CSCI 552 Data Visualization Shiaofen Fang What Is Visualization? We observe and draw conclusions A picture says more than a thousand words/numbers Seeing is believing, seeing is understanding Beware of illusions (magicians) Visualization: transformation of data/information into pictures 2 1
Different Types of Data Visualization Scientific Visualization From science, engineering, medicine, etc. Is a method of computing: exploration, simulation, discovery, insight. Data are usually homogeneous with predefined spatial structures. Information Visualization Abstract Data: WWW documents, file structures, relationships, financial data, etc. Usually heterogeneous without spatial structures. 3 Functions of Visualization A Representation of Information Aid for Understanding and Analysis Validation of Results A Tool for Communication 4 2
Examples Terrain geometry: Terrain Texture: (10,20,21), (12,13,14), (13,32,12),..., (1,2,3), (2,4,5),(3,5,6),... (23,34,54), (23,34,23), (45,26,78),... Time 0: Volumetric cloud cover: 0, 0, 12, 14, 15, 15, 17, 12, 23, 45,... Wind vectors: (0.2, 0.3, 0.93,5), (0.4,0.5,0.76,12),..., Time 1: Volumetric cloud cover: 0, 0, 11, 12, 13, 16, 20, 12, 32, 45,... Wind vectors: (0.4,0.5,0.76,12),(0.5,0.5,0.7,6),... 6 3
Info-graphics 7 8 4
9 How Many V s 10 5
11 Perpetual Ocean 12 6
Visible Human 13 Graphical Design Can be more precise and revealing than numerical display Can capture a large amount of information in a very small space Can extend to time-series display Can be narrative Can represent each data point by visual information (graphic, icon, image, color, pattern) 14 7
Cholera map of central London, 1854, by Dr. John Snow 15 Train schedule Paris-Lyon, 1880s 16 8
Napoleon s Russia campaign, 1812, plots 6 variables on a 2D graph 17 Graphical Display (example) fear-rage graph 18 9
Graphical Integrity -- What To Avoid In Visualization The Lie Factor = Size of effect shown in Graphic Size of effect in Data Example: fuel economy standards 19 Graphical Integrity (2)... Visualizing data bearing some dimension by means of objects of higher dimensions Example: the growing barrel 20 10
Graphical Integrity (3)... Quoting data out of context and/or too sparse Example: Connecticut traffic deaths 21 Graphical Integrity (4)... Is cosmetic decoration really needed to make data more interesting Misleading graphical representation Example: NCSA storm model 22 11
Visual Perceptions Visual Memory is Limited We are sometimes not very sensitive to small visual changes Visual perception can be influenced contrast and surrounding environment 23 24 12
How many black dots? 25 26 13
Seeing parallel lines 27 Seeing is not always believing 28 14
Visualization Design Principles Show the data Induce the viewer to think about the substance rather than methodology, design, and the technology Avoid distorting what the data have to say Present large data sets coherently and concisely Encourage comparison of different pieces of data Reveal the data at several levels of detail Serve a reasonably clear purpose: description, exploration, tabulation, or decoration Be closely integrated with the statistical and verbal description of a data set 29 Visualization Design Data Filtering Visual Mapping View Selection and Interactions Aesthetics in Visualization Metaphor in Visualization 30 15
The Design Process 31 Data Filtering Determining the optimal amount of information a certain visualization process can handle. Two approaches 1. Let the users choose the data scale to visualize 2. Multi-view or multi-display 32 16
Visual Mapping From data elements to visual elements People s prior knowledge can help visual perception 33 The Wind Map 34 17
View Selection and Interaction Visual Interaction Zoom and Roll Controlling color mapping. Controlling visual mapping of data. Data zooming and filtering Level of Detail control 4D data visualization using scatter plot and parallel coordinates 35 Aesthetics in Visualization Focus Balance Simplicity - Labels - Networks - Color 36 18
Visual Metaphor A visual metaphor maps the characteristics of some well understood source domain to a more poorly understood target domain (data) so as to render aspects of the target understandable in terms of the source. 37 Trees 38 19
Rivers 39 Ferris Wheel 40 20
Sunflower 41 Tools (InfoVis) Google Refine Tableau R Processing D3 (JS) ColorBrewer 42 21
Tools (SciVis) 43 22