Chapter+One+-+Science+Of+Data+Visualization

=Chapter One - Science Of Data Visualization= Process of data visualization = How to transform data for optimal decision making - Collection and storage of data - Translate data in simple terms - Image production (ie display hardware, graphics algorithms) - Personal perception
 * possible feedback loop for more data gathering

Physical environment = source of data Social environment = how data is collected and interpreted

- How symbols convey meaning - Meaning is relative to culture = meaning is created by soc = language Pictures as sensory language - Not arbitrary like language (i.e. tromp l’oeil violate laws of perspective) Sensory vs arbitrary symbols - Sensory = brain perception (i.e. cave drawings) - Arbitrary = no perceptual basis (i.e. word dog has no relation with the animal) Properties of sensory and arbitrary representation - Understanding without training - Resistance to instructional bias (i.e. optical illusion) - Sensory immediacy - Cross-cultural validity = Universally accepted code
 * Semiotics of graphics**
 * Ability to differentiate sensory info quickly
 * Segmentation = process of dividing the visual world using the visual system

- Perception is designed for action - Affordance = perceivable possibilities for action - Physical properties of the environment that we directly perceive. - Resonance = how the visual system responds to the environment - Problems Model of perceptual processing = Two stage model of human visual information processing Stage 1: Parallel Processing to Extract Low-Level Properties of the Visual Scene (bottom up process) - Early processing for contour, color, texture, and spatial cues - First visual info process = large arrays of neurons in the eye and primary visual cortex - Unconscious rapid process = tune to certain info and particular features of the enviro only Stage 2: Sequential Goal-Directed Processing (top down processing) - Perception for action, spatial layout = motor output, long term memory - Obj identification, visual working memory, long term storage = natural language subsystem - Specialized obj recognition = matching visual characteristics with existing long term memory
 * Gibson’s Affordance Theory**
 * Even if enviro perception is direct, data/computer graphics is indirect (ie stock)
 * No clear phys affordance in a graphic interface (i.e. image linking)
 * Rejection of visual mechanism (i.e. color TV)

- Two visual system hypothesis Fundamental forms of data = data values and data structures
 * Locomotion
 * Symbolic obj manipulation

Entities = individual or grouped objects to be visualized Relations = define structures and patterns that relate entities to one another obviously or not

Attributes of entities or relationships - Attribute quality – Four levels of measurement Types of data - Category data = nominal data - Integer data = ordinal class in order - Real-number data = interval and ratio scales
 * Nominal = labeling function, not always in ordered sequence (i.e. fruit – apples, oranges)
 * Ordinal = numeral sequence (ie queued list, film ranking, order of preference)
 * Interval = gap between data values (ie time of departure vs arrival)
 * Ratio scales = expressive power of numbers (ie A is bigger than B, 1:3)

Quantity (1D, 2D, 3D) - Scalar (i.e. weight) - Vector (i.e. direction of travel path) - Tensors (i.e. stress/force in material)

Metadata = theoretical construct derived from a database analysis.