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  lOMoAR cPSD| 23136115 Perception 
1. Perception VS Cognition    - Perception   
+ Identification and interpretation of sensory information   
+ From the physical stimulus to recognizing information 
+ Shaped by learning, memory, expectation -   Cognition   
+ The processing of information, applying knowledge   Perception  Cognition  Eye, optical nerve, visual  Recognizing objects  cortex  Relations between objects  Basic perception  Conclusion drawing  First processing  Problem solving  Not conscious  Learning, …  Reflexes 
2. Dimensions: Hue - Saturation - Value   Hue  Saturation  Value 
color value expressed as a + The purity of a color 
+ Lightness or darkness of a color  number from 0 to 360 
+ Pigments: no white/black is added + the intensity of light 
+ Light: what is the ratio of dominant  wavelength to others 
Other color models: RGB, HSL, … 
3. Paint mixing: light mixing (additive) VS ink mixing  
(subtractive) VS paint mixing (subtractive - wavelength)  Paint mixing  Ink mixing  Light mixing 
paints+ Subtractive mix of transparent + Additive mix of colored lights 
+ Physical mixing of opaque inks  + Primary: Red, Green, Blue 
+ Primary: red, yellow, blue + + Primary: cyan, magenta, yellow
+ Secondary: Cyan, Magenta, Yellow Secondary (mixed): green,      + Secondary: RGB + Additive model orange, purple      
+ Subtractive model - absorb + Approx. black = C+M+Y  wavelength  + True black = C+M+Y+K +    Subtractive model 
4. Colormap: categorical vs ordered, sequential vs diverging, 
segmented vs continuous, univariate vs bivariate  a. Categorical vs. Ordered:      lOMoAR cPSD| 23136115
Categorical Colormap: Used for representing categorical data where each category is 
assigned a distinct color. These colormaps have discrete color categories without any 
inherent ordering. (phân loại, mà riêng biệt) 
Ordered Colormap: Represents data that has an inherent order, such as ordinal or 
sequential data. Colors in an ordered colormap are usual y arranged in a meaningful 
sequence. (số đếm, sử dụng thang màu)  b. Sequential vs. Diverging: 
Sequential Colormap: Progresses through a range of colors from low to high values, 
typically used for representing data that has a natural progression, like temperature or 
elevation. (luôn tăng,vd 1234, cái sau màu đậm hơn cái trc) 
Diverging Colormap: Contains two different colors, each representing opposite extremes 
with a neutral color in the middle. Diverging colormaps are often used to highlight 
deviations from a midpoint or to emphasize positive and negative deviations from a central 
value. (bắt đầu từ 1 cực)      Sequential  Diverging  c. Segmented vs. Continuous: 
Segmented Colormap: Comprises distinct, non-continuous color segments. Each 
segment may represent a different range or category. (giá trị tách biệt) 
Continuous Colormap: Gradual y changes in color across its range without any distinct 
breaks or segments. Continuous colormaps are commonly used for representing smooth  transitions in data.  d. Univariate vs. Bivariate: 
Univariate Colormap: Represents variation in a single variable. For example, a colormap 
might represent different shades of blue to indicate varying temperatures. (1 biến) 
Bivariate Colormap: Represents variation in two variables simultaneously. Bivariate 
colormaps are often used in heatmaps where both rows and columns are assigned colors 
based on their respective values. (2 biến) 
5. Qualitative data vs. quantitative data (value, saturation, not hue)  Qualitative data  Quantitative data 
Qualitative data are data representing 
Quantitative data are data represented 
information and concepts that are not 
numerical y, including anything that can be  represented by numbers. 
counted, measured, or given a numerical value.  6. Bin or not to bin  
Number of bin: log2(n) (n: Range)      lOMoAR cPSD| 23136115
Clearer Pictures: Binning makes graphs smoother, so it's easier to spot trends. Loses 
Some Details: Binning might smooth things out too much, hiding little differences in the  data. 
7. Caution to color blindness: red-green weakness/blindness  NOTION  10% of males, 1% of females 
Most common: red-green weakness / blindness 
Reason: lack of medium or long wavelength receptors, or altered spectral sensitivity  (most common: green shift) 
8. Color is relative to brightness contrast 
The perceived brightness of an object is relative to it‘s background  Pop out 
1. Difference in hue/curvature  Hue Pop Out  Curvature Pop Out 
This occurs when an object stands This happens when an object 
out from its surroundings due to a stands out because of its shape or  difference in color. 
curvature, rather than its color.  Our  eyes  are  sensitive 
to Even if all objects have the same 
differences in color, so objects with color, the one with a unique shape 
distinct hues tend to grab our will catch our eye.  attention more easily. 
2. Task: target detection, boundary detection, region tracking,  counting/estimation  - Target detection   
+ Detect the presence or absence of a target  - Boundary detection 
+ Detect a texture boundary between two groups of elements, where are 
of the elements in each group have a common visual property  - Region tracking 
+ Track one or more elements with a unique visual feature as they move  in time and space  - Counting and estimation 
+ Users count or estimate the number of elements with a unique visual  feature      lOMoAR cPSD| 23136115
3. To find meaning in what we see, we must selectively pay 
attention to what is important 
Vd trong bar chart yếu tố độ cao quan trọng hơn màu sắc, chú ý ~ điểm qtrọng 
để nhận biết ý nghĩa của hình 
4. Gestalt principles:  
patterns that transcend the visual stimuli that produced them, 
grouping/linking by placing entities in close proximity, Co modulation of a 
channel color, shape, size, value, orientation, texture  Data  1. Dataset types:     - Table    Flat table 
Multi-dimensional table Collections  Visualize table      lOMoAR cPSD| 23136115 1 item per row, Indexing  based  on How we groups items:    each column is multiple keys  Sets  attribute, 
unique - Multiple keys: Items,  -  Unique  items, 
(implicity) key, no Store location, Customer, unordered  duplicates  Period  List  - Attribute: Quantity  -  duplicates  allowed  Clusters  -  Groups of similar  items  - Graphs/Network    + Set of nodes, set of edges    + Connecting these vertices  - Tree   
+ A tree is a graph with no cycles    - Fields   
+ Sets of attributes values associated with cells 
+ Cell contains data from continuous domain +   Grids types        lOMoAR cPSD| 23136115 - Geometry      2. Data unit types: 
- Items : individual entity, discrete 
- Attributes : measured, observed, logged property 
- Links : Express relationship between two items 
- Positions : Spatial data → location in 2D or 3D 
- Grids : Sampling strategy for continuous data 
3. Structured vs Unstructured vs Semi-structured data  Structure  Unstructured  Semi-structure  - Know data types, 
data khong duoi dang email, hinh anh  semantics (data  bang  dạng bảng)  4. InfoVis vs SciVis 
SciVis (cho 1 mảng khoa học) 
InfoVis (visualization thể hiện thông tin)      lOMoAR cPSD| 23136115 - 
It's about showing information -  It's about showing scientific 
in pictures to help people understand it stuff in pictures to help scientists  better.  understand it better.  - 
Deals with things like numbers, -  Deals with things like how 
trends, and how things are connected. things move, how they're shaped, and 
- Is used for things like making charts, how they behave.  graphs, and interactive maps.  - 
Is used for things like showing  - 
Use it in fields like business, how atoms look, how air flows, and 
science,and social studies to analyze how diseases spread.  data and find patterns.  -  Use in fields like physics, 
medicine, and weather forecasting to  study   
complex things and make discoveries.  5. Attribute types:  
- Categorical, quantitative (nominal vs ordinal vs interval vs ratio equals, not equals/ sign/ 
plus, minus/multiply, division) (file C3, slide 27)   
6. Sequential vs Diverging data  Sequential  - Homogeneous from min to max  Diverging 
- Two or multiple sequences that meet 
- Elevation dataset: above sea level & below sea level 
- Temperature of water: below or above freezing / boiling  Mark vs Channel 
1. Marks: represent items or links (points, lines, areas, connection, containment)      lOMoAR cPSD| 23136115      
2 . Channels : change appearance based 
on attribute = visual variable (position, 
color, shape, tilt, size, volume 3D)     Type s of Channels  *      * Rank of Channels      lOMoAR cPSD| 23136115  
4. Expressiveness principle: the visual encoding should express all of, and 
only, the information in the dataset attributes 
5. Effectiveness principle: the importance of the attribute should match the  salience of the channel 
6. Characteristics: selective, associative, quantitative, order, length   
7. Position: Strongest visual variables, Suitable for all data types 
8. Length, size: Good for 1D, OK for 2D, Bad for 3D (ratio) 
9. Luminance: OK for quantitative data when length & size are used. Not very  many shades are recognizable 
10. Color: Good for qualitative data (identity channel) 
11. Shape: Great to recognize many classes. No grouping, ordering  Design Guideline 
1. The visualization should show all of the data and only the data 
2. Use the best visual channel available for the most important aspect of the data      lOMoAR cPSD| 23136115
3. Show data variation, not design variation; Clear, detailed, and thorough labeling 
and appropriate scales; Size of the graphic effect should be directly proportional  to the numerical quantities 
4. Focus on LIE FACTOR, SCALE DISTORTION, ZERO-STARTING BASELINE 
(should start at minimum of data), FRAMING, BIASES, AGGREGATED CHARTS 
(chồng chéo), PIE CHARTS (difficult for comparison) 
Visualization Design Principles  1. Data-Ink ratio  2. Chart Junk 
- A term coined by data visualization expert Edward Tufte to refer to elements of a 
chart or graph that do not add value to the data being presented, but rather serve 
only to distract or confuse the viewer.  3. Alignment matter  4. Unjustified 3D 
Interaction ( tuong tac) WHY 
INTERACT WITH VISUALIZATION? 
Need to explore data that is big/complex 
Too much data Too many ways to show it 
Interaction ampli 昀椀 es cognition 
Understand things better if we can touch them When we can observe cause and  e 昀昀 ect  MANTRA 
Visual information seeking matra (Shneiderman, 1996) Overview 昀椀 rst, Zoom 
and 昀椀 lter, Then details on   demand  Related, history, extract     Views C7  WHY MULTIPLE VIEWS?  Eyes beat memory 
- Showing two views side by side are easier to compare thanchanging views over time 
No single visual encoding is optimal for all possible tasks 
- Use di 昀昀 erent encoding for one dataToo many to shown in one view  LINKED VIEWS OPTIONS  Four options      lOMoAR cPSD| 23136115
- Highlighting: to link, or not 
- Navigation: to share, or not 
- Encoding: same or multiform- Dataset: Shared all, subset, or none  TRELLIS PLOTS 
panel variables attributes encoded in individual views partitioning variables 
partitioning attributes assigned to columns and rows 
main-e 昀昀 ects ordering order partitioning variable based on derived data support 
perception of trends and structure in data  Story telling C9 
Good stories do more than provide facts and data - They situate and give context - They 
engage - They educate Who / What / Where Why / How 
Underscore your arguments with Data/Facts Leverage the power of Visualization - Show 
trends - Show correlations - Show outliers - Convey magnitudes 
Martini Glass structure start with author driven, open up for exploration Interactive 
slideshow Split into multiple scenes, allow interaction mid-way Dril -down story Let reader 
decide which path to follow, all paths are annotated  LAYOUT PRINCIPLE   Descriptive titles 
Descriptive subtitles ( phu de mieu ta)   Annotations (chu thich) 
Saturation ( phan hoa theo mau cua tung entity)  WHEN DO WE DESIGN? 
Wicked problems No clear problem definition Solutions are either good enough or not 
good enough Multiple solutions exist, not true/false No clear point to stop with a solution 
INTERNAL VS EXTERNAL VALIDITY 
Internal validity – can you trust your experiment 
High when tested under controlled lab conditions Observed e 昀昀 ects 
are due to the test conditions (and not random variables) 
External validity – is your experiment representative of real world usage 
High when interface is tested in the 昀椀 eld, e.g. handheld device  tested in museum 
Results are valid in real world The trade-o 昀昀 
The more akin to real-world situations, the more experiment is susceptible 
to uncontrolled sources of variation 
QUANTITATIVE VS QUALITATIVE EVALUATION  Quantitative methods 
Objective metrics, measurements Use      lOMoAR cPSD| 23136115
numbers/statistics for interpreting data  Qualitative methods  Subjective metrics 
Description of situations, events, people, interactions, and observed 
behaviors, the use of direct quotations from people about their 
experiences, attitudes, beliefs, and thoughts 
Focused on understanding how people make meaning of and experience  their environment or world  SCOPE OF EVALUATION  Pre-design 
To understand potential users’ work env. And work 昀氀 ow Design 
To scope a visual encoding and interaction design space based on 
human perception and cognition Prototype 
to see if a visualization has achieved its design goals, to see how a 
prototype compares with the current state- 
of-the-art systems or techniques  Deployment 
to see how a visualization in 昀氀 uences work 昀氀 ow and work 
processes, to assess the visualization’s e 昀昀 ectiveness and use in the  昀椀 eld  Re-design 
to improve a current design by identifying usability problems 
Exercise for Storytelling (chap 9)  Ex1