Information Visualization Introduction and Presentation Topics CSCI 6175 Spring 2016

Introduction and Definitions “Information Visualization” – C. Chen, 2010 –Computer generated interactive graphical representations of information –Process of producing information visualization representations I.e., their design, development and application Deals primarily with abstract, non-spatial data –E.g., visual representation of document collection –Vs. scientific visualization, visual representation of “real world” –Transformation of such abstract, non-spatial data to “intuitive” and meaningful visual representations a central challenge of IV “The transformation is also a creative process in which designers assign new meanings to graphical patterns. Like art, information visualization aims to communicate complex ideas and inspire its users for new connections. Like science, information visualization must present information and associated patterns rigorously, accurately, and faithfully.”

Scientific vs. Information Visualization Contrast: –Scientific visualization - quantitative, (often) spatial data –Information visualization - abstract, (often) non-spatial data

Scientific vs. Information Visualization Contrast: –Scientific visualization - quantitative, (often) spatial data –Information visualization - abstract, (often) non-spatial data

Scientific vs. Information Visualization Contrast: –Scientific visualization - quantitative, (often) spatial data –Information visualization - abstract, (often) non-spatial data

Scientific vs. Information Visualization Contrast: –Scientific visualization - quantitative, (often) spatial data –Information visualization - abstract, (often) non-spatial data How to transform data that is not quantitative or spatial ….. so that it is, and, hence, amenable to physical display? –Involves visual design and development of algorithms Creation of visual-spatial model of the data –E.g., given text of documents, –Create similarity matrix of documents, –Perform multi-dimensional scaling to find 2 dimensions correspond to x, y –Frequency = height

Visualization Stages Creation of visual-spatial model of the data – Data transformation Data transformations: –Map raw data into data tables – e.g., text to similarity matrix Raw Information Visual Form Dataset Views User - Task Data Transformations Visual Mappings View Transformations F F -1 Interaction Visual Perception

Visualization Stages Creation of visual-spatial model of the data – Visual mapping Data transformations: –Map raw data into data tables – e.g., text to similarity matrix Visual Mappings: –Transform data tables into visual structures – e.g., mds to 2 dims – x, y Raw Information Visual Form Dataset Views User - Task Data Transformations Visual Mappings View Transformations F F -1 Interaction Visual Perception

Visualization Stages Display the data, which now has visual form – same as scientific visualization Data transformations: –Map raw data into data tables – e.g., text to similarity matrix Visual Mappings: –Transform data tables into visual structures – e.g., mds to 3 dims – x,y,z View Transformations: –Create views of the Visual Structures by specifying graphical parameters such as position, scaling, and clipping Raw Information Visual Form Dataset Views User - Task Data Transformations Visual Mappings View Transformations F F -1 Interaction Visual Perception

Visualization Stages User interacts with visual form of data Data transformations: –Map raw data into data tables – e.g., text to similarity matrix Visual Mappings: –Transform data tables into visual structures – e.g., mds to 3 dims – x,y,z View Transformations: –Create views of the Visual Structures by specifying graphical parameters such as position, scaling, and clipping Raw Information Visual Form Dataset Views User - Task Data Transformations Visual Mappings View Transformations F F -1 Interaction Visual Perception

Visualization Stages User might even change data transformations and visual mappings Data transformations: –Map raw data into data tables – e.g., text to similarity matrix Visual Mappings: –Transform data tables into visual structures – e.g., mds to 3 dims – x,y,z View Transformations: –Create views of the Visual Structures by specifying graphical parameters such as position, scaling, and clipping Raw Information Visual Form Dataset Views User - Task Data Transformations Visual Mappings View Transformations F F -1 Interaction Visual Perception

Geometry, Structure, & Semantics Again, visualization of data without a predefined geometry, e.g., x,y,z, hallmark of information visualization –So, providing a visual-spatial structure is required Additionally, it is necessary to provide semantics for the visualization –Set of rules mapping meaning to displays –Not only for graphical entities, but also for structure The goal of an information visualization design is to convey the intended message to the viewer through its structural and geometric patterns, as well as visual encodings

Geometry, Structure, & Semantics Convey intended message to viewer through structural & geometric patterns, and visual encodings Spheres are scientific publications Height of bar indicates number of citations Colors represent time citations made, later on top Graph formed from co-citations Objective is to show relationships among scientific publications

Geometry, Structure, & Semantics Convey intended message to viewer through structural & geometric patterns, and visual encodings Spheres are scientific publications Height of bar indicates number of citations Colors represent time citations made, later on top Graph formed from co-citations Objective is to show relationships among scientific publications

Insight Ultimate goal of visualization is for users to gain insights: –Unexpected discoveries –Deepened understanding –New way of thinking about data, or even questions to ask –“Eureka-like” experiences –… and other intellectual breakthroughs Whether just by presenting data in “straightforward” ways –E.g., US deficit, space shuttle data, income – “data graphics” Or by using all tools of spatial-visual structure creation –E.g., document collections, co-citation structure

Insight, through Visualization John Snow’s 1854 map of Cholera deaths in London British physician Then current explanations centered on miasma theory, or, “bad air” Aided by map, plotting deaths by location, showed correlation of pump location and deaths … epidemiology Except monks in area not die

Insight, through Visualization US Deficit What accounts for US deficit? –Economic recovery measures –TARP, Fannie, and Freddie “bailouts” –Wars in Iraq and Afghanistan –Bush-era tax cuts –Economic downturn –Other (longer standing) things

Insight, through Visualization Relationship of income and education by state Numbers – states, %college, income: State % college degree income

Insight, through Visualization Insights : –What state has highest income? –What is relation between education and income? –Any outliers? State % college degree income

Insights, through Visualization Insights : –What state has highest income?, –What is relation between education and income?, –Any outliers?

Insight, through Visualization Challenger crash Presented to decision makers prior to launch –To launch or not –Temp in 30’s “Chart junk” Finding form of visual representation is important –cf. “Many Eyes”

An Example, Challenger Shuttle With right visualization, insight (pattern) is obvious –Plot o-ring damage vs. temperature

Science, Art & Information Visualization Creation of visual-spatial model of data – “like art”, “like science” Recall from first slide, IV deals primarily with abstract, non-spatial data … –Transformation of such abstract, non-spatial data to “intuitive” and meaningful visual representations a central challenge “The transformation is also a creative process in which designers assign new meanings to graphical patterns. Like art, information visualization aims to communicate complex ideas and inspire its users for new connections. Like science, information visualization must present information and associated patterns rigorously, accurately, and faithfully.” Raw Information Visual Form Dataset Views User - Task Data Transformations Visual Mappings View Transformations F F -1 Interaction Visual Perception

Science, Art & Information Visualization Like science, … present information and associated patterns rigorously, accurately, and faithfully –Functional information visualization –Primary role is to communicate a message, “What’s in the data” –Efficiency (speed) often a primary goal Like art, … communicate complex ideas and inspire its users for new connections. –Aesthetic information visualization, or, aesthetics of … –Goal is to present a subjective impression of a data set by eliciting a visceral or emotive response from the user … as art does –Efficiency not a goal, rather “enticing” user to spend more time, … and explore

Science, Art & Information Visualization Which more efficient? Which more engaging? Note: Not art as “mimicry” of style

Science, Art & Information Visualization Galaxy of News – efficiency and engagement x

Information Visualization Focus on interaction Interactive visual representations of information that exploit the perceptual capabilities of the human visual system and the interactive capabilities of the cognitive problem solving loop –Ware, 2002 –Cycle that goes repeatedly through formulation of goals and subgoals –Information needs change as go through problem solving loop –Recall, user change in earlier model

Scope of Info Vis Research Display and interaction techniques –Zooming –Focus + context … seeing whole data set, as well as detail of some Types of data –1d, 2d, 3d –N-dimensional data as special and common info vis challenge –Graphs and networks –Text and document collections, again, a common challenge

Possible Topics Introduction to info. vis. –Papers assigned – Visualizing different data types –Trees and hierarchies –Networks and graphs –N-dimensional visualization Display technique: Focus + context Applications – Social networks –Text and documents –Knowledge domains –Security Perceptual elements in visualization Interaction in information visualization Evaluation and theoretical frameworks Tools and systems for visualization Future directions Suggestions and requests

Trees, Networks, and Graphs Connections between /among individual entities Most generally, a graph is a set edges connected by a set of vertices –G = V(e) –“Most general” data structure Graph layout and display an area of iv Trees, as data structure, occur … a lot –E.g., Cone trees

Tree/Hierarchical Data Tree / hierarchy

Hyperbolic Tree Tree layout - decreasing area f(d) center Interactive systems, e.g., web site

3-d Hyperbolic Tree using Prefuse

Networks - Graphs NSFNET –Cox, D. & Patterson, R., NCSA, 1992

Networks - Graphs Routes of the Internet, 1/15/05 The opte project Earlier snapshot in permanent collection of NY Museum of Modern Art

N-dimensional Data “Straightforward” 1, 2, 3 dimensional representations –E.g., time and concrete Can extend to more challenging n- dimensional representations –Which is at core of visualization challenges E.g., Feiner et al., “worlds within worlds”

N-dimensional Data Parallel Coordinates

N-dimensional Data Multiple Views

Shneiderman’s “7 Tasks” Interaction, Supported by Display Overview task –overview of entire collection Zoom task –zoom in on items of interest Filter task – –filter out uninteresting items Details-on-demand task –select an item or group to get details Relate task –relate items or groups within the collection History task –keep a history of actions to support undo, replay, and progressive refinement Extract task –allow extraction of sub-collections and of the query parameters

Visualization System: VxInsight Developed by Sandia Labs to visualize databases “Elements of database can be “anything” –For IV “abstract” –e.g., document relations, company profiles

Quick Look: VxInsight vvv

VxInsight Interaction paradigm: –Overview –Zoom –Filter –Details on demand –Browse –Search query

VxInsight Overview

VxInsight Zoom in

VxInsight to detail

Overview Strategies Typically useful, or critical, to have “feel” for all data –Then, allows closer inspection in “context” of all data –Overview + detail, focus + context Known from the outset of visualization – Bifocal Lens Shneiderman mantra – “overview first, zoom and filter, details on demand”

Focus+Context: Fisheye Views Detail + Overview –Keep focus while remaining aware of context Fisheye views –Physical, of course, also.. –A distance function. (based on relevance) –Given a target item (focus) –Less relevant other items are dropped from the display –Or, are just physically smaller – distortion

Distortion Techniques, Generally Distort space = Transform space –By various transformations “Built-in” overview and detail, and landmarks –Dynamic zoom Provides focus + context –Several examples follow Spatial distortion enables smooth variation

Focus + Context, 2 Fisheye Views Keep focus while remaining aware of the context Fisheye views: –A distance function (based on relevance) –Given a target item (focus) –Less relevant other items are dropped from the display. Demo of Fisheye Menus: –

Perceptual Elements Visualization provides means to use humans’ visual bandwidth and human perceptual system to to: –Make discoveries, –Form decisions, or –Propose explanations about patterns, groups of items, or individual items

Possible Topics Introduction to info. vis. –Papers assigned – Visualizing different data types –Trees and hierarchies –Networks and graphs –N-dimensional visualization Display technique: Focus + context Applications – Social networks –Text and documents –Knowledge domains –Security Perceptual elements in visualization Interaction in information visualization Evaluation and theoretical frameworks Tools and systems for visualization Future directions Suggestions and requests

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