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Week 1 (Textbook chapters 1.1 to 1.6, 2, 5)

Data visualisation definition
data types

Week 2

Table idioms 1 (Textbook chapter 7 (pages 146–148, 150–153, 155–157, 168–170)) Textbook chapter 7 (pages 146–148, 150–153, 155–157, 168–170), scans from Kirk 2019 on Moodle.

Week 3

Required reading by National Geographic on Moodle

Week 4

Colour,
Required reading about gestalt principles on Moodle and blog posts by Lisa Charlotte Rost about use of colour for data visualisation.
layout,
typography (including label placement)

Week 5 Idioms for networks and trees

Textbook chapter 9 and scans from Kirk 2019 on Moodle.

Week 6 Table idioms 2 and repeating patterns

Required reading about radar charts and repeating patterns and scans from Kirk 2019 on Moodle.

Week 7

map idioms
(dot maps, proportional symbol maps, choropleth maps, area cartograms, flow maps).
Required readings by axismap and scans from Kirk 2019 on Moodle.

Week 8

Scalar field visualisation/terrain visualisation
(contour lines, shaded relief, colour mapping, line integral convolution), web maps

Week 9

Data classification, interactive visualisation
Textbook chapter sections 6.5 and 6.7,
required readings by axismaps and Lisa Charlotte Muth on classification on Moodle.

Week 10

Animation for data visualisation and tools for creating visualisations

Week 11 Immersive data visualisation

 
FIT3179 - Visualisation Vocabulary

Week 1

In week one, I learnt about the fundamental data types, marks and channels.
Data visualisation definition (There is no universal definition!)
definition 1
"Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively." - Munzner, Visualization Analysis & Design
definition 2
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Bernie uses the framework of what, why and how to introduce them.

Data types (What)

Generally, you gotta understand three questions :

What are the types of attributes?

  1. Categorical, 2. Ordered → (Ordinal, Quantitative)

If the data attributes can be ordered, what are the types of order direction?

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What are the types of dataset?

  1. Table (Easy to explain: cell, column and row)
2. Tree (with node and vertexes)
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  1. Spatial
    1. Fields
      1. Unlike table, fields stores the continuous values
    2. Geometry
      1. There are many position, spatially ,of which it consists of different values.
More detail can be found via this link.

Analyse and Action (Why)


Your action needs to have a clear goal of which you want to
  • analyse,
  • search
  • or query.
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And when we say action (those 3 above), we need to have targets to be ACTED, which are from :
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One of the most important actions is Analyse.

Using why why why, we can know the first principles of analysing things:

1. Why analyse?

Essentially, you want to analyse to get the insight.

2. Why bother getting the insight?

Two reason: one is to consume and produce
3.1. Why consume?
  • (Discover) some new information that is not understood; that is to Verify or Generate a hypothesis
  • Or (present) the situation (like making a dashboard)
3.2. Why produce?
  • Generate new output
  • Derive (like taking average of some values) or Transform a the data (making a new attributes)

The synonyms of and difference among "Searching".

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Mark and Channels (How)


Marks :

Note :
  • Bar chart uses Line, not Area.
  • Area chart and Treemap use Area
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Channels : Give meaning for the mark

Color channels
There are 3 dimension that that humans intuitively can distinguish.
Color Hue : Raw color like 彩虹顔色
Color brightness : the brightness of the color
Color Saturation :
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Magnitude Channels VS Identity Channels

  • Identity Channel tells us about where something is. (e.g the dots in scatterplot)
    • human is quite good to identify lines (1d) but bad to distinguish volume (3d)
  • Whereas Magnitude Channel tells us how much of something there is. (e.g. the size of dots)
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Week 2


This week is to understand Visualisation idioms for table data.
Essentially, you want to know which types of charts to use, that we call IDIOMs here. To select the types of which is based on :
  • attribute type and table layout
  • dataset type
  • action and target
  • shape

As mentioned, we analyse the types of chart according to What, Why, How framework.
 
As an example, we examine a scatterplot.
What:
  • Is to find out how many types of attributes are there
Why:
  • The purpose of the graph
How :
  • How do you present the chart based on Marks and Channels
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Here is the table you want to find out the quantity of attributes corresponding to the kind of chart
What Why How framework
Scatterplot matrix (SPLOM)
Document 1
Dot plot
Dot plot
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stacked bar chart
Document 2
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Pie chart
Document 3 & 4
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Week 5 Idioms for networks and trees
Week 6 Content : Idioms for Tables
Venn diagram
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Radar chart
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Parallel Coordinate Plot (High-dimensional)
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Streamgraph
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ISOTYPE
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Without What Why How
Proportional Symbol Chart
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Word Cloud
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Dot Matrix Chart
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Waffle Chart
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Histogram
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Heat Map
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Box Plot
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Slope Chart
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Bump Chart
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Spiral plot
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Week 7 Map projections
Dot maps
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Proportional symbol maps
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Choropleth Maps
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Bin maps
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Area Cartograms
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Flow Map
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Vector Fields
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Week 8 - Visualisation of geographic fields

Week 3

Data-Ink Ratio vs. ChartJunks


Calculation is no way to be done, generally it is a reference:
  • High ratio is good; which means ink used is to describe the data.
  • Whereas Low D-I ratio means the elements were not describing the data directly, and somehow distracting.
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Information Density

 

What is ChartJunk?

  • Redundant and distracting-from-understanding elements of a chart
  • Does not add values to understanding the data
ChartJunk Elements
  • heavy or dark grid lines,
  • unnecessary text,
  • inappropriately complex or gimmicky font faces,
  • ornamented chart axes, and display frames, pictures, backgrounds or icons within data graphs, ornamental shading and
  • unnecessary dimensions.
Example 2 of ChartJunk
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BUT having ChartJunk does not mean it is bad as it :

  • increases memorability, and
  • create a positive attitude towards an artifact.

In conclusion

we need to find a balance between decorative graphical design and increasing data-ink ratio

Elements of Storytelling

7 kinds of genres. Note : they are not MUTUALLY EXLUCSIVE; you can have Slide Show and Flow Chart at the same time
7 kinds of genres. Note : they are not MUTUALLY EXLUCSIVE; you can have Slide Show and Flow Chart at the same time

How to Lie with Visualisations

We, as readers, often are being manipulated of what we perceived from the content consumption. One of the mediums is through Visualisations.
There are kinds of way to prevent ourselves to be tricked :

1. Lies with Playing with Scales

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3. Lies with Absolute vs Relative (or Proportional vs Non-proportional)

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2. Lies with Individual vs. Accumulated

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4. Lies with Inexistent Correlation

Just because two sets of numbers follow a similar path doesn’t mean there is a correlation.
Just because two sets of numbers follow a similar path doesn’t mean there is a correlation.
 

5. Problems of charts with two axes

Delusion
Delusion
Crystallisation
Crystallisation

Five Design Sheet Methodology

  • Helps us structure our approach to ideation.
Stages : Brainstorm → Initial Design → Realisation Design

Brainstorm

  1. Sketch and draw as many ideas as you can think of
  1. Remove duplication
  1. Group similar ideas
  1. Combine & Refine: From mini-ideas to bigger solutions… perhaps one system with multiple views?
  1. Does this solution satisfy the Why?
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Initial Design

Layout:
e.g. sketched screen-shot
Focus:
explanations of key/novel visualisation techniques
Operations:
details of key interactions (e.g. a statechart)
Discussion:
focus on advantages and disadvantages of the design
Meta-information:
title/author, date, sheet- number, task
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Realisation Design

Take the best of the previous designs and explore in greater detail.
Focus on:
  1. Description of algorithms / techniques
  1. Dependencies: e.g. software libraries, compatibility, etc.
  1. Estimate time and effort to build the solution
  1. Specific requirements of materials, hardware (desktop, tablet, phone, etc.)
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Week 4

Color


Colour Models

There are 2 kinds of alternative representations to encode the RGB color models
  • HSV (for hue, saturation, value)
  • HSL (for hue, saturation, lightness)

Colour Spaces

How to effectively use color in Data Visualisation

Rules are referenced from here
  1. Rule : If you need more than seven colors in a chart, consider using another chart type or to group categories together.
  1. Rule : Consider using the same color for the same variables
  1. Rule : Make sure to explain to readers what your colors encode
  1. Rule : Consider the color grey as the most important color in Data Visualisation
  1. Rule : Make sure your contrasts are high enough
  1. Rule : Consider where your colors appear in relation to each other.
  1. Rule : Use intuitive colors
  1. Rule : Use light colors for low values and dark colors for high values
  1. Rule : Don’t use a gradient color palette for categories and the other way round
  1. Rule : Use lightness to build gradients, not just hue
  1. Rule : Consider using two hues for a gradient, not just one
  1. Rule : Consider using diverging color gradients.
  1. Rule : Consider color-blind people

Gestalt Principles of Visual Perception


What is Gestalt?

It means Form or Shapes.

Why Gestalt Principles of Visual Perception

When it comes to identify which visual elements are signals (the information we want to communicate) and which might be noise (clutter), consider this principles.

There are 6 Gestalt principles to follow :


1. Proximity

物以(近)聚 : we tend to think objects that are close together as a group
物以(近)聚 : we tend to think objects that are close together as a group

2. Similarity

物以類聚 :  we tend to think objects that have SIMILAR color, shape, size, orientation are perceived as a group
物以類聚 : we tend to think objects that have SIMILAR color, shape, size, orientation are perceived as a group

3. Enclosure

We tend to think of objects that are physically enclosed together as a group
We tend to think of objects that are physically enclosed together as a group
Leveraging the principles to draw a visual distinction
Leveraging the principles to draw a visual distinction

4. Closure

We tend to perceive as a set of individual elements as a single shape when they can. For example, people tend to find this as a circle first and only after that S individual element.
We tend to perceive as a set of individual elements as a single shape when they can. For example, people tend to find this as a circle first and only after that S individual element.
By default we tend to think that a chart must have a background shading and border. But when we remove The unnecessary elements our data stands out more
By default we tend to think that a chart must have a background shading and border. But when we remove The unnecessary elements our data stands out more

5. Continuity

The principal tells that when looking at objects we tend to seek smoothest path and naturally create continuity in what we see. For example if I take the object 1 apart  and pull them apart we will expect to see what’s shown next
The principal tells that when looking at objects we tend to seek smoothest path and naturally create continuity in what we see. For example if I take the object 1 apart and pull them apart we will expect to see what’s shown next
Similarly, when applying this principle to the graph, that means when I remove the unnecessary vertical Y-axis from the graph, our data stands out more. Because the consistent white space between the label on the left and the data on the right.
Similarly, when applying this principle to the graph, that means when I remove the unnecessary vertical Y-axis from the graph, our data stands out more. Because the consistent white space between the label on the left and the data on the right.

6. Connection

We tend to think of objects that are physically connected as a group.
We tend to think of objects that are physically connected as a group.
So, we leverage connection principle in a line graphs, to help our eyes to see orders in the data.
So, we leverage connection principle in a line graphs, to help our eyes to see orders in the data.
 

 

Visual Hierarchy with Figure-Ground


Visual Hierarchy :

Figure-Ground

  • Graphical representation in which elements are ranked according to their importance.
  • Important elements are graphically emphasised and less important elements are de-emphasised.
  • Representation :
    • Bold, Italic, Saturation, color text
  • Visual depth for accentuating one object over another
  • Perception : one object stands in front of another and appears to be closer to the reader.

What does Figure-Ground mean ?

  • Figures:
    • important objects, become objects of attention and standout from the background
  • Grounds:
    • things less important, the background.
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Layout


There are a few rules for designing layout; it sounds simple but is very essential.

1. Reading direction

We read from top-left to bottom-right
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2. Visual Centre

most important things should be placed in the center
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3. Sight line

An invisible horizontal or vertical grid lines that separate the visual elements
The less number of Sight line, the more stable the layout mapped.
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4. Alignment with Invisible Frame

Self-explanatory
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5. Symmetry and Balance

Ensure visual elements are aligned in the Symmetry manner

What is the difference between Symmetry and Balance ?

Symmetry
  • a balance around a central vertical axis
Balance
  • All elements are placed with proper distances; which is not off-balanced.

6. Balance with white space

  • used to group elements
  • There is no need to completely fill space. Use empty space as a design element.

Typography


Things to evaluate Typography

  • Is it READABLE?
  • Is it Aesthetically appealing?

Before diving into other concepts, a number of terms needed to be understood first :

Text = what you typed
Character = a numerical code that represents the character
Glyph = a visual symbol that represents a character
Character encoding = code matches to the Glyph
Style = Italic, Bold or so
Font = Digital files of all Glyph of geometry of font
Typeface = the set of font (often misunderstood as font)
Typography = An art of text (READABLE & AESTHETIC)

2 groups of typefaces

  1. Sans Serif = traditional
  1. Serif = Modern
 

3. Attribute types of text characteristics

Categorical or Ordered
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How Many Typefaces?

• Generally use a single typeface, but vary weight, size, case, italic/regular, and colour to create a visual hierarchy / figure-ground. • If your really need, use a maximum of two typefaces, but make sure they go well together (this is difficult to get right!).
  • Generally combine one serif and one sans serif type family.

Tips to improve visualisation

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Alignment

Hierarchy

  • Important notes can be bold.
  • Other text and colour-coded annotations can use a normal font, and special terms can use italic style.
    • notion image
Style good for visualisation
Style good for visualisation

Week 5 Idioms for networks and trees


How to Arrange Networks and Trees

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Using Gestalt : Connection

Node-link diagram

Force-directed graph drawing is an algorithm to make the node-link diagram more visually appealing.
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Alluvial diagram

A series of stack bar connected with curve lines
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Sankey diagram

Nodes can be placed in anywhere
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Chord diagram

Shows flows between multiple nodes, in which they are arranged in a circleChord Diagram with Bundling
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Chord Diagram with Bundling
Chord Diagram with Bundling

Using Gestalt : Enclosure

Shows hierarchical relationship
hierarchical relationships with multiple categories,
Good for medium size network
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Overall
Excel Version
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Week 6


ISOTYPE research paper

Techniques for repeating things


Periodic line chart
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OVERLAYING TIME FRAMES
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AGGREGATION
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SMALL MULTIPLES
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ANIMATION
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Week 7


▪ Use Projection Wizard to select a map projection

Map Projections

Poles, Meridians, Parallels

Terms
赤道 (地球一半): Equator
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Longitude and Latitude

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Map Projections for Smaller Areas

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Developable surfaces
cylinder: cylindrical projection cone: conic projection plane: azimuthal projection

Map Projection Distortion

  • The relative area of objects or/and angles are distorted.

There are 2 types of projection


1. Mercator

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Example of an angle-preserving / conformal projection.
Advantage
  • Use for naval navigation, where bearings are measured on a map showing a small section of the world.
Disadvantages
  • Area is hugely inflated towards the poles.
  • Not useful for showing the entire world.

2. Map Projections for World Maps

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Example of an area-preserving (or equal-area) projection.
Advantage
  • Useful for showing the entire world.
  • Useful when the size of areas is compared.
Disadvantages
  • Angles (and shapes) are increasingly distorted towards the border of the map.
Types Map Projections for World Maps
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Map Idioms


Dot maps

Alternatives
– choropleth or bin map by counting points per area – convert to scalar field and use isocontours or colour mapping
  • Don't work well with strongly varying density

Proportional symbol maps

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Design Principles for PSM
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Choropleth Maps

Find correlation, trends, outliers
Find correlation, trends, outliers
Color tips for quantitative attributes
  • Primarily luminance changes: the greater the value, the darker.
  • Slight change in hue is possible, to increase the number of distinguishable colours. Change hue for diverging distributions.
  • Change in saturation is possible, but should not be main variable.
Delusion
pop VS pop.density
pop VS pop.density
Granularity
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Rmb to NORMALISE data!

Bin maps

Find correlation, trends, outliers
Find correlation, trends, outliers

Week 8 - Visualisation of geographic fields

Isolines or Contour Lines

Isolines or Contour Lines
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Isocontours: contour interval
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Isocontours = contour lines = isopleth (interval)
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Comparison of all mapping of scalar field/terrain
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Week 10 - Animation for Data Visualisation and Tool for Creating Visualisations

“Overview first, zoom and filter, then details-on-demand.” - Ben Shneiderman (1996)


Animation for data visualisation

Latin animare = “to bring life”
Sequences of static graphic depiction (frames), the graphic content of which, when shown in rapid succession, begins moving in a fluid motion.
There are 2 types of animation :

Temporal

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Non-temporal

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A good vid about transition

https://www.youtube.com/watch?v=vLk7mlAtEXI

Potential Pitfalls of Animated Visualisations

  • Directing user attention
    • notion image
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  • change blindness
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  • Difficulty in detecting changes in scenes.
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  • cognitive load
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  • length of animation (running time)

 

Week 11 - Immersive Data Visualisation


Mixed reality

Mixed reality continuum: real world, AR, VR
Mixed reality continuum: real world, AR, VR
AR with headsets and mobile devices
AR with headsets and mobile devices

 

VR for immersive visualisation

ImAxes

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Multiview maps

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Monash Immersive Analytics Lab

 
 
 
 
 
Dunno
FAQ
W1

Difference between Table and Field
difference between color channels
Which Channel share the same with Identity and Magnitude channels?
There is a channel that appears in both of the two ranked lists of channels:
position.
In the magnitude channels, we have "position on a common scale", or "position on an unaligned scale". In the identity channels, we have "spatial region".
They are actually all related to the "spatial position" of the graph, which means - spatial position is the only channel that is both a magnitude channel and an identity channel, also it is the most effective one.
How to identify the type of dataset
Your example - "For example, network can be represented as an adjacency matrix, or a field can be represented has a high-dimension table etc. ? If so how do we definitively distinguish among them?"
  • -----
It is not based on how we store the data or how we represent the data to distinguish the dataset types. We should consider the structure of the data.
Why not the format?
  • 1) Most of the dataset types can be possibly stored in a table or in a rational database - e.g., a network dataset can be stored as two variables: "user_1", "user_2". Each row of data with two users means they are connected.
  • 2) A dataset stored in a json or xml format could also be table data.
Why not the representation?
  • for example, the adjacency matrix you mentioned is basically a 2D heatmap. So a heatmap could possibly be used to represent a table dataset or a network dataset.
So how to differentiate the data types?
  • you need to first understand the data types: items, attributes, like, positions, and grids.
  • then based on what data types are in the dataset, you can decide the dataset type: e.g., if there is a link between the items in the dataset, then this might be a network or tree dataset; if there are geo-positions, then this might be a geometry or a field dataset.
What are Fields, spatial fields and grid
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Grid is related to cells:
Suppose that we want to show the distribution of the NBA shot locations - shown below. To get this, we actually need to first divide the space into a set of cells (either square cells - grid or hexagon cells); then we can get statistics of how many players have made the shots inside each cell.
Fields and spatial fields:
they are the name of the dataset that is based on cells.
What is part-to-whole relation (in pie chart) ?
part-to-whole relation is about - these four types of ingredients belong to the pancake
 
 
 
The Nothingness of Money - By Lawrence YeoHow to develop the right taxonomy for your UX research repository

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Jason Siu
A warm welcome! I am a tech enthusiast who loves sharing my passion for learning and self-discovery through my website.
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