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Data Viz > Quiz 1 > Flashcards

Quiz 1 Flashcards

1
Q

Interactive visualization

A
  • used for discovery
  • intended for a single investigator or collaborators
  • rerenders based on input
  • prototype quality
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2
Q

Presentation Visualization

A
  • used for communication
  • intended for larger group or mass audience
  • does not support user input
  • highly polished
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3
Q

Interactive storytelling

A

Presentations via interactive webpages

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4
Q

Modes of visualization
Interactive Visualization
- User Interaction
- Graphics Rendering
- Target
- Medium

A
  • User controls everything, including dataset
  • Real-time rendering
  • individual or collaborators
  • software or internet
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5
Q

Modes of visualization
Presentation Visualization
- User Interaction
- Graphics Rendering
- Target
- Medium

A
  • user only observes
  • precomputed rendering
  • colleagues, mass audience
  • slide shows, videos
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6
Q

Modes of visualization
Interactive Storytelling
- User Interaction
- Graphics Rendering
- Target
- Medium

A
  • user can filter or inspect details of preset datasets
  • real-time rendering
  • mass audience
  • internet or kiosk
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7
Q

Ultimate goal of data visualization

A

not just about seeing data, it is about understanding data and being able to make decisions based on the data

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8
Q

Why data visualization is important?

A
  • generating a lot of data and information
  • need to process such information
  • need to communicate increasing levels of information
  • stats not enough
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9
Q

Visual variables

A
  • position
  • shape
  • size
  • brightness
  • color
  • orientation
  • texture
  • motion

most important:
- position
- mark/shape

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10
Q

Data Types/Attribute Types

A

Attribute types
- Categorical
- Ordered
-> Ordinal
-> Quantitative

Ordering Direction
- Sequential
- Diverging
- Cyclic

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11
Q

Nominal scale of measurement

A
  • Only satisfies the identity property of measurement
  • Categorial and Arbitrary
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12
Q

Ordinal scale of measurement

A
  • Has the property of both identity and magnitude
  • Ranked (and all the numeric)
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13
Q

Interval scale of measurement

A
  • Has the properties of identity, magnitude, and equal intervals.
  • Discrete. e.g., Fahrenheit (or centigrade) scale to measure temperature
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14
Q

Ratio scale of measurement

A
  • Satisfies identity, magnitude, equal intervals, and a minimum value of zero.
  • Continuous. e.g., weight, distance, etc. Can apply operations of / and
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15
Q

Steven’s law

A
  • 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
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16
Q

Size

A
  • 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
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17
Q

brightness

A
  • 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
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18
Q

color map

A

continuous range of hue and saturation values

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19
Q

Best marks for orientation

A

those with natural single axis

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20
Q

Texture

A

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

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21
Q

Motion

A
  • 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
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22
Q

Issues with color

A
  • 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
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23
Q

Munsell’s model

A

properties of color:
- lightness (black to white)
- hue ( red, orange, yellow, green, blue, indigo, violet)
- saturation (dull to bright)

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24
Q

Most common data types

A
  • sequential data
  • divergent data
  • qualitative data
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25
Q

Selecting a color map

A

Properties of the attribute:
- spatial frequency
- continuous or discrete nature
- type of analysis to be performed

26
Q

divergent/bipolar data

A

data that varies from a central value
- profits and losses
- differences from the norm (e.g. daily temperature vs. monthly average)
- change over time

27
Q

Qualitative data

A
  • also categorical or thematic data
  • color is used to separate areas into distinct categories
28
Q

Perception

A

Process of:
- recognizing (being aware of)
- organizing (gathering and storing)
- interpreting (binding to knowledge) sensory information

  • interpreting the world around us
  • brain makes assumptions about the world to overcome the inherent ambiguity in all sensory data, and in response to the task at hand
29
Q

“preattentive” properties

A
  • limited set of visual properties that are detected very rapidly and accurately by the low-level visual system
  • tasks that can be performed on large multi-element displays in less than 200 to 250 milliseconds
  • certain information in the display is processed in parallel by the low-level visual system
30
Q

conjunction target

A

target made up of a combination of non-unique features

31
Q

Target detection

A
  • Users rapidly and accurately detect the presence or absence of a “target” element with a unique visual feature within a field of distractor elements
32
Q

Boundary detection

A
  • Users rapidly and accurately detect a texture boundary between two groups of elements, where all of the elements in each group have a common visual
33
Q

Region tracking

A

users track one or more elements with a unique visual feature as they move in time and space

34
Q

Counting and estimation

A

users count and estimate the number of elements with a unique visual feature

35
Q

Feature Integration Theory (Anne Treisman)

A

when perceiving a stimulus, features are “registered early, automatically, and in parallel, while objects are identified separately” and at a later stage in processing

response time:
- if task completion time is relatively constant and below chosen threshold, independent of number of distractors, task is said to be preattentive

accuracy:
- If viewers can complete task accurately, regardless of the number of distractors, the feature used to define the target is assumed to be preattentive

36
Q

Change blindness

A

an interruption in what is being seen renders us “blind” to significant changes that occur in the scene during the interruption

37
Q

change blindness explanation

A
  • Overwriting: information that was not abstracted from the first image is lost.
  • First Impression: hypothesis that only the initial view of a scene is abstracted
  • Nothing Is Stored: after a scene has been viewed and information has been abstracted, no details are represented internally
  • Everything Is Stored, Nothing Is Compared: only compared is requested
  • Feature Combination: details from an initial view might be combined with new features from a second view
38
Q

Absolute Judgment of Multidimensional Stimuli

A
  • Combining different stimuli does enable us to increase the amount of information being communicated, but not at the levels we might hope
  • added stimuli resulted in the reduction of the discernibility of the individual attributes
  • having a little information about a large number of parameters seems to be the way we do things
39
Q

Weber’s Law

A

The likelihood of detecting a change is proportional to the relative change, not the absolute change, of a graphical attribute

40
Q

Steven’s Law

A

perceived scale in absolute measurements is the actual scale raised to a power

  • For linear features, this power is between 0.9 and 1.1
  • for area features, it is between 0.6 and 0.9
  • for volume features it is between 0.5 and 0.8
41
Q

Expanding Capabilities

A
  • reconfigure the communication task to require relative, rather than absolute, judgment (adding grid lines and axis tick marks)
  • increasing the dimensionality with caution and in a limited way
  • reconfigure the problem to be a sequence of different absolute judgments, rather than simultaneous stimuli
42
Q

Channel Rankings by Tamara Munzner

Magnitude Channels: Ordered Attributes

A
  1. Position on common scale
  2. Position on unaligned scale
  3. Length (1D size)
  4. Tilt/angle
    …
    worst:
    - Volume (3D size)
43
Q

Channel Rankings by Tamara Munzner

Identity Channels: Categorical Attributes

A
  1. Spatial Region
  2. Color hue
  3. Motion
  4. Shape
44
Q

Separability

A
  • Fully separable (Position + Hue)
  • Some interference (Size + Hue)
  • Significant interference (Width + Height)
  • Major interference (Red + Green)
45
Q

Core idea of data visualization

A

The mapping from data variables to visual variables

46
Q

Tableau’s Key features

A
  • VizQL - Visual Query Language that translates drag-and-drop actions into data queries and then expresses that data visually
  • Live Query Engine - A technology that lets people query databases, cubes, warehouses, cloud sources, spreadsheets, etc. without any programming knowledge
  • With In-Memory Data Engine - uses the complete memory hierarchy (Disk-RAM-L1 Cache) on ordinary computers to speedup access to slow databases
47
Q

Tableau data types

A
  • Text (string) values
  • Date values
  • Date & Time values
  • Numerical values
  • Boolean values (relational only)
  • Geographic values
48
Q

Dimensions and Measures

A
  • terms from Data Warehousing and Multidimensional Models

Dimensions allow data analysis from various perspectives:
- Time, Product, Supplier
–> categorical data

Measures are numeric representations of facts that occurred:
- Sales amount, Store percentage of profit, number of returned products
–> numeric values

49
Q

Independent/dependent variables

A

Independent: Answer questions like Who?, When?, What?

Dependent: Aggregated values

50
Q

Drill up

A

Decrease the Level of detail of VIS by removing dimensions to the VIS

51
Q

Level of detail of VIS

A

= {dimensions that are present in the VIS}

52
Q

Drill down

A

Increase the Level of detail of VIS by adding more dimensions to the VIS

53
Q

Dimensionality choices for visual analysis

A
  • Dimension subsetting (choose 2 dimensions)
  • Dimension embedding (mapping dimensions to color, size etc.)
  • Multiple displays (e.g. matrix)
  • Dimension reduction (transform to lower dimension)
54
Q

Dimension reduction
Principal component analysis (PCA)

A
  • computes new dimensions/attributes which are linear combinations of the original data attributes
  • new dimensions can be sorted according to their contribution in explaining the variance of the data
  • most relevant new dimensions: minimized average error of lost information
55
Q

Multidimensional scaling (MDS)

A
  • projecting M points in N dimensions into L dimensions (L=2 or 3)
  • key goal: maintain the N-dimensional features and characteristics through projection process -> keep relationships
56
Q

RadViz

A
  • force-driven point layout technique
  • n-dimensional data set: N anchor points are placed on the circumference of the circle
  • different placement will give different result
57
Q

Superimposed

A

One line put over another line

58
Q

Parallel Coordinates

A

Each dimension is one axis, one line is one data point in dataset

  • shows relationships between axis
59
Q

Radial axis techniques

A
  • circular line graph
  • polar graphs
  • circular bar charts
  • circular area graphs
  • circular bar graphs
60
Q

Region-based techniques

A
  • bar charts/histograms (stacked, clustered, simple)
  • area (stacked, aligned)
  • heat tables
  • tree maps
  • stacked bubbles

Data Viz (3 decks)

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