DOCSLIB.ORG

Voting System Technologies

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Voting System Technologies Visual Analytics: New Tools for Gaining Insight from Your Data Ben Shneiderman [email protected] Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer Studies University of Maryland College Park, MD 20742 Visual Analytics: New Tools for Gaining Insight from Your Data Ben Shneiderman [email protected] Twitter: @benbendc University of Maryland College Park, MD 20742 Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil) Design Issues • Input devices & strategies • Keyboards, pointing devices, voice • Direct manipulation • Menus, forms, commands • Output devices & formats • Screens, windows, color, sound • Text, tables, graphics • Instructions, messages, help • Collaboration & Social Media www.awl.com/DTUI • Help, tutorials, training Fifth Edition: 2010 • Search • Visualization HCI Pride: Serving 5B Users Mobile, desktop, web, cloud Diverse users: novice/expert, young/old, literate/illiterate, abled/disabled, cultural, ethnic & linguistic diversity, gender, personality, skills, motivation, ... Diverse applications: E-commerce, law, health/wellness, education, creative arts, community relationships, politics, IT4ID, policy negotiation, mediation, peace studies, ... Diverse interfaces: Ubiquitous, pervasive, embedded, tangible, invisible, multimodal, immersive/augmented/virtual, ambient, social, affective, empathic, persuasive, ... Workshop Overview Wordle.net Information Visualization • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive data • Interaction: widgets & window coordination • Process models for discovery Information Visualization & Visual Analytics • Visual bands • Human percle • Trend, clus.. • Color, size,.. • Three challe • Meaningful vi • Interaction: w • Process mo 1999 Information Visualization & Visual Analytics • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive da • Interaction: widgets & window coordinati • Process models for discovery 1999 2004 Information Visualization & Visual Analytics • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive data • Interaction: widgets & window coordination • Process models for discovery 1999 2004 2010 Business takes action • General Dynamics buys MayaViz • Agilent buys GeneSpring • Google buys Gapminder • Oracle buys Hyperion • Microsoft buys Proclarity • InfoBuilders buys Advizor Solutions • SAP buys (Business Objects buys Xcelsius & Inxight & Crystal Reports ) • IBM buys (Cognos buys Celequest) & ILOG • TIBCO buys Spotfire Spotfire: Retinol’s role in embryos & vision Spotfire: DC natality data http://registration.spotfire.com/eval/default_edu.asp 10M - 100M pixels: Large displays 100M-pixels & more 1M-pixels & less Small mobile devices Information Visualization: Mantra • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand Information Visualization: Data Types . • 1-D Linear Document Lens, SeeSoft, Info Mural • 2-D Map GIS, ArcView, PageMaker, Medical imagery • 3-D World CAD, Medical, Molecules, Architecture SciViz SciViz • Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords, • Temporal LifeLines, TimeSearcher, Palantir, DataMontage • Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap InfoViz InfoViz • Network Pajek, JUNG, UCINet, SocialAction, NodeXL infosthetics.com flowingdata.com infovis.org www.infovis.net/index.php?lang=2 Anscombe’s Quartet 1 2 3 4 x y x y x y x y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89 Anscombe’s Quartet 1 2 3 4 x y x y x y x y Property Value 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 Mean of x 9.0 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 Variance of x 11.0 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Mean of y 7.5 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 Variance of y 4.12 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 Correlation 0.816 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 Linear regression y = 3 + 0.5x 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89 Anscombe’s Quartet Temporal Data: TimeSearcher 1.3 • Time series • Stocks • Weather • Genes • User-specified patterns • Rapid search Temporal Data: TimeSearcher 2.0 • Long Time series (>10,000 time points) • Multiple variables • Controlled precision in match (Linear, offset, noise, amplitude) LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines LifeLines2: Contrast+Creatine LifeLines2: Align-Rank-Filter & Summarize LifeFlow: Aggregation Strategy Temporal Categorical Data (4 records) LifeLines2 format Tree of Event Sequences LifeFlow Aggregation www.cs.umd.edu/hcil/lifeflow LifeFlow: Interface with User Controls Treemap: Gene Ontology + Space filling + Space limited + Color coding + Size coding - Requires learning (Shneiderman, ACM Trans. on Graphics, 1992 & 2003) www.cs.umd.edu/hcil/treemap/ Treemap: Smartmoney MarketMap www.smartmoney.com/marketmap Market falls steeply Feb 27, 2007, with one exception Market falls steeply Sept 22, 2011, some exceptions Market mixed, February 8, 2008 Energy & Technology up, Financial & Health Care down Market rises, September 1, 2010, Gold contrarians Market rises, March 21, 2011, Sprint declines Treemap: Newsmap (Marcos Weskamp) newsmap.jp Treemap: WHC Emergency Room (6304 patients in Jan2006) Group by Admissions/MF, size by service time, color by age Treemap: WHC Emergency Room (6304 patients in Jan2006) (only those service time >12 hours) Group by Admissions/MF, size by service time, color by age Treemap: Supply Chain www.hivegroup.com Treemap: Nutritional Analysis www.hivegroup.com Treemap: Spotfire Bond Portfolio Analysis www.spotfire.com Treemap: NY Times – Car&Truck Sales www.cs.umd.edu/hcil/treemap/ Treemap (Voronoi): NY Times - Inflation www.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html VisualComplexity.com : Manuel Lima Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information SocialAction • Integrates statistics & visualization • 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst) • Identified desired features, gave strong positive feedback about benefits of integration www.cs.umd.edu/hcil/socialaction Perer & Shneiderman, CHI2008, IEEE CG&A 2009 Network from Database Tables www.centrifugesystems.com NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl NodeXL: Import Dialogs www.codeplex.com/nodexl Tweets at #WIN09 Conference: 2 groups ‘GOP’ tweets, clustered (red-Republicans) Twitter networks: #SOTU WWW2010 Twitter Community Twitter Network for “msrtf11 OR techfest ” Twitter Network for “msrtf11 OR techfest ” Twitter Network for “SpaceX ” Twitter Network for “TTW” Twitter Network for #CI2012 No Location Philadelphia Innovation Clusters: People, Locations, Companies 11,000 nodes 26,000 links Pharmaceutical/Medical Pittsburgh Metro Westinghouse Electric No Location Philadelphia Innovation Clusters: People, Locations, Companies Pharmaceutical/Medical Pittsburgh Metro Westinghouse Electric No Location Philadelphia Innovation Clusters: People, Locations, Companies Patent Tech Navy SBIR (federal) PA DCED (state) Related patent 2: Federal agency Pharmaceutical/Medical 3: Enterprise Pittsburgh Metro 5: Inventors 9: Universities 10: PA DCED 11/12: Phil/Pitt metro cnty 13-15: Semi-rural/rural cnty 17: Foreign countries Westinghouse Electric 19: Other states CHI2010 Twitter Community www.codeplex.com/nodexl/ Flickr clusters for “mouse” Computer Mickey Animal Flickr networks Analyzing Social Media Networks with NodeXL I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social media: New Technologies of Collaboration 3. Social Network Analysis II. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics 6. Preparing Data & Filtering 7. Clustering &Grouping III Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
Load more

Recommended publications
  • 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]
  • Treemap Art Project
    EVERY ALGORITHM HAS ART IN IT Treemap Art Project By Ben Shneiderman Visit Exhibitions @ www.cpnas.org 2 tree-structured data as a set of nested rectangles) which has had a rippling impact on systems of data visualization since they were rst conceived in the 1990s. True innovation, by denition, never rests on accepted practices but continues to investigate by nding new In his book, “Visual Complexity: Mapping Patterns of perspectives. In this spirit, Shneiderman has created a series Information”, Manuel Lima coins the term networkism which of prints that turn our perception of treemaps on its head – an he denes as “a small but growing artistic trend, characterized eort that resonates with Lima’s idea of networkism. In the by the portrayal of gurative graph structures- illustrations of exhibition, Every AlgoRim has ART in it: Treemap Art network topologies revealing convoluted patterns of nodes and Project, Shneiderman strips his treemaps of the text labels to links.” Explaining networkism further, Lima reminds us that allow the viewer to consider their aesthetic properties thus the domains of art and science are highly intertwined and that laying bare the fundamental property that makes data complexity science is a new source of inspiration for artists and visualization eective. at is to say that the human mind designers as well as scientists and engineers. He states that processes information dierently when it is organized visually. this movement is equally motivated by the unveiling of new In so doing Shneiderman seems to daringly cross disciplinary is exhibit is a project of the knowledge domains as it is by the desire for the representation boundaries to wear the hat of the artist – something that has Cultural Programs of the National Academy of Sciences of complex systems.
    [Show full text]
  • The Eyes Have It: a Task by Data Type Taxonomy for Information Visualizations
    The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations Ben Shneiderman Department of Computer Science, Human-Computer Interaction Laboratory, and Institute for Systems Research University of Maryland College Park, Maryland 20742 USA ben @ cs.umd.edu keys), are being pushed aside by newer notions of Abstract information gathering, seeking, or visualization and data A useful starting point for designing advanced graphical mining, warehousing, or filtering. While distinctions are user interjaces is the Visual lnformation-Seeking Mantra: subtle, the common goals reach from finding a narrow set overview first, zoom and filter, then details on demand. of items in a large collection that satisfy a well-understood But this is only a starting point in trying to understand the information need (known-item search) to developing an rich and varied set of information visualizations that have understanding of unexpected patterns within the collection been proposed in recent years. This paper offers a task by (browse) (Marchionini, 1995). data type taxonomy with seven data types (one-, two-, Exploring information collections becomes three-dimensional datu, temporal and multi-dimensional increasingly difficult as the volume grows. A page of data, and tree and network data) and seven tasks (overview, information is easy to explore, but when the information Zoom, filter, details-on-demand, relate, history, and becomes the size of a book, or library, or even larger, it extracts). may be difficult to locate known items or to browse to gain an overview, Designers are just discovering how to use the rapid and Everything points to the conclusion that high resolution color displays to present large amounts of the phrase 'the language of art' is more information in orderly and user-controlled ways.
    [Show full text]
  • Christopher L. North – Curriculum Vitae (Updated Sept 2014)
    Christopher L. North – Curriculum Vitae (updated Sept 2014) Department of Computer Science (540) 231-2458 114 McBryde Hall (540) 231-9218 fax Virginia Tech north @ vt . edu Blacksburg, VA 24061-0106 http://www.cs.vt.edu/~north/ Google Scholar: • http://scholar.google.com/citations?user=yBZ7vtkAAAAJ • h-index = 35 Short Bio: Dr. Chris North is a Professor of Computer Science at Virginia Tech. He is Associate Director of the Discovery Analytics Center, and leads the Visual Analytics research group. He is principle architect of the GigaPixel Display Laboratory, one of the most advanced display and interaction facilities in the world. He also participates in the Center for Human-Computer Interaction, and the Hume Center for National Security, and is a member of the DHS supported VACCINE Visual Analytics Center of Excellence. He was awarded Faculty Fellow of the College of Engineering in 2007, and the Dean’s Award for Research Excellence in 2014. He earned his Ph.D. at the University of Maryland, College Park, in 2000. He has served as General Co-Chair of IEEE VisWeek 2009, and as Papers Chair of the IEEE Information Visualization (InfoVis) and IEEE Visual Analytics Science and Technology (VAST) Conferences. He has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics (TVCG), the Information Visualization journal, and Foundations and Trends in HCI. He has been awarded over $6M in grants, co-authored over 100 peer-reviewed publications, and delivered 3 keynote addresses at symposia in the field. He has graduated 8 Ph.D. and 14 M.S. thesis students, 4 receiving outstanding research awards at Virginia Tech, and advised over 70 undergraduate research students including several award winners at Virginia Tech’s annual undergraduate research symposium.
    [Show full text]
  • Human-Centered Artificial Intelligence: Three Fresh Ideas
    AIS Transactions on Human-Computer Interaction Volume 12 Issue 3 Article 1 9-30-2020 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman University of Maryland, [email protected] Follow this and additional works at: https://aisel.aisnet.org/thci Recommended Citation Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Transactions on Human-Computer Interaction, 12(3), 109-124. https://doi.org/10.17705/1thci.00131 DOI: 10.17705/1thci.00131 This material is brought to you by the AIS Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in AIS Transactions on Human-Computer Interaction by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact [email protected]. Transactions on Human-Computer Interaction 109 Transactions on Human-Computer Interaction Volume 12 Issue 3 9-2020 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park, [email protected] Follow this and additional works at: http://aisel.aisnet.org/thci/ Recommended Citation Shneiderman, B. (2020). Human-centered artificial intelligence: Three fresh ideas. AIS Transactions on Human- Computer Interaction, 12(3), pp. 109-124. DOI: 10.17705/1thci.00131 Available at http://aisel.aisnet.org/thci/vol12/iss3/1 Volume 12 pp. 109 – 124 Issue 3 110 Transactions on Human-Computer Interaction Transactions on Human-Computer Interaction Research Commentary DOI: 10.17705/1thci.00131 ISSN: 1944-3900 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park [email protected] Abstract: Human-Centered AI (HCAI) is a promising direction for designing AI systems that support human self-efficacy, promote creativity, clarify responsibility, and facilitate social participation.
    [Show full text]
  • Geotime for a Patrol
    GeoTime Visualization for Peace Support Operations William Wright and Thomas Kapler Oculus Info Inc. Toronto, Canada [email protected] 1. INTRODUCTION Visualization display techniques allow people to see, use and interact with large amounts of multi- dimensional data in a natural and easy manner, particularly in complex, time sensitive situations. Information visualization techniques amplify cognition by increasing human mental resources, reducing search times, improving recognition of patterns, increasing inference making, and increasing monitoring scope [1, 6]. These benefits translate into significant system and task related performance objectives [7]. As more information is available, from more sources, commanders with advanced visualization capabilities will be able to make the most of this data without being overwhelmed. Operational and planning tasks are both amenable to visualization support. Previous work with DARPA’s Command Post of the Future program has shown the importance of multiple, shared, tailored views rather than one common operational picture, and has demonstrated significant progress for Force-on-Force scenarios [3, 7]. There is an opportunity to explore potential high payoff, leap ahead tailored visualization techniques suitable for the unique demands of Peace Support Operations (PSO) and that build on the success to date. This paper reports on one new concept for geo-temporal visualization conceived during a research project sponsored by DRDC in 2002. The term Operations Other than War (OOTW) covers a wide range of activities, for example domestic operations in Canada (Ice Storm 98), humanitarian assistance abroad (Honduran earthquake), or peace support operations (Bosnia). In order to focus the initial work, the investigation and analysis was confined to peace support operations.
    [Show full text]
  • Empirical Studies in Information Visualization: Seven Scenarios Heidi Lam, Enrico Bertini, Petra Isenberg, Catherine Plaisant, Sheelagh Carpendale
    Empirical Studies in Information Visualization: Seven Scenarios Heidi Lam, Enrico Bertini, Petra Isenberg, Catherine Plaisant, Sheelagh Carpendale To cite this version: Heidi Lam, Enrico Bertini, Petra Isenberg, Catherine Plaisant, Sheelagh Carpendale. Empirical Studies in Information Visualization: Seven Scenarios. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2012, 18 (9), pp.1520–1536. 10.1109/TVCG.2011.279. hal-00932606 HAL Id: hal-00932606 https://hal.inria.fr/hal-00932606 Submitted on 17 Jan 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. JOURNAL SUBMISSION 1 Empirical Studies in Information Visualization: Seven Scenarios Heidi Lam Enrico Bertini Petra Isenberg Catherine Plaisant Sheelagh Carpendale Abstract—We take a new, scenario based look at evaluation in information visualization. Our seven scenarios, evaluating visual data analysis and reasoning, evaluating user performance, evaluating user experience, evaluating environments and work practices, evaluating communication through visualization, evaluating visualization algorithms, and evaluating collaborative data analysis were derived through an extensive literature review of over 800 visualization publications. These scenarios distinguish different study goals and types of research questions and are illustrated through example studies. Through this broad survey and the distillation of these scenarios we make two contributions.
    [Show full text]
  • Beyond Animation: Interactive Visualization Techniques for Big Transportation Data
    Beyond Animation: Interactive Visualization Techniques for Big Transportation Data Michael L. Pack Univ. of Maryland CATT Laboratory Traditional Modeling & Simulation Outputs Things we all care about • Solving problems • Analyzing data • See what we study actually get used by others! Things we all NEED to care MORE about • Showcasing our work to others • Getting people excited about our work • Getting people who have the power to fund our work willing to fund MORE of our work! • COMMUNICATING RESULTS! Changing our mindset • Is an animation always appropriate? • What other options do I have? • Should this be difficult? • Do we have the tools we need? • Goal: “Effectively communicating our results to get better results.” How much data are we getting today? • RITIS Today • Traffic events: 40,000 records per day: 0.001 Gb/day • Traffic detectors: 35,000,000 records per day: 5 Gb/day • Probe vehicle data: 4,200,000,000 records per day: 550 Gb/day • CCTV, weather, radio, etc: NO,STA,TSK,EPT records per day: ??? Tb/day • V2X & Automation data: ?,???,???,???,??? records per day: ??? ?b/day Our Challenge • Our mission is to make ALL of this data • easily accessible, • usable, and • understandable to end users and ITS applications… How do we attempt to do this? 7 © 2013 Michael L. Pack, UMD CATT Laboratory Visual Analytics • Not just about pics and graphs… • Traditional querying (or analysis) demands that you • Know what question to ask • know how to ask it (SQL or other language) • Visual Analytics provide the freedom to: - Explore data in new ways - Ask meaningful questions that you wouldn’t have normally thought to ask - Develop new hypothesis - and realize new solutions 8 Why Visualization? • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier..
    [Show full text]
  • Ben Shneiderman Presented by Dinah Coops, Bella Chiu, and Kyle Thomas
    Ben Shneiderman Presented by Dinah Coops, Bella Chiu, and Kyle Thomas Ben Shneiderman at the TreeMap Art Project exhibit, 2013 HCDE 501 - January 22, 2019 https://treemapart.wo rdpress.com Presenters Dinah Bella Kyle Career Trajectory Hypertext Research Direct Manipulation, and dev. of HyperTIES Spotfire From Keyword Search BS, MS & PHD + + to Exploration Teaching Founding Director Reading in Information + Professor at of HCI Lab Visualization: Using Analyzing Social Media University of MD University of Maryland Vision to think Networks with Node XL 1963- 1970- 1997- 2002- 2010- 2015- 1983 1986 2018 1973 1999 2003 2011 2018 Software The Eight Golden Rules Leonardo’s Laptop Node XL Psychology of Interface Design + + + + Craft of Information The New ABC’s Nassi-Shneiderman Designing the Visualizations of Research Diagrams User Interface + + Treemaps Rock the Research Scope of Influence: Theory and Frameworks 5 USES OF HCI THEORY TREE- HCI MAPPING THEORY INFORMATION VISUALIZATION SOFTWARE PSYCHOLOGY DESIGNING THE USER NASSI- SHNEIDERMA N INTERFACE DIAGRAMS 6 REVISIONS SPOTFIRE DIRECT EIGHT GOLDEN MANIPULATION RULES OF INTERFACE DESIGN HYPER- LINKS Honorary degrees Honorary Doctorate Honorary Doctorate Honorary Doctorate University of Stony Brook University, Swansea University, Guelph, Canada United States Wales, UK 2002- 1995 2010 2015 2017 2018 2003 Honorary Doctorate Honorary Doctorate Honorary Doctorate University of of Engineering University of Pretoria, Castile-La Mancha, University of South Africa Spain Melbourne, Australia Achievements 2001 ACM SIGCHI Lifetime Achievement Award Ben ACM SIGCHI is the premier international society President for professionals, academics and students who are interested in human-technology and human-computer interaction (HCI). The CHI Lifetime Achievement Award is the most prestigious award SIGCHI gives.
    [Show full text]
  • List of Readings
    http://wednesdaysmnlove.blogspot.com/2008/10/list-of-readings.html TUESDAY, OCTOBER 7, 2008 List of readings Here are some of the readings I'm hoping to go through during the residency. This is going to be an evolving list. How-To's / Visualization for Beginners 1. “Tactical Technology Collective,” http://www.tacticaltech.org/. 2. John Emerson, “Visualizing Information for Advocacy: An Introduction to Information Design” (Tactical Technology Collective, January 2008), http://www.tacticaltech.org/infodesign. 3. “The Work of Edward Tufte and Graphics Press,” http://www.edwardtufte.com/tufte/index. 4. “Flickr Photo Download: periodic table of visualization methods,” http://flickr.com/photos/colby/352895722/sizes/o/. Books 1. Edward R. Tufte, Beautiful Evidence (Graphics Press, 2006). 2. Edward R. Tufte, Envisioning Information (Graphics Press, 1990). 3. Franco Moretti, Graphs, Maps, Trees, 2005. 4. “Graphs, Maps, Trees - Franco Moretti: an overview of the reviews and critical reactions,” http://www.complete-review.com/reviews/morettif/graphs.htm. 5. Stephen Few, Information Dashboard Design: The Effective Visual Communication of Data (O'Reilly Media, Inc., 2006). 6. Robert L. Harris, Information Graphics: A Comprehensive Illustrated Reference, 1st ed. (Oxford University Press, USA, 2000). 7. Colin Ware, Information Visualization, Second Edition: Perception for Design, 2nd ed. (Morgan Kaufmann, 2004). 8. Ira Greenberg, Processing: Creative Coding and Computational Art, 1st ed. (friends of ED, 2007). 9. Edward R. Tufte, The Visual Display of Quantitative Information, 2nd edition, 2nd ed. (Graphics Press, 2001). 10. Edward R. Tufte, Visual Explanations: Images and Quantities, Evidence and Narrative (Graphics Press, 1997). 11. Paul Mijksenaar, Visual Function: an Introduction to Information Design (010 Publishers, 1997).
    [Show full text]
  • Visualization Viewpoints Pathways for Theoretical Advances in Visualization
    Editor: Visualization Viewpoints Theresa-Marie Rhyne Pathways for Theoretical Advances in Visualization Min Chen Jessie Kennedy University of Oxford Edinburgh Napier University Georges Grinstein Melanie Tory University of Massachusetts Tableau Software Chris R. Johnson University of Utah ore than a decade ago, Chris Johnson Visualization theory is commonly viewed as a re- proposed the “Theory of Visualization” search focus for only a few individual researchers. as one of the top research problems in Its outcomes, perhaps in the form of theorems or Mvisualization.1 Since then, there have been several laws, are perceived as too distant from practice to be theory-focused events, including three workshops useful. Perhaps inspired by well-known theoretical and three panels at IEEE Visualization (VIS) Con- breakthroughs in the history of science, visualiza- ferences. Together, these events have produced a tion researchers may unconsciously have high ex- set of convincing arguments: pectations for the originality, rigor, and significance of the theoretical advancements to be made in a ■ As in all scientific and scholarly subjects, theo- research project or presented in a research paper. retical development in visualization is a neces- On the contrary, although textbooks tend to sary and integral part of the progression of the attribute major breakthroughs to single pioneers subject itself. at specific times and places, in most cases, such ■ Theoretical developments in visualization can breakthroughs generally take years or even decades draw on theoretical advances in many disci- and are usually the product of numerous incremen- plines, including, for example, mathematics, tal developments, including a substantial number computer science, engineering science, psychol- of erroneous solutions suggested by the pioneers ogy, neuroscience, and the social sciences.
    [Show full text]
  • Martin Gardner Papers SC0647
    http://oac.cdlib.org/findaid/ark:/13030/kt6s20356s No online items Guide to the Martin Gardner Papers SC0647 Daniel Hartwig & Jenny Johnson Department of Special Collections and University Archives October 2008 Green Library 557 Escondido Mall Stanford 94305-6064 [email protected] URL: http://library.stanford.edu/spc Note This encoded finding aid is compliant with Stanford EAD Best Practice Guidelines, Version 1.0. Guide to the Martin Gardner SC064712473 1 Papers SC0647 Language of Material: English Contributing Institution: Department of Special Collections and University Archives Title: Martin Gardner papers Creator: Gardner, Martin Identifier/Call Number: SC0647 Identifier/Call Number: 12473 Physical Description: 63.5 Linear Feet Date (inclusive): 1957-1997 Abstract: These papers pertain to his interest in mathematics and consist of files relating to his SCIENTIFIC AMERICAN mathematical games column (1957-1986) and subject files on recreational mathematics. Papers include correspondence, notes, clippings, and articles, with some examples of puzzle toys. Correspondents include Dmitri A. Borgmann, John H. Conway, H. S. M Coxeter, Persi Diaconis, Solomon W Golomb, Richard K.Guy, David A. Klarner, Donald Ervin Knuth, Harry Lindgren, Doris Schattschneider, Jerry Slocum, Charles W.Trigg, Stanislaw M. Ulam, and Samuel Yates. Immediate Source of Acquisition note Gift of Martin Gardner, 2002. Information about Access This collection is open for research. Ownership & Copyright All requests to reproduce, publish, quote from, or otherwise use collection materials must be submitted in writing to the Head of Special Collections and University Archives, Stanford University Libraries, Stanford, California 94304-6064. Consent is given on behalf of Special Collections as the owner of the physical items and is not intended to include or imply permission from the copyright owner.
    [Show full text]