SLIDE 1
INFORMATION VISUALIZATION
Alvitta Ottley Washington University in St. Louis CSE 557A | Sep 26, 2016
Slide Credits: Mariah Meyer, University of Utah Remco Chang, Tufts University
HEIDELBERG LAUREATE FORUM
INFORMATION VISUALIZATION Alvitta Ottley Washington University in - - PowerPoint PPT Presentation
INFORMATION VISUALIZATION Alvitta Ottley Washington University in - - PowerPoint PPT Presentation
CSE 557A | Sep 26, 2016 INFORMATION VISUALIZATION Alvitta Ottley Washington University in St. Louis Slide Credits: Mariah Meyer, University of Utah Remco Chang, Tufts University HEIDELBERG LAUREATE FORUM CSE 557A: INFORMATION VISUALIZATION 1
SLIDE 2
SLIDE 3
SLIDE 4
SLIDE 5
SLIDE 6
SLIDE 7
CSE 557A: INFORMATION VISUALIZATION 1 Assignment 1: Bar and Line Chart Due: 09-28-2016, 11:59pm (midnight) In this assignment, you will be using Processing to draw a bar chart and a line chart. This is your first assignment with Processing and you will be learning the basics of Processing such as, handling mouse events, basic intersection detection, and keeping track of the state of the
- visualization. You will be required to display a visualization based on input data, and there
are a few concepts that you need to explore: (1) reading and parsing data; (2) mouse hovering for highlighting visual elements. Basic Requirements:
- 1. You will be given a simple comma delimited file (CSV) called “data.csv”. This file will have
the following properties:
- a. It has two columns, the first column contains categorical (ordinal) data, and the
second column contains quantitative data.
- b. The first row has labels for each column.
- c. There are around 10 rows of data.
- 2. In Processing, do the following:
- a. Parse the CSV file and read in the data
- b. The canvas should display the data in the CSV file as a bar chart as default. The bar
chart should display each data item as a bar. c. The axes need to be labeled using the labels from the first row of the CSV file. The X- axis should display the categorical data and the Y-axis should display the as “(Apple, 12)”, showing both x be “cut off”.
Due on Wednesday
SLIDE 8
THE LERP FUNCTION
SLIDE 9
Recap…
Data Types Data Mapping
SLIDE 10
WHAT IS A DATA VISUALIZATION?
- A mapping of data attributes to visual attributes
- What are data attributes?
- What are visual attributes?
SLIDE 11
DATA DEFINITION
- A typical dataset in visualization consists of n records
- (r1, r2, r3, … , rn)
- Each record ri consists of m (m >=1) observations or variables
- (v1, v2, v3, … , vm)
- A variable may be either independent or dependent
- Independent variable (iv) is not controlled or affected by another variable
- For example, time in a time-series dataset
- Dependent variable (dv) is affected by a variation in one or more associated
independent variables
- For example, temperature in a region
- Formal definition:
- ri = (iv1, iv2, iv3, … , ivmi , dv1, dv2, dv3, … , dvmd)
- where m = mi + md
SLIDE 12
DATA TYPE TAXONOMY
- 1-D
- 2-D
- 3-D
- T
emporal
- Multi-dimensional
- Tree
- Network
The Eyes Have It, Shneiderman 1996
SLIDE 13
IS THIS COMPLETE?
SLIDE 14
BASIC DATA ATTRIBUTES
- Nominal
- Ordinal
- Scale / Quantitative
- Interval
- Ratio
SLIDE 15
BASIC DATA ATTRIBUTES (FORMAL)
- Nominal (N)
{…}
- Ordinal (O)
<…>
- Scale / Quantitative (Q)
[…]
- Q → O
- [0, 100] → <F, D, C, B, A>
- O → N
- <F, D, C, B, A> → {C, B, F, D, A}
- N → O (??)
- {John, Mike, Bob} → <Bob, John, Mike>
- {red, green, blue} → <blue, green, red>??
- O → Q (??)
- Hashing?
- Bob + John = ??
Readings in Information Visualization: Using Vision To Think. Card, Mackinglay, Schneiderman, 1999
SLIDE 16
OPERATIONS ON BASIC DATA ATTRIBUTES
- What are the operations that we can perform on these data types?
- Nominal (N)
- = and ≠
- Ordinal (O)
- >, <, ≥, ≤
- Scale / Quantitative (Q)
- everything else (+, -, *, /, etc.)
SLIDE 17
ANY QUESTIONS?
Slide courtesy of Mariah Meyer
Summary
SLIDE 18
Today…
Visual Attributes Data Mapping
SLIDE 19
SLIDE 20
SLIDE 21
SLIDE 22
How many times is height encoded?
SLIDE 23
Multiple encodings:
1. Height of the left line 2. Height of the right line 3. Height of shading 4. Position of top horizontal line 5. Position (placement) of the number 6. Value of the number
SLIDE 24
ACROSS CHARTS
Name Price Apple 12 Pear 4 Orange 13 Cherry 13 Blueberry 8 Banana 2 Peach 13 Lemon 5 Watermelon 15 Lime 24 Mango 16 Grape 10 Kiwi 1 Pineapple 15 Date 110
20 40 60 80 100 120 20 40 60 80 100 120
SLIDE 25
ACROSS CHARTS
Name Price Apple 12 Pear 4 Orange 13 Cherry 13 Blueberry 8 Banana 2 Peach 13 Lemon 5 Watermelon 15 Lime 24 Mango 16 Grape 10 Kiwi 1 Pineapple 15 Date 110
20 40 60 80 100 120 20 40 60 80 100 120
Never ever use a line graph for categorical data
SLIDE 26
IN CLASS EXERCISE…
SLIDE 27
why is this animation bad?
SLIDE 28
OTHER WAYS TO REPRESENT 2D DATA
Price
Apple Pear Orange Cherry Blueberry Banana Peach Lemon Watermelon Lime Mango Grape Kiwi Pineapple Date
Price
Apple Pear Orange Cherry Blueberry Banana Peach Lemon Watermelon Lime Mango Grape Kiwi Pineapple Date 20 40 60 80 100 120 Apple Pear Orange Cherry Blueberry Banana Peach Lemon Watermelon Lime Mango Grape Kiwi Pineapple Date
Price
SLIDE 29
Types of marks & channels
SLIDE 30
SLIDE 31
SLIDE 32
identify (what and where) magnitude (how much)
SLIDE 33
identify (what and where) magnitude (how much)
SLIDE 34
identify (what and where) magnitude (how much)
SLIDE 35
Expressiveness & effectiveness
SLIDE 36
expressiveness
SLIDE 37
magnitude (how much) identify (what or where)
expressiveness
SLIDE 38
SLIDE 39
Where do the ranking come from?
SLIDE 40
Bertin, 1967
SLIDE 41
SLIDE 42
SLIDE 43
SLIDE 44
SLIDE 45
SLIDE 46
SLIDE 47
encoding semantics
Ware 2010
SLIDE 48
NEXT TIME…
REQUIRED READING
SLIDE 49