on the top band (lightest gray bars) correspond to data domain types. The GEO band marks papers as either containing both geospatial data and a geospatial analysis (dark gray) or geospatial data only (light gray). The TRK band is colored by the VIS Conference paper types and tracks listed on Open Access VIS [15]. ABSTRACT gies such as GPS-equipped mobile devices, remote sensing satellites, Geospatial analysis is crucial for addressing many of the world’s and drones have proliferated, the centrality of georeferenced data most pressing challenges. Given this, there is immense value in has only continued to grow. The complexity and volume of the data improving and expanding the visualization techniques used to com- and the importance of the issues at stake drive a need for innovative municate geospatial data. In this work, we explore this important visualization tools to support exploration and communication of intersection – between geospatial analytics and visualization – by geospatial information. examining a set of recent IEEE VIS Conference papers (a selec- As a focal event for the visualization community, the IEEE Vi- tion from 2017-2019) to assess the inclusion of geospatial data and sualization (VIS) Conference profoundly influences the agenda for geospatial analyses within these papers.
    [Show full text]
  • Tamara Munzner Department of Computer Science University of British Columbia
    Ch 3: Task Abstraction Paper: Design Study Methodology Tamara Munzner Department of Computer Science University of British Columbia CPSC 547, Information Visualization Day 4: 22 September 2015 http://www.cs.ubc.ca/~tmm/courses/547-15 News • headcount update: 29 registered; 24 Q2, 22 Q3 – signup sheet: anyone here for the first time? • marks for day 2 and day 3 questions/comments sent out by email – see me after class if you didn’t get them – order of marks matches order of questions in email • Q2: avg 83.9, min 26, max 98 • Q3: avg 84.3, min 22, max 98 – if you spot typo in book, let me know if it’s not already in errata list • http://www.cs.ubc.ca/~tmm/vadbook/errata.html • but don’t count it as a question • not useful to tell me about typos in published papers – three questions total required • not three questions per reading (6 total)! not just one! 2 VAD Ch 3: Task Abstraction Why? Actions Targets Analyze All Data Consume Trends Outliers Features Discover Present Enjoy Attributes Produce Annotate Record Derive One Many tag Distribution Dependency Correlation Similarity Extremes Search Target known Target unknown Location Lookup Browse Network Data known Location Locate Explore Topology unknown Query Paths Identify Compare Summarize What? Spatial Data Why? Shape How? [VAD Fig 3.1] 3 High-level actions: Analyze • consume Analyze –discover vs present Consume Discover Present Enjoy • classic split • aka explore vs explain –enjoy • newcomer Produce • aka casual, social Annotate Record Derive tag • produce –annotate, record –derive • crucial
    [Show full text]
  • Tamara Munzner
    The 2015 Visualization Technical Achievement Award Tamara Munzner The 2015 Visualization Technical Achievement Award goes to Tamara Munzner in recognition of foundational research that has produced a scientific basis for principles and design choices for visualization. The IEEE Visualization & Graphics Technical Community (VGTC) is pleased to award Tamara Munzner the 2015 Visualization Technical Achievement Award. Biography Tamara Munzner Tamara Munzner is a full professor at the University of University of British British Columbia Department of Computer Science, where Columbia she has been since 2002. She was a research scientist from Award Recipient 2015 2000 to 2002 at the Compaq Systems Research Center (the former DEC SRC). She earned her PhD from Stanford between 1995 and 2000, working with Pat Hanrahan. She and prescribe models and methods for visualization design holds a BS from Stanford from 1991, the year she first and the research process itself, including a nested model of attended VIS. design and validation and methodology for design studies. From 1991 to 1995, Tamara was a technical staff Her 2014 book Visualization Analysis and Design provides member at The Geometry Center, based at the University a systematic, comprehensive framework for thinking about of Minnesota. She was one of the architects and imple- visualization in terms of principles and design choices. It mentors of Geomview, the Center’s public domain interac- features a unified approach encompassing information visu- tive 3D visualization system that supported hyperbolic and alization techniques for the abstract data of tables and net- spherical geometry in addition to Euclidean geometry. She works, scientific visualization techniques for spatial data, was co-director and one of the animators of two videos and visual analytics techniques for interweaving data trans- that brought concepts from the cutting edge of geomet- formation and analysis with interactive visual exploration.
    [Show full text]
  • Introduction to Information Visualization.Pdf
    Introduction to Information Visualization Riccardo Mazza Introduction to Information Visualization 123 Riccardo Mazza University of Lugano Switzerland ISBN: 978-1-84800-218-0 e-ISBN: 978-1-84800-219-7 DOI: 10.1007/978-1-84800-219-7 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008942431 c Springer-Verlag London Limited 2009 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publish- ers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer Science+Business Media springer.com To Vincenzo and Giulia Preface Imagine having to make a car journey. Perhaps you’re going to a holiday resort that you’re not familiar with.
    [Show full text]
  • Tamara Munzner Department of Computer Science University of British Columbia
    Ch 1/2/3: Intro, Data, Tasks Paper: Design Study Methodology Tamara Munzner Department of Computer Science University of British Columbia CPSC 547, Information Visualization Week 2: 17 September 2019 http://www.cs.ubc.ca/~tmm/courses/547-19 News • Signup sheet round 2: check column (or add yourself) • Canvas comments/question discussion –one question/comment per reading required • everybody got this right, great! –responses to others required • a few of you did not do this • original requirement of 2, considering cutback to just 1: discuss • decision: cut back to just 1 –if you spot typo in book, let me know if it’s not already in errata list • http://www.cs.ubc.ca/~tmm/vadbook/errata.html • (but don’t count it as a question) • not useful to tell me about typos in published papers 2 Ch 1. What’s Vis, and Why Do It? 3 Why have a human in the loop? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods. • don’t need vis when fully automatic solution exists and is trusted • many analysis problems ill-specified – don’t know exactly what questions to ask in advance • possibilities – long-term use for end users (e.g. exploratory analysis of scientific data) – presentation of known results – stepping stone to better understanding of requirements before developing models – help developers of automatic solution refine/debug, determine parameters – help end users of automatic solutions verify, build trust 4 Why use an external representation? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.
    [Show full text]
  • A Bounded Measure for Estimating the Benefit of Visualization
    arXiv Report 2021 Volume 0 (1981), Number 0 A Bounded Measure for Estimating the Benefit of Visualization: Theoretical Discourse and Conceptual Evaluation Min Chen1 and Mateu Sbert2 1University of Oxford, UK and 2 University of Girona, Spain Visual Mapping with Topological Abstraction Visual Mapping with a Volume Rendering Integral Color Pixel Color Opacity Color Pixel Color Opacity ...... Color Pixel Color Opacity (a) mapping from different sets of voxel values to the same pixel color (b) mapping from different geographical paths to the same line segment Figure 1: Visual encoding typically features many-to-one mapping from data to visual representations, hence information loss. The significant amount of information loss in volume visualization and metro maps suggests that viewers not only can abide the information loss but also benefit from it. Measuring such benefits can lead to new advancements of visualization, in theory and practice. Abstract Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson-Shannon divergence and a new divergence measure formulated as part of this work. We describe the rationale for proposing a new divergence measure. As the first part of comparative evaluation, we use visual analysis to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties.
    [Show full text]
  • F I N a L P R O G R a M 1998
    1998 FINAL PROGRAM October 18 • October 23, 1998 Sheraton Imperial Hotel Research Triangle Park, NC THE INSTITUTE OF ELECTRICAL IEEE IEEE VISUALIZATION 1998 & ELECTRONICS ENGINEERS, INC. COMPUTER IEEE SOCIETY Sponsored by IEEE Computer Society Technical Committee on Computer Graphics In Cooperation with ACM/SIGGRAPH Sessions include Real-time Volume Rendering, Terrain Visualization, Flow Visualization, Surfaces & Level-of-Detail Techniques, Feature Detection & Visualization, Medical Visualization, Multi-Dimensional Visualization, Flow & Streamlines, Isosurface Extraction, Information Visualization, 3D Modeling & Visualization, Multi-Source Data Analysis Challenges, Interactive Visualization/VR/Animation, Terrain & Large Data Visualization, Isosurface & Volume Rendering, Simplification, Marine Data Visualization, Tensor/Flow, Key Problems & Thorny Issues, Image-based Techniques and Volume Analysis, Engineering & Design, Texturing and Rendering, Art & Visualization Get complete, up-to-date listings of program information from URL: http://www.erc.msstate.edu/vis98 http://davinci.informatik.uni-kl.de/Vis98 Volvis URL: http://www.erc.msstate.edu/volvis98 InfoVis URL: http://www.erc.msstate.edu/infovis98 or contact: Theresa-Marie Rhyne, Lockheed Martin/U.S. EPA Sci Vis Center, 919-541-0207, [email protected] Robert Moorhead, Mississippi State University, 601-325-2850, [email protected] Direct Vehicle Loading Access S Dock Salon Salon Sheraton Imperial VII VI Imperial Convention Center HOTEL & CONVENTION CENTER Convention Center Phones
    [Show full text]
  • 3. Graphical Perception Tomorrow
    Graphical Perception Nam Wook Kim Mini-Courses — January @ GSAS 2018 What is graphical perception? The visual decoding of information encoded on graphs Why important? “Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space” — Edward Tufte Goal Understand the role of perception in visualization design Topics • Signal Detection • Magnitude Estimation • Pre-Attentive Processing • Using Multiple Visual Encodings • Gestalt Grouping • Change Blindness Signal Detection Detecting Brightness A Which is brighter? B Detecting Brightness (128,128,128) (144,144,144) A B Detecting Brightness A Which is brighter? B Detecting Brightness (134,134,134) (138,138,138) A B Weber’s Law Just Noticeable Difference (JND) dS dp = k S Weber’s Law Just Noticeable Difference (JND) dS Change of Intensity dp = k S Physical Intensity Weber’s Law Just Noticeable Difference (JND) dS Change of Intensity Perceived Change dp = k S Physical Intensity Weber’s Law Just Noticeable Difference (JND) dS Change of Intensity Perceived Change dp = k S Physical Intensity Most continuous variation in stimuli are perceived in discrete steps Ranking correlation visualizations [Harrison et al 2014] Ranking correlation visualizations Which of the two appeared to be more highly correlated? A B [Harrison et al 2014] Ranking correlation visualizations Which of the two appeared to be more highly correlated? r = 0.7 r = 0.6 Ranking correlation visualizations Which of the two appeared to be more highly correlated? A B Ranking correlation visualizations Which of the two appeared to be more highly correlated? r = 0.7 r = 0.65 Ranking visualizations for depicting correlation Overall, scatterplots are the best for both positive and negative correlations.
    [Show full text]
  • Session 2 Tamara Munzner Department of Computer Science University of British Columbia
    Visualization Analysis & Design Full-Day Tutorial Session 2 Tamara Munzner Department of Computer Science University of British Columbia Sanger Institute / European Bioinformatics Institute June 2014, Cambridge UK http://www.cs.ubc.ca/~tmm/talks.html#minicourse14 Outline • Visualization Analysis Framework • Idiom Design Choices Session 1 9:30-10:45am Session 2 11:00am-12:15pm – Introduction: Definitions – Arrange Tables – Analysis: What, Why, How – Arrange Spatial Data – Marks and Channels – Arrange Networks and Trees – Map Color • Idiom Design Choices, Part 2 • Guidelines and Examples Session 3 1:15pm-2:45pm Session 4 3-4:30pm – Manipulate: Change, Select, Navigate – Rules of Thumb – Facet: Juxtapose, Partition, Superimpose – Validation – Reduce: Filter, Aggregate, Embed – BioVis Analysis Example http://www.cs.ubc.ca/~tmm/talks.html#minicourse14 2 How? EncodeEncode Manipulate Manipulate Facet Facet ReduReducece Arrange Map Change Juxtapose Filter Express Separate from categorical and ordered attributes Color Hue Saturation Order Align Luminance Select Partition Aggregate Size, Angle, Curvature, ... Use Navigate Superimpose Embed Shape Motion Direction, Rate, Frequency, ... 3 Arrange space Encode Arrange Express Separate Order Align Use 4 Arrange tables Express Values Axis Orientation Rectilinear Parallel Radial Separate, Order, Align Regions Separate Order Layout Density Dense Space-Filling Align 1 Key 2 Keys 3 Keys Many Keys List Matrix Volume Recursive Subdivision 5 Keys and values Tables • key Attributes (columns) Items – independent
    [Show full text]
  • Data Visualization by Nils Gehlenborg
    Data Visualization Nils Gehlenborg ([email protected]) Center for Biomedical Informatics / Harvard Medical School Cancer Program / Broad Institute of MIT and Harvard ISMB/ECCB 2011 http://www.biovis.net Flyers at ISCB booth! Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg A good sketch is better than a long speech. Napoleon Bonaparte Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Minard 1869 Napoleon’s March on Moscow Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 4 I believe when I see it. Unknown Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Anscombe 1973, The American Statistician Anscombe’s Quartet mean(X) = 9, var(X) = 11, mean(Y) = 7.5, var(Y) = 4.12, cor(X,Y) = 0.816, linear regression line Y = 3 + 0.5*X Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 6 Anscombe 1973, The American Statistician Anscombe’s Quartet Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 7 Exploration: Hypothesis Generation trends gaps outliers clusters - A large data set is given and the goal is to learn something about it. - Visualization is employed to perform pattern detection using the human visual system. - The goal is to generate hypotheses that can be tested with statistical methods or follow-up experiments. Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 8 Visualization Use Cases Presentation Confirmation Exploration Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 9 Definition The use of computer-supported, interactive, visual representations of data to amplify cognition. Stu Card, Jock Mackinlay & Ben Shneiderman Computer-based visualization systems provide visual representations of datasets intended to help people carry out some task more effectively.effectively.
    [Show full text]
  • An Interview with Visualization Pioneer Ben Shneiderman
    6/23/2020 The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman Jessica Hullman Follow Mar 12, 2019 · 13 min read Few people in visualization research have had careers as long and as impactful as Ben Shneiderman. We caught up with Ben over email in between his travels to get his take on visualization research, what’s worked in his career, and his advice for practitioners and researchers. Enjoy! Multiple Views: One of the main purposes of this blog is to explain to people what visualization research is to practitioners and, possibly, laypeople. How would you answer the question “what is visualization research”? Ben S: First let me define information visualization and its goals, then I can describe visualization research. Information visualization is a powerful interactive strategy for exploring data, especially when combined with statistical methods. Analysts in every field can use interactive information visualization tools for: more effective detection of faulty data, missing data, unusual distributions, and anomalies deeper and more thorough data analyses that produce profounder insights, and richer understandings that enable researchers to ask bolder questions. Like a telescope or microscope that increases your perceptual abilities, information visualization amplifies your cognitive abilities to understand complex processes so as to support better decisions. In our best
    [Show full text]