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Data Viz > Quiz 2 > Flashcards

Quiz 2 Flashcards

1
Q

Characteristics of Time
scale

A

ordinal (A before B before C)
discrete (points)
continuous (line)

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2
Q

Characteristics of Time
scope

A

point based (most common)
interval based

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3
Q

Characteristics of Time
arrangement

A

linear
cyclic

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4
Q

Characteristics of Time
viewpoint

A

ordered
branching
multiple perspectives

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5
Q

Characteristics of Time-Oriented Data
scale

A

quantitative (numbers)
qualitative (words)

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6
Q

Characteristics of Time-Oriented Data
frame of reference

A

abstract
spatial

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7
Q

Characteristics of Time-Oriented Data
kind of data

A

events
states

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8
Q

Characteristics of Time-Oriented Data
number of variables

A

univariate
multivariate

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9
Q

Mapping of time

A
  • mapping time to space: static visualization, time and data in single coherent representation
  • mapping time to time: dynamic representation, utilise physical dimension to convey time dependency of data
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10
Q

Categorisation on TimeViz Browser
data

A
  • frame of reference: abstract vs. spatial
  • variables: univariate vs. multivariate
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11
Q

Categorisation on TimeViz Browser
time

A
  • arrangement: linear vs. cyclic
  • time primitives: instant vs. interval
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12
Q

Categorisation on TimeViz Browser
vis

A
  • mapping: static vs. dynamic
  • dimensionality: 2D vs. 3D
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13
Q

Geospatial data

A
  • describes objects with specific location in real world
  • map spatial attributes to the two physical screen dimensions resulting in map visualizations
  • map: world reduced to points, lines, and areas
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14
Q

Spatial phenomena

A
  • point phenomena
  • line phenomena: have length, but no width
  • area phenomena: have both length and width
  • surface phenomena: have length, width and height
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15
Q

Maps subdivided into:

A

Map types based on:

Properties of data:
- qualitative vs. quantitative
- discrete vs. continuous

Properties of graphical variables:
- points
- lines
- surface
- volumes

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16
Q

Map projections

A

mapping the positions on the globe (sphere) to positions on the flat surface

longitude: negative = western degrees
latitude: negative = southern

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17
Q

Cylinder projections

A
  • preserve local angles
  • conformal projections
  • degrees of longitude and latitude usually orthogonal to each other
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18
Q

Plane projections

A
  • azimuthal projections
  • map to a plane that is tangent to the sphere with tangent point corresponding to the center point of projection
  • some are true perspective projections
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19
Q

Cone projections

A
  • map to cone that is tangent to the sphere
  • degrees of latitude represented as circles around the projection center
    degrees of longitude as straight lines emanating from center
  • designed to preserve distance from center to the cone
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20
Q

Point data

A
  • discrete in nature, buy may describe continuous phenomenon
  • discrete: occur at distinct locations
  • continuous: defined at all locations
  • smooth: data that change in gradual fashion
  • abrupt: change suddenly
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21
Q

Dot maps

A
  • place symbol at location
  • quantitative parameter mapped to the size or color of the symbol
  • problem of overlap or over plotting in highly populated areas
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22
Q

approaches for dense spatial data

A
  • 2.5D visualization showing data points aggregated up to map regions
  • individual data points as bars, according to their statistical value on a map
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23
Q

visualization of line data

A
  • represent as line segments between pairs of endpoints specified by longitude and latitude
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24
Q

mapping of line data

A
  • standard: data parameters mapped to line width, line pattern, line color, and line labelling
  • start, end, and intersection points can be mapped to visual parameters (size, shape, color, labelling)
  • lines do not need to be straight
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25
Q

Choropleth maps

A

values of an attribute or statistical variable are encoded as coloured or shaded regions on the map

26
Q

Choropleth maps: Issues

A
  • most interesting values often concentrated in densely populated areas with small polygons
  • assumption that in one region values are uniformly distributed
  • size of regions impact perception of importance (color)
  • people can be color blind
27
Q

Dasymetric maps

A
  • variable to be shown forms areas independent of original regions
  • attribute has different distribution than the partitioning
  • ancillary information acquired -> cartographer steps statistical data according to extra information collected within boundary
28
Q

Filters

A

Context filters before dimension filters

Context filters -> dimension filters -> measure filters

29
Q

Data blending

A
  • combines data from multiple data sources into single view
  • sends separate queries to the separate data sources and aggregates results to common level in Tableu
    -> usually join data at row level
30
Q

The query pipeline

A
  1. Extract filters
  2. Data Source Filters
  3. Context Filters
    -> Top N, Fixed LOD
  4. Dimension Filters
    -> Include/Exclude LOD, aggr, Data Blending
  5. Measure Filters
    -> Table Calcs, Clustering
  6. Table Calc Filters
    -> Trend lines, reference lines, pages
31
Q

Elements for designing a dashboard

A
  • identify audience
  • define analytical questions
  • built data viz that will be components of the dashboard
  • design layout
  • design useful tooltips
  • define interactions
  • experiment and refine
32
Q

Isarithmic maps

A
  • shows the contours of some continuous phenomena

-> isometric: if contours determined from real data points (e.g. temperatures)

-> isopleth: if data measured for a certain region and centroid considered as data point

main task: interpolation of data to obtain smooth contours (e.g. triangulation, inverse distance mapping)

33
Q

Cartograms

A
  • generalisation of ordinary thematic maps
  • distorting geography: size of regions scaled to reflect statistical variable
  • can’t fully satisfy shape or area objective -> search compromise between shape and area preservation
34
Q

Noncontinuous cartograms

A
  • keeps map, but fills correct size inside of the map
  • doesn’t preserve input map’s topology
  • scaled polygons drawn inside the original regions
35
Q

Noncontiguous cartograms

A
  • scale polygons to their target size
  • satisfy area objective
  • perfect are adjustment
  • lose map’s global shape and topology
36
Q

Circular cartograms

A
  • completely ignore input’s polygon’s shape
  • each polygon is a circle
  • lose map’s global shape and topology
37
Q

Continuous cartograms

A
  • retains map’s topology perfectly
  • not use area and shape constraints
38
Q

Issues for spatial data mapping

A
  • class separation (bins)
  • normalisation (absolute vs. relative),
  • spatial aggregation (area definition)

has severe impacts on visualization result

39
Q

Map generalization

A
  • process of selecting and abstracting information on a map
  • used when small-scale map is derived from large-scale map
  • application- and task-dependent
    -> emphasise elements that are most important for task
40
Q

Why use interactions?

A
  • static visualisations belong to the past
  • static fail to answer unpredicted questions
  • interactions help users ask their own questions and answer them while exploring data
41
Q

Types of interactivity

A

is a mechanism for modifying WHAT users see and HOW they see it

  • Find relevant data (in sea of other data)
  • reveal more data (drill down to enter details)
  • change views or context (to better answer questions)
42
Q

Interaction operators

navigation

A

altering position of the camera and for scaling the view

e.g. panning, rotating, zooming

43
Q

Interaction operators

selection

A
  • identify an object, collection of objects, or regions of interests

e.g. highlighting and modifying

44
Q

Interaction operators

filtering

A

reduce size of data being mapped on the screen

e.g. eliminating records, dimensions, or both

45
Q

Interaction operators

reconfiguring

A
  • changing way data is mapped to graphical entities or attributes

e.g. reordering the data or layouts -> diff. way of viewing a data subset

46
Q

Interaction operators

encoding

A
  • changing graphical attributes to reveal different features

e.g. point size, line color

47
Q

Interaction operators

connecting

A

linking different views or objects to show related items

48
Q

Interaction operators

abstracting/elaborating

A

modifying the level of detail

49
Q

Interaction operators

hybrid

A
  • combining several of the above in one technique
50
Q

Operand

A

space upon which operator is applied

  • Screen space (pixels)
  • Data Value space
  • Data Structure Space
  • Attribute Space
  • Object space
  • Visualization Structure space
51
Q

Main interactivity tools in Tableau

A
  • selection
  • highlight
  • filtering
  • parameters
  • sets
  • tooltips
  • URL actions
52
Q

Highlighting suitable for

A
  • finding data of interest in the same context
  • show other marks that share attributes
  • find data on another sheet that is related
53
Q

Filtering suitable for

A
  • focus only on the data we want to analyze, reducing cognitive load
  • control the context of data
  • remove unnecessary data and show only relevant one
54
Q

Parameters used to

A
  • explore what-if scenarios
  • customize view
  • make dashboard more flexible
  • since defined globally, can be used across all data sources
55
Q

Sets and way of creating sets

A
  • are custom fields that define a subset of data based on some conditions

Ways of creating sets:
- from marks in a vis
- from a computation
- combining sets

56
Q

Different types of sorting data

A
  • sorting on axis
  • sort by labels
  • sorting by pill and toolbar sort button
  • custom sort
  • clear sorts
57
Q

Default sort

A

All variables take on their default sort

58
Q

Comparative sort

A

When variables are sorted by e.g. SUM(Shipping Cost)

sorted by the highest value of SUM

59
Q

Nested sort

A

Each sub-category is independently sorted, e.g. after highest value

60
Q

Manual sort

A

Variables were manually sorted

Data Viz (3 decks)

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