INFORMATION VISUALIZATION

Transcription

1 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

2 HEIDELBERG LAUREATE FORUM

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7 Assignment 1: Bar and Line Chart Due: , 11:59pm (midnight) CSE 557A: INFORMATION VISUALIZATION 1 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. Due on Wednesday 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

8 THE LERP FUNCTION

9 Recap Data Types Data Mapping

10 WHAT IS A DATA VISUALIZATION? A mapping of data attributes to visual attributes What are data attributes? What are visual attributes?

11 DATA DEFINITION A typical dataset in visualization consists of n records (r 1, r 2, r 3,, r n ) Each record r i consists of m (m >=1) observations or variables (v 1, v 2, v 3,, v m ) 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: r i = (iv 1, iv 2, iv 3,, iv mi, dv 1, dv 2, dv 3,, dv md ) where m = m i + m d

12 DATA TYPE TAXONOMY 1-D 2-D 3-D Temporal Multi-dimensional Tree Network The Eyes Have It, Shneiderman 1996

13 IS THIS COMPLETE?

14 BASIC DATA ATTRIBUTES Nominal Ordinal Scale / Quantitative Interval Ratio

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, 199

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.)

17 Summary ANY QUESTIONS? Slide courtesy of Mariah Meyer

18 Today Visual Attributes Data Mapping

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22 How many times is height encoded?

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

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

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 Never ever use a line graph for categorical data 0

26 IN CLASS EXERCISE

27 why is this animation bad?

28 OTHER WAYS TO REPRESENT 2D DATA Price Price Price Pineapple Kiwi Grape Date 120 Apple Pear Orange Cherry Blueberry Mango Banana Apple Pear Orange Cherry Blueberry Banana Peach Lemon Watermelon Lime Apple Pear Orange Cherry Blueberry Banana Peach Lemon Watermelon Lime Lime Watermelon Lemon Peach Mango Grape Kiwi Pineapple Date Mango Grape Kiwi Pineapple Date

29 Types of marks & channels

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32 identify (what and where) magnitude (how much)

33 identify (what and where) magnitude (how much)

34 identify (what and where) magnitude (how much)

35 Expressiveness & effectiveness

36 expressiveness

37 magnitude (how much) identify (what or where) expressiveness

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39 Where do the ranking come from?

40 Bertin, 1967

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47 encoding semantics Ware 2010

48 NEXT TIME REQUIRED READING

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