---
# System prepended metadata

title: Data Visualization
tags: [DV]

---

# Data Visualization
NTNU 資料視覺化
##### [Back to Note Overview](https://hackmd.io/@NTNUCSIE112/NTNUNote)

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###### tags: `DV`
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## Score

## Syllabus
- 0908
    - 00_Introduction(Syllabus)
- 0915
    - D3
        - 01 Before D3
    - VISUAL DESIGN
        - 01_Introduction
        - 02_WhatWhyHow
    - SCIVIS
        - 01 Scivis Introduction
        - 02 BasicVisAndParaview 
- 0922
    - D3
        - 02 Selecting & 03 Data Binding檔案
    - VISUAL DESIGN
        - 03 MarkChannel

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    - D3
        - xxx
    - VISUAL DESIGN
        - xxx
    - SCIVIS
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## Visual Design Note

### 02 What, Why, How

### 03 Mark Channel
- Visual Encoding
- Marks
- Channels
    - Magnitude
        - Ordered Attributes
    - Identify
        - Categorical Attributes
- Seoarability
- Popout
- Grouping
    - containment
    - connection
    - identity channels


## 04 Color
![](https://i.imgur.com/iFPWdN3.png)

![](https://i.imgur.com/mEatR2k.png)


## SCIVIS Note
- ![](https://i.imgur.com/CmNTwXl.png)
    1. yes
    2. yes
    3. yes
    4. no
- ![](https://i.imgur.com/M9hPeLY.png)
    - In some local space region, data values change a lot. But, data values in some other local regions do not.
    - more accurately represent the dataset values
    - ~~By using complex grid structure (e.g. curvlinear grid), It allows us not to store all data attributes (e.g. temperature, pressure...) at all grid points~~
    - Save storage
    
    
- S02-04: The user provides a dataset with the longitude and latitude of found meteorite. The user is interested in knowing which country has been hit most time by meteorites. What "actions" may need to complete the tasks? 

- S04-01: If you know values at vertices A, B, and C, which one is the correct equation to calculate the value at P (by interpolation)?![](https://i.imgur.com/POGkq1C.png)
- S04-02: Calculate the value at P by interpolation?
    ![](https://i.imgur.com/EcEOkfG.png)


### 1027
- S07-03: Which statements are correct?
    - [x] Adjacency matrix can represent both network and tree structure dataset
    - [x] The order of row and column matters when using adjacency matrix
    - [ ] Treemap can represent network dataset
    - [x] Treemap cannot show the intermediate nodes
    - [ ] Adjacency matrix is good for path tracing in a network data
    - [ ] Node-link diagram has better scalability than adjacency matrix

### 1103
- S08-01: What are the differences between figures in each pair (from A and B)? The dataset in each pair is the same.![](https://i.imgur.com/kUjdOi4.png)    
    - (A) order of marks (B) alignment point
- S08-02: order user's expected response time of each action from short to long?
    1. mouse over a mark and a tooltip popout
    2. click on a mark to popout small window for detailed information
    3. input a number in a dialog to update views in the visual tool

#### SciVis
- S08-01: We use the two transfer function to render the two images. Can you guess, the image at right-hand side is rendered by which transfer function (top or bottom?)![](https://i.imgur.com/pevU1IX.png)
    - bottom
- S08-02: Match A B C D E F by the order of 0 1 2 3 4 5 ![](https://i.imgur.com/i9GL48R.png)
    - A: air
    - B: tissue
    - C: bone
    - D: aie and tissue boundary
    - E: tissue and bone boundary
    - F: air and bone boundary
### 1110
#### SciVis
- S10-1: Match them by (A) (B) and (C) order. (each black curve is a path)
    ![](https://i.imgur.com/DgzIIaz.png)
    - (A) streakline
    - (B) timeline
    - (C) pathline
- S10-02: If we have the a following vector field and release a particle at position [0.5, 0.5]. Use Euler's Method to trace the particle for 2 steps (set delta t to 1), answer the position after two steps. Red: vector on the grid point. Black: coordinate
    ![](https://i.imgur.com/AgbUww7.png)\]
### 1124
![](https://i.imgur.com/dCnd02b.png)
## Note（Midterm）
- VisualDesign: from the first week to 08_Manipulate
- SciVis: from the first week to 10_VectorFieldVis-NumericalIntegration



### Visual Design
#### 02
what 前面那個
what 
attribute type
分類可能有 應該是會有

//
02 07 loop which pro
02 16 data abs
02 26 which is what
#### 03
mark
頻道
p12 channel
14
19
popout
group
point

//
03 05~12 12's table
03 14
03 separ popout group
#### 04
2/3 color
8
11 color map
12  reason poor 
23
25 color map fit 

//

04 03 three part of color
04 08 match
04 11 color map reason
04 12 why rainbow poor
04 15 what is dis
04 20, 21
04 23 interaction
04 25 color map fit
#### 05
read table fit what chart
//
05 how to readm pro&cons
#### 06
12
13/14
25
//
06 same as 05
#### 07
1/2/3 pros and cons
5 pros and cons
8 edge cross
36 tree map
//
07 same as 05


#### 08
4 what 
12 animatd transition what need 
21 responsiveness 
//
08 same as 05, why animation, responsiveness
### SciVis
#### 01
01 what data model look like, grid cell
01 14 struct grid pro&cons
01 20 different type

#### 02

02 how to compute 
10 
#### 03
4 
5 what is var cell ...
14 pros and cons
//
03 how to compute, no hydr blablabla


#### 04
要會算內插
#### 05
要會算 isovalue
應該不會要算isosurface
不會有什麼model那個
#### 06

#### 07
Back to front 
front to back

#### 08
猜transfer function 的代表
17
20
// 
08 transfer function, how to read 2d hist, boundry

#### 09
四種line
算path
//
09 vector field, ?
#### 10
22
115
14
