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Intro to Info Vis Outline CS 791/891 Visualization Seminar Fall 2015 Intro to Info Vis Dr. Michele C. Weigle http://www.cs.odu.edu/~mweigle/CS891-F15/ Outline ! Motivation -- why vis? ! Tools ! What, Why, How Framework ! How ! marks and channels ! chart types 2 CS 791/891 - Fall 2015 - Weigle What is visualization? ! "The communication of information using graphical representations" ! Ward, Grinstein, Keim ! "The use of computer-supported interactive visual representations of data to amplify cognition" ! Card, Mackinlay, Shneiderman, Readings in Information Visualization: Using Vision to Think ! "The purpose of visualization is insight, not pictures." ! Ben Shneiderman 3 CS 791/891 - Fall 2015 - Weigle Where have you seen a visualization today? http://hamptonroads.com/weather http://hamptonroads.com/traffic 4 CS 791/891 - Fall 2015 - Weigle What's vis? ! Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods. ! The design space of possible vis idioms is huge, and includes the considerations of both how to create and how to interact with visual representations. ! Vis design is full of tradeoffs, and most possibilities in the design space are ineffective for a particular task. ! Vis usage can be analyzed in terms of why the user needs it, what data is shown, and how the idiom is designed. Munzner, pg. 1 5 CS 791/891 - Fall 2015 - Weigle augment human capabilities Why have a human in the loop? ! Vis allows people to analyze data when they don't know exactly what questions to ask in advance. ! Best path - put a human in the loop ! exploit the pattern detection properties of human vision 6 CS 791/891 - Fall 2015 - Weigle augment human capabilities Humans are great at pattern recognition Create visualizations that lets computers do what computers do well and lets humans do what humans do well. 7 CS 791/891 - Fall 2015 - Weigle augment human capabilities Why have a computer in the loop? Munzner, Fig 1.2 (Barsky et al., 2007) 8 CS 791/891 - Fall 2015 - Weigle augment human capabilities Why use an external representation? ! Vis allows people to offload cognition and memory usage to make space for other operations. ! Diagrams as external representations ! information can be organized by spatial location ! search - grouping items needed for problem-solving in one location ! recognition - grouping relevant info for one item in the same location 9 CS 791/891 - Fall 2015 - Weigle augment human capabilities Visualization can extend your memory What is 57 x 48? paper mental buffer 52 57 [8 * 7 = 56] x 48 [8 * 5 = 40 + 5 = 45] 1 45 6 [4 * 7 = 28] 22 8 [4 * 5 = 20 + 2 = 22] 2 7 3 6 [8 + 5 = 13] [4 + 2 + 1 = 7] Example courtesy Tamara Munzner, Univ. of British Columbia 10 CS 791/891 - Fall 2015 - Weigle augment human capabilities Why depend on vision? ! Visual system provides a high-bandwidth channel to our brains. ! Significant amount of visual information processing occurs in parallel at the pre- conscious level. 11 CS 791/891 - Fall 2015 - Weigle augment human capabilities Can you find the red dot? preattentive processing http://www.csc.ncsu.edu/faculty/healey/PP/index.html 12 CS 791/891 - Fall 2015 - Weigle augment human capabilities Which state had the highest marriage rate? Florida - 7.5 Connecticut - 5.9 Colorado - 6.8 Delaware - 5.4 District of Columbia - 4.7 13 CS 791/891 - Fall 2015 - Weigle augment human capabilities Which state had the highest marriage rate? U.S. Census Bureau, Statistical Abstract of the United States: 2012 http://www.census.gov/compendia/statab/2012/tables/12s0133.pdf, http://www.census.gov/compendia/statab/2012/tables/12s0133.xls 14 CS 791/891 - Fall 2015 - Weigle augment human capabilities Why show the data in detail? ! Vis tools can allow people to explore data to find patterns or to determine if a statistical model actually fits the data ! Look out for questionable data ! "just because it's numbers doesn't mean it's true" ! is it a typo or something interesting? ! "make sure you know which one it is" i i 15 CS 791/891 - Fall 2015 - Weigle i i 8 1. What’saugment Vis, human and Why capabilities Do It? Anscombe's Quartet Anscombe’s Quartet: Raw Data I II III IV xyxyxyxy 10.08.04 10.09.14 10.07.46 8.06.58 8.06.95 8.08.14 8.06.77 8.05.76 13.07.58 13.08.74 13.012.74 8.07.71 9.08.81 9.08.77 9.07.11 8.08.84 11.08.33 11.09.26 11.07.81 8.08.47 14.09.96 14.08.10 14.08.84 8.07.04 6.07.24 6.06.13 6.06.08 8.05.25 4.04.26 4.03.10 4.05.39 19.012.50 12.010.84 12.09.13 12.08.15 8.05.56 7.04.82 7.07.26 7.06.42 8.07.91 5.05.68 5.04.74 5.05.73 8.06.89 mean 9.07.5 9.07.5 9.07.5 9.07.5 var. 10.03.75 10.03.75 10.03.75 10.03.75 corr. 0.816 0.816 0.816 0.816 F.J. Anscombe, "Graphs in Statistical Analysis", American Statistician, Feb 1973 Munzner, Figure 1.3 16 CS 791/891 - Fall 2015 - Weigle Figure 1.3: Anscombe’s Quartet is four datasets with identical simple statistical properties: mean, variance, correlation, and linear regression line. However, visual inspection immediately shows how their struc- tures are quite di↵erent. After [Anscombe 73], Figures 1–4. From http://en.wikipedia.org/wiki/File:Anscombe.svg i i i i augment human capabilities The four data sets are not the same "Graphics reveal data" - Edward Tufte, The Visual Display of Quantitative Information http://en.wikipedia.org/wiki/File:Anscombe.svg 17 CS 791/891 - Fall 2015 - Weigle Why focus on tasks? ! Vis usage can be analyzed in terms of why the user needs it, what data is shown, and how the idiom is designed. ! The intended task is just as important as the data to be visualized. ! Four categories of tasks ! presentation ! discovery ! enjoyment of information ! producing more information for later use 18 CS 791/891 - Fall 2015 - Weigle Outline ! Motivation -- why vis? ! Tools ! What, Why, How Framework ! How ! marks and channels ! chart types 19 CS 791/891 - Fall 2015 - Weigle Tools of the Trade http://selection.datavisualization.ch/ 20 CS 791/891 - Fall 2015 - Weigle Excel http://chandoo.org/wp/2008/09/03/6-charts-to-never-use/ http://www.juiceanalytics.com/writing/recreating-ny-times-cancer-graph/ 21 CS 791/891 - Fall 2015 - Weigle http://www.r-project.org 22 CS 791/891 - Fall 2015 - Weigle Tableau http://www.tableausoftware.com 23 CS 791/891 - Fall 2015 - Weigle D3 http://d3js.org 24 CS 791/891 - Fall 2015 - Weigle Outline ! Motivation -- why vis? ! Tools ! What, Why, How Framework ! How ! marks and channels ! chart types 25 CS 791/891 - Fall 2015 - Weigle Analysis: What, why, and how Analysis: What, •why, what and is shown? how • what is shown? – data abstraction • why is the user looking at it? – data abstraction – task abstraction • why is the user •looking how is atit shown?it? – task abstraction – idiom: visual encoding and interaction • how is it shown?• abstract vocabulary avoids domain-specific terms – idiom: visual encoding and interaction Main reference: Visualization Analysis and Design, Munzner. AK Peters / CRC Press, Oct 2014. • abstract vocabulary avoids domain-specific termshttp://www.cs.ubc.ca/~tmm/courses/533/book/ – translation processSlides initerative, this style are tricky shamelessly borrowed from Tamara Munzner's VAD minicourse, June 2014 (http://www.cs.ubc.ca/~tmm/talks.html#minicourse14) 26 • what-why-how analysis framework as scaffold to think systematically about design space 15 What? Datasets Attributes What? Data Types Attribute Types Items Attributes Links Positions Grids Categorical Data and Dataset Types Why? Tables Networks & Fields Geometry Clusters, Ordered Trees sets, lists Ordinal Items Items (nodes) Grids Items Items Attributes Links Positions Positions How? Attributes Attributes Quantitative Dataset Types Ordering Direction Tables Networks Fields (Continuous) Sequential Attributes (columns) Grid of positions Link Items Cell (rows) Node Diverging (item) Cell containing value Attributes (columns) Value in cell Cyclic Multidimensional Table Trees Value in cell Geometry (Spatial) Dataset Availability Static Dynamic Position 16 2716 DatasetDataset types types Dataset Types Tables Networks Fields (Continuous) Geometry (Spatial) Attributes (columns) Grid of positions Link Items Cell (rows) Position Node (item) Cell containing value Attributes (columns) Value in cell Multidimensional Table Trees Value in cell 2817 DatasetDataset andand data data types types Data and Dataset Types Tables Networks & Fields Geometry Clusters, Trees sets, lists Items Items (nodes) Grids Items Items Attributes Links Positions Positions Attributes Attributes Data Types Items Attributes Links Positions Grids Dataset Availability Static Dynamic 29 18 AttributeAttribute types types Attribute Types Categorical Ordered Ordinal Quantitative Ordering Direction Sequential Diverging Cyclic 30 19 • {action, target} • pairs{action, target} pairs –– discover distribution distribution –– compare trends trends –– llocate outliers outliers –– browse topology topology 31 20 High-level actions: Analyze High-level actions: Analyze consume • Analyze • consume– discover vs –discoverpresent vs present Consume Discover Present Enjoy •• classicclassic split split •• akaaka explore explore vs explainvs explain – enjoy •• newcomernewcomer Produce •• akaaka casual, casual, social social Annotate Record Derive tag •• produce – annotate, record record – derive –derive • crucial design • crucialchoice design choice 32 21 Actions: Mid-level search, low-level query • what does user know? – target, location • how much of the data matters? – one, some, all 33 Why: Targets 34 Outline ! Motivation -- why vis? ! Tools ! What,
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