Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

University > FIT3179 Content Revision > Flashcards

FIT3179 Content Revision Flashcards

1
Q

List the three types of actions in Munzner’s Framework.

A
  • Analyse (the overall viz).
  • Search (for elements).
  • Query (specific objects).
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

List the four types of targets in Munzner’s Framework.

A

All Data. Attributes. Network Data. Spatial Data.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

List and describe the ‘Consume’ and ‘Produce’ actions in Munzner’s Framework.

A 
Consume:
- Discover (new knowledge).
- Present (communicate data). 
- Enjoy (casual encounters through curiosity).
Produce:
- Annotate (add graphics / text).
- Record (save elements).
- Derive (produce new elements).
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

List and describe the ‘Target Known’ and ‘Target Unknown’ possible actions in Munzner’s Framework.

A
  • Target Known + Location Known: Lookup (find observation).
  • Target Unknown + Location Known: Browse (look around for attribute).
  • Target Known + Location Unknown: Locate (find specific object).
  • Target Unknown + Location Unknown: Explore (simply explore).
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

List and describe the actions within “Query” in Munzner’s Framework.

A

Identify (understand characteristics). Compare (find differences). Summarise (overview of targets).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

List the targets in ‘All Data’ in Munzner’s Framework.

A

Trends (patterns in data). Outliers (data which doesn’t fit). Features (structures of interest).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

List the targets in ‘Attributes’ in Munzner’s Framework.

A

Distributions. Dependency. Correlation. Similarity. Extremes.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

List the targets in ‘Network Data’ in Munzner’s Framework.

A

Topology. Paths.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

List the targets in ‘Spatial Data’ in Munzner’s Framework.

A

Shape.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

What is a visualisation?

A

Transforms data into information, and then this information into understanding and insights.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Why are visualisations important in today’s age?

A

We work with far more data. Computers allow us to provide interactivity.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Describe the differences between hue, luminance and saturation.

A
  • Hue: Angular direction around the colour wheel.
  • Luminance: Level of illumination or brightness.
  • Saturation: Difference from neutral gray.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Describe the colour conventions for choropleth maps.

A

Primarily the luminance changes. The greater number, the darker the colour. Slight change in hue is possible for an aesthetic function.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Describe the four levels of the analysis framework.

A
  • Domain: a field of interest of the users.
  • Task and data abstraction: Translate from the domain into a what you are going to display (data abstraction) and the purpose of displaying it (task abstraction).
  • Idiom: How is the information presented?
  • Algorithm: Computation of the idiom.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

List all of the data attribute types and ordering directions in Munzner’s Framework.

A
  • Categorical
  • Ordered (ordinal, quantiative)
  • Sequential
  • Diverging
  • Cyclic
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

List all of the dataset types in Munzner’s Framework.

A
  • Tables (cells contain values, observations in rows, attributes in columns).
  • Trees & Networks (node = vertex, link = edge).
  • Spatial: Field, Geometry.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

What is the purpose of the five design sheet methodology?

A

Helps to structure our approach in idea generation. It is a design thinking process which encourages us to explore a design space before finding a solution.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

What are the steps in the five design sheet process?

A
  • Brainstorm (sketch ideas, group similar ideas).
  • Layout, Focus, Operations, Discussions, Meta-information.
  • Realisation (take the best from previous designs. Focus on algorithm, dependencies).
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

What is a mark? List a few marks.

A

A geometric primitives used for displaying data. eg. points, lines and areas.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

What is a channel? List a few channels.

A

Control appearance of marks. eg. position (horizontal, vertical), colour, shape, tilt, size (length, area, volume).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Rank the channels in terms of easiest to understand to the human eye (ordered data).

A
  1. Position (common scale).
  2. Position (non-aligned scale).
  3. Length, direction, angle.
  4. Area.
  5. Volume, curvature.
  6. Luminance, saturation.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Rank the channels in terms of easiest to understand to human eye (categorical data).

A
  1. Spatial region / position.
  2. Colour hue.
  3. Motion.
  4. Shape.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Describe the expressiveness principles.

A

Visual encoding should express all of the information in the dataset attributes. Ordered data should use channels which we sense as ordered. Categorical data should not imply ordering.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Describe the effectiveness principles.

A

The most important attributes should be encoded with the most effective channels to be the most noticeable.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

Describe the data-ink ratio by Edward Tufte’s and how it should be used.

A
  • Ratio = Ink (elements used for data) / Total Ink for all Elements
  • Tufte argues that the ratio should be close to 1 and ornamental elements should be removed. Strip away ink not dedicated to data.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

Describe what chartjunk is.

A

Chartjunk is unnecessary and redundant ink that does not add anything to the understanding of the data. Common when a visualisation doesn’t have much data.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

Describe the purpose of storytelling.

A

Guide the viewer through the visualisation. Control the order in which data is seen. Eyes move from left to right and top to bottom.

28
Q

List and describe Gestalt’s principles of visual perception.

A
  • Proximity: Physically close together = group.
  • Similarity: Objects of same colour, shape, orientation = group.
  • Closure: Create objects which fit our perception.
  • Enclosure: Objects inside of the same shape = group.
29
Q

What are the seven genres of narrative visualisations?

A
  • Magazine Style.
  • Annotated Chart.
  • Partitioned Poster.
  • Flow Chart.
  • Comic Strip.
  • Slide Show.
  • Film / Video / Animation.
30
Q

Define the macro-reading and micro-reading?

A
  • Macro: Simplest and broadest reading (and the intended reading).
  • Micro: Details or the subtle story, which may be personal and unique to each viewer.
31
Q

What are some quick signs that a graphic is lying?

A
  • Broken scales (doesn’t start at zero).
  • Two different scales for different data.
  • Data which has not been normalised.
  • Unnecessary decorations such as 3D effects.
32
Q

List the different types of colour spaces and their uses.

A
  • RGB, HSV, HSL, XYZ, Lab.
  • Lab is visually equidistant. A colour appearing halfway between colour 1 and colour 2 will be numerically half way.
  • HSV and HSL are used for colour picking.
33
Q

What are some guidelines for picking colours in a data visualisation?

A
  • Avoid gradients for critical numerical values. They are hard to read.
  • Avoid using too many (7+) colours.
  • Use the same colour for variables consistently throughout vis.
  • Grey is very useful for less important elements. Important elements stand out.
  • Make sure the contrast is high enough.
  • Light colours for low values and dark colours for high values.
34
Q

Describe the difference between the Mercator and Hammer projection.

A

The mercator projection preserves angles, and is useful for naval navigation.
The hammer projection is an area-preserving projection. However, angles and shapes are distorted towards the border of the map.

35
Q

Describe figure-ground, figures and grounds.

A
  • Figure-ground: visual depth for accentuating one object over another.
  • Figures: important objects, distinct shape, saturated colour, larger.
  • Grounds: things less important, background, thing, small, desaturated.
36
Q

Describe the visual center, sight lines, symmetry and balance.

A
  • Visual center: The important element slightly above the middle of the viz.
  • Sight lines: invisible horizontal, vertical lines which stabilises map layout.
  • Symmetry: Balance around vertical axis. Left is traditional, cautious, right is more modern, complex and creative.
  • Balance: Where everything is placed. Does it feel right?
37
Q

Describe font family, typeface, font and glyph.

A
  • Font family: variations of a single typeface (standard, bold, condensed, italic).
  • Typeface: a set of letters with a unique design.
  • Font: A subset of typeface, including all letters and numbers.
  • Glyph: an element / aspect of writing (“shape of character”).
38
Q

What is the difference between Serif and Sans serif? Where are they used?

A
  • Serif: Times, Garamond, Bodoni are Serif typefaces. They are traditional and used in print newspapers.
  • Sans Serif: Helveltica, Myriad, Arial are Sans Serif. They are modern and remove the unnecessary parts of letters. Better on maps.
39
Q

How can a typeface be varies to indicate importance?

A

Size, Weight (bold, medium, regular), Italic, Case, Colour.

40
Q

What are some label placement guidelines?

A
  • No overlapping labels.
  • Labels must be at visual top level.
  • Labels must not cover important features.
  • Clear visual association between label and labelled feature.
41
Q

List some tools for data preparation.

A

Python, PHP, Mr Data Converter, Mr People.

42
Q

List some tools for analysis and visualisation.

A

Tableau Public, IBM Many Eyes, Raw, Dygraphs, Wolfram Alpha, jqPlot, Excel, Spotfire.

43
Q

List some tools for interactive visualisation programming.

A

D3.js (popular, low level, powerful), ProtoVis, R & Shiny, Google Charts, Leaflet, Vega, Chart.js.

44
Q

List some tools for presentation of a visualisation.

A

Inkscape, Illustrator, Powerpoint.

45
Q

List some tools for use in a map.

A

QGis, Leaflet (used for maps), ArcGIS.

46
Q

Why do we classify data? What are the advantages and disadvantages of selecting classes?

A
  • We simplify data to make visualisations easier to read, clarify the message and show trends.
  • Fewer classes are easier to read, but results in a reduction in information.
47
Q

List and describe different classification methods.

A
  • Equal data: each class contains same range. Works best when data is well spread.
  • Quantiles: Equal number of observations in each class.
  • Natural breaks: Find breaks in data to maximise the difference between classes.
  • Unclassed: Each unique value gets a unique colour along a gradient.
48
Q

What are some guidelines for data classification?

A
  • Group similar values into one class.
  • Show clusters and extreme values.
  • Avoid empty classes if possible.
  • Do not overlap classes.
  • Avoid gaps between classes, as they are confusing.
49
Q

What is the motivation behind visualising multivariate data?

A
  • Multidimensional data is ubiquitous and comes from sensors, user data, tracking data in sports and stock exchanges.
  • We need to understand complex processes and support decision making.
50
Q

How do we visualise multidimensional data?

A
  • Scatterplots, scatterplot matrices, parallel coordinate plots.
51
Q

What is the goal of immersive analytics?

A
  • Remove barriers between people, data and the tools they use for analysis.
  • Support data understanding and decision making everywhere by everyone.
  • Make embodied tools that are intuitive, engaging, and make the best possible use of all sensory channels.
52
Q

What is the design space and spectrum for describing immersive analytics.

A
  1. Interaction space size: from hand to a room.
  2. Physicality: display versus projection.
  3. Navigation support.
  4. Support for menu-interaction.
  5. Display space: 2D, 2.5D, 3D and beyond…
  6. Mapping between interaction and display space: controlling space using mouse versus touching the space.
53
Q

What are the challenges of origin-destination flows?

A
  • Scalability: showing a low to large amount of flows can be challenging and result in difficulties in interpretation.
  • Embedding geographic context: how do we show geographical information amongst the elements.
  • Some solutions: (1) route flows away from each other. (2) group flows going in the same direction.
54
Q

What is the best method of displaying flows in 3D space?

A
  • 3D heights for distance between locations was the most accurate and preferred.
  • 2D straight is fast for comparing flows but less preferred.
55
Q

Why is interactivity important?

A
  • With big data sets, there is too much data to be presented easily.
  • Therefore we must see an overview first, zoom and then filter, requesting details-on-demand.
56
Q

What are some reasons for adding interactivity?

A
  • Reduce cognitive load on user.
  • Cater for extra dimensions.
  • To more easily facilitate macro/micro readings.
  • To add extra meaning: narrative for the user, information discovery.
57
Q

Describe cognitive load and our memory processes.

A
  • Cognitive load is when we have to process more than our brains can handle.
  • Our long term memory is good and unlimited, but our working memory is limited.
  • Focus and context principles help us deal with this.
58
Q

How does improving macro/micro readings help with interactivity?

A
  • Macro readings should always be evident.

- Interactivity can allow exploration of data, creating additional narratives.

59
Q

What are the methods for manipulation of a visualisation?

A
  • Change a visualisation to show a specific time.
  • Select specific observations, supported in every interactive idiom.
  • Navigate (change of viewpoint): zoom, pan and constrain view (limit motion of camera).
  • Attribute reduction: Slice (extract a single slice from a 3D view), cut (view a volume from one perspective).
60
Q

When should nominal, sequential and diverging colour schemes be used?

A
  • Nominal colour schemes should be mapped with nominal colour schemes. Different hues can be used for categorical data.
  • Sequential colour schemes should be used for ordered categories or numerical data.
  • Diverging colour schemes should be used for data which can be divided at a meaningful middle point.
61
Q

What are the advantages, disadvantages and considerations of using a dot density map?

A
  • Advantages: Raw data can be used, data does not need a unit. Works in black and white.
  • Disadvantages: Terrible for reading the numbers.
  • Considerations: Find a balance between density of dots in heavy and light areas.
62
Q

What are the disadvantages and considerations of choropleth maps?

A
  • Disadvantages: May provide too much information. Difficult to match colours. They may print poorly.
  • Considerations: Unclassed choropleths should be used for filtering the view and classed choropleth maps should be used when rates need to be extracted from a map.
63
Q

What is the usage, advantages, disadvantages and considerations of proportional symbol maps?

A
  • Usage: Scales the size of an area of simple symbols.
  • Advantages: Used for numerical data, or ordered categorical data. Easier to extract numbers than dot maps. Symbols maintain size despite geographical area.
  • Disadvantages: Congestion and overlap if there is big variation in size. May be hard to estimate areas.
  • Considerations: Flexible in presenting a range of data types.
64
Q

What is task abstraction?

A
  • Defining the goal of the visualisation. What do you want the user to do?
65
Q

What is data abstraction?

A
  • Looks at what we are going to display and visualise.
66
Q

What are some key aspects in analysing a choropleth map?

A
  • What data type is mapped?
  • Does it need to be normalised?
  • Which colour components vary?
  • Is the colour choice appropriate for the type of data?
  • Are the class breaks and the legend logical?

University (11 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions