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CSCI 552Data Visualization

Shiaofen Fang

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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

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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.

Functions of Visualization

A Representation of InformationAid for Understanding and AnalysisValidation of ResultsA Tool for Communication

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(10,20,21), (12,13,14), (13,32,12),...., (1,2,3), (2,4,5),(3,5,6),.....

Terrain geometry:

Terrain Texture:

Time 0:

(23,34,54), (23,34,23), (45,26,78),....

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),...,

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),...

Time 1:

Examples

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Info-graphics

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How Many “V”s

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Perpetual Ocean

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Visible Human

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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)

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15Cholera map of central London, 1854, by Dr. John Snow

16Train schedule Paris-Lyon, 1880s

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17Napoleon’s Russia campaign, 1812, plots 6 variables on a 2D graph

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Graphical Display (example)

fear-rage graph

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Graphical Integrity --What To Avoid In Visualization

Example: fuel economy standards

The Lie Factor = Size of effect shown in Graphic Size of effect in Data

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Graphical Integrity (2) ...

Example: the growing barrel

Visualizing data bearing some dimension by means of objects of higher dimensions

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Graphical Integrity (3) ...

Example: Connecticut traffic deaths

Quoting data out of context and/or too sparse

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Graphical Integrity (4) ...

Example: NCSA storm model

Is cosmetic decoration really needed to make data more interesting

Misleading graphical representation

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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

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How many black dots?

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Seeing parallel lines

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Seeing is not always believing

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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

Visualization Design

Data Filtering

Visual Mapping

View Selection and Interactions

Aesthetics in Visualization

Metaphor in Visualization

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The Design Process

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Data Filtering

Determining the optimal amount of information a certain visualization process can handle.

Two approaches1. Let the users choose the data scale to visualize

2. Multi-view or multi-display

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Visual Mapping

From data elements to visual elements

People’s prior knowledge can help visual perception

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The Wind Map

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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

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4D data visualization using scatter plot and parallel coordinates

Aesthetics in Visualization

Focus

Balance

Simplicity

- Labels

- Networks

- Color

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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.

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Trees

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Rivers

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Ferris Wheel

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Sunflower

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Tools (InfoVis)

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Google Refine

Tableau R Processing D3 (JS) ColorBrewer

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Tools (SciVis)

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