Quiz 1 Flashcards
Interactive visualization
- used for discovery
- intended for a single investigator or collaborators
- rerenders based on input
- prototype quality
Presentation Visualization
- used for communication
- intended for larger group or mass audience
- does not support user input
- highly polished
Interactive storytelling
Presentations via interactive webpages
Modes of visualization
Interactive Visualization
- User Interaction
- Graphics Rendering
- Target
- Medium
- User controls everything, including dataset
- Real-time rendering
- individual or collaborators
- software or internet
Modes of visualization
Presentation Visualization
- User Interaction
- Graphics Rendering
- Target
- Medium
- user only observes
- precomputed rendering
- colleagues, mass audience
- slide shows, videos
Modes of visualization
Interactive Storytelling
- User Interaction
- Graphics Rendering
- Target
- Medium
- user can filter or inspect details of preset datasets
- real-time rendering
- mass audience
- internet or kiosk
Ultimate goal of data visualization
not just about seeing data, it is about understanding data and being able to make decisions based on the data
Why data visualization is important?
- generating a lot of data and information
- need to process such information
- need to communicate increasing levels of information
- stats not enough
Visual variables
- position
- shape
- size
- brightness
- color
- orientation
- texture
- motion
most important:
- position
- mark/shape
Data Types/Attribute Types
Attribute types
- Categorical
- Ordered
-> Ordinal
-> Quantitative
Ordering Direction
- Sequential
- Diverging
- Cyclic
Nominal scale of measurement
- Only satisfies the identity property of measurement
- Categorial and Arbitrary
Ordinal scale of measurement
- Has the property of both identity and magnitude
- Ranked (and all the numeric)
Interval scale of measurement
- Has the properties of identity, magnitude, and equal intervals.
- Discrete. e.g., Fahrenheit (or centigrade) scale to measure temperature
Ratio scale of measurement
- Satisfies identity, magnitude, equal intervals, and a minimum value of zero.
- Continuous. e.g., weight, distance, etc. Can apply operations of / and
Steven’s law
- change in these parameters (area, loudness and brightness) is in some way underestimated by the human perception
- when marks are represented with graphics that contain sufficient area, the quantitative aspects of size fall, and the differences between marks becomes more qualitative
Size
- easily maps to interval and continuous data variables
- more difficult to distinguish between marks of near similar size -> size can only support categories with very small cardinality
brightness
- human perception cannot distinguish between all pairs of brightness values
- used to provide relative difference for large interval and continuous data variables
- mark distinction for marks drawn using a reduced sampled brightness scale
color map
continuous range of hue and saturation values
Best marks for orientation
those with natural single axis
Texture
considered as a combination of many of the other visual variables:
- marks (texture elements),
- color (associated with each pixel in a texture region)
- orientation (conveyed by changes in the local color)
most commonly associated with a polygon, region or surface
Motion
- associated with any of the other visual variables, since the way a variable changes over time can convey
more information - common use is in varying the speed at which a change is occurring
- other aspect is in the direction for position, this can be up, down, left, right, diagonal, or basically any slope,
- for other variables it can be larger/smaller, brighter/dimmer, steeper/shallower angles, and so on
Issues with color
- relationship btw light we see and colors we perceive is very different
- multiple types of data, each suited to a different color scheme
- significant no of people are color blind
- arbitrary color choices an be confusing
- light color on dark field and dark color on light field are perceived differently which complicated visualization tasks
Munsell’s model
properties of color:
- lightness (black to white)
- hue ( red, orange, yellow, green, blue, indigo, violet)
- saturation (dull to bright)
Most common data types
- sequential data
- divergent data
- qualitative data