Data Types, Tasks, Visual Encodings CS 7250 S PRING 2020 Prof. - - PowerPoint PPT Presentation

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Data Types, Tasks, Visual Encodings CS 7250 S PRING 2020 Prof. - - PowerPoint PPT Presentation

Aug 13, 2022 293 likes •969 views

Data Types, Tasks, Visual Encodings CS 7250 S PRING 2020 Prof. Cody Dunne N ORTHEASTERN U NIVERSITY Slides and inspiration from Michelle Borkin, Krzysztof Gajos, Hanspeter Pfister, 1 Miriah Meyer, Jonathan Schwabish, and David Sprague R EADING

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Data Types, Tasks, Visual Encodings

CS 7250 SPRING 2020

  • Prof. Cody Dunne

NORTHEASTERN UNIVERSITY

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Slides and inspiration from Michelle Borkin, Krzysztof Gajos, Hanspeter Pfister, Miriah Meyer, Jonathan Schwabish, and David Sprague

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READING QUIZ

8 min

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QUESTIONS?

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PREVIOUSLY, ON CS 7250…

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Visualization Building Blocks

Channels :

Note: these are all really important concepts when it comes time to coding your visualizations...!

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Expressiveness and Effectiveness

Expressiveness principle: the visual encoding should express all

  • f, and only, the information in the

dataset attributes. (i.e., data characteristics should match the channel)

Mackinlay (1986)

Effectiveness principle: the importance of the attribute should match the salience of the channel; that is, its noticeability. (i.e., encode most important attributes with highest ranked channels)

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DATASET = collection of information that is the target of analysis

Data Types

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NOW, ON CS 7250…

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Analysis

What data is shown? Why is the user analyzing / viewing it? How is the data presented?

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Analysis

DATA ABSTRACTION TASK ABSTRACTION VISUAL ENCODING

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Analysis

DATA ABSTRACTION TASK ABSTRACTION VISUAL ENCODING

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GOALS FOR TODAY

  • Learn what are data types and dataset types
  • Learn what are attribute types
  • Learn how to pick appropriate visual representations

based on attribute type and perceptual properties

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DATASET = collection of information that is the target of analysis

Data Types

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Data Types

DATASET = collection of information that is the target of analysis

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Attribute Types

(continuous)

e.g., fruit (apple, pear, grape), colleges (CAMD, CCIS, COE) e.g., sizes (xs, s, m, l, xl), months (J, F, M) e.g., lengths (1’, 2.5’, 5’), population

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http://www.nytimes.com/interactive/2016/09/12/science/earth/ocean-warming-climate- change.html

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Quantitative Quantitative Categorical

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https://xkcd.com/388/

Ordinal Ordinal Categorical

Note: On could also argue that Difficulty and Tastiness could be quantitative (continuous)

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Categorical Quantitative

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(Categorical)

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Mackinlay (1986)

Channel Ranking by Data Type

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Mackinlay (1986)

Channel Ranking by Data Type

(Categorical)

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Mackinlay (1986)

Channel Ranking by Data Type

AREA

Quantitative Ordinal Categorical

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Mackinlay (1986)

Channel Ranking by Data Type

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DATA ABSTRACTION

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Analysis

DATA ABSTRACTION TASK ABSTRACTION VISUAL ENCODING

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GOALS FOR TODAY

  • Learn what “Tasks” are and why they are so

important.

  • Learn the differences between high, mid, and low

level task classifications.

  • Begin practicing how to classify tasks (key step in

visualization design process!).

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TASK ABSTRACTION

Why abstract?

Avoids domain specific terms thus easier to apply to

  • ther cases (broadly

applicable results).

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TASK ABSTRACTION

Why abstract?

Avoids domain specific terms thus easier to apply to

  • ther cases (broadly

applicable results).

Visualization Tools

Specific General Altair

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TASK ABSTRACTION

Why abstract?

Avoids domain specific terms thus easier to apply to

  • ther cases (broadly

applicable results).

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ACTIONS define user goals. High-level

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ACTIONS define user goals.

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ACTIONS define user goals. Mid-level

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What is the address of Ryder hall?

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Where is Ryder Hall?

Ryder Hall

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What buildings are near Ryder Hall?

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What is south of Huntington Ave?

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ACTIONS define user goals. single target multiple targets all targets Low-level

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TARGETS are aspects of the data interest that are interest to the user.

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ACTIONS define user goals. High-level Mid-level Low-level Lots of other task taxonomies...!

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Analytic Task Taxonomy

Amar et al., 2005

Retrieve Value Filter Compute Derived Value Find Extremum Sort Determine Range Characterize Distribution Find Anomalies Cluster Correlate

How long is the movie Gone with the Wind? What comedies have won awards? How many awards have MGM studio won in total? What director/film has won the most awards? Rank movies by most number of awards. What is the range of film lengths? What is the age distribution of actors?

Are there exceptions to the relationship between number of awards won and total movies made by an actor?

Is there a cluster of typical film lengths? Is there a trend of increasing film length over the years?

Low-level

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AN EXAMPLE OF TASK ANALYSIS → VISUALIZATION DESIGN

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Zhang et al., 2018

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During a type 1 diabetes clinical visit with a Certified Diabetes Educator… Hierarchical Task Analysis Task Abstraction Design

Zhang et al., 2018 44

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Zhang et al., 2018 45

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Zhang et al., 2018 46

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  • DR1. Composite Visualization of Integrated Data
  • DR2. Visualization of Folded Temporal Data
  • DR3. Align and Scale Temporal Data
  • DR4. Summary Statistics

Design Requirements

Hierarchical Task Analysis Task Abstraction Design

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Hierarchical Task Analysis Task Abstraction Design

14-Day Overview Detail View Summary Statistics Panel

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IN-CLASS EXERCISE: MOCK INTERVIEW, TASK ANALYSIS

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Interview Advice

  • Have a designated note-taker and designated leader
  • Be prepared. (Have some questions prepared in advance.)
  • Start slow, safe, and personal.
  • Coax, don’t hammer.
  • Make some questions open ended.
  • Ask what you don’t know.
  • Let the interviewees wander a bit–but be careful.
  • Listen, really listen.
  • For software, look for “work arounds” and hacks.
  • Make sure to write down your thoughts and impressions immediately

after the interview.

  • You are the visualization expert – don’t ask them what vis they want,

don’t think too early about what vis to build.

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www.forbes.com/sites/shelisrael/2012/04/14/8-tips-on-conducting-great-interviews/+&cd=3&hl=en&ct=clnk&gl=us

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Task Analysis

  • Break-out into groups of ~3 people.
  • Pretend you are transportation engineers, e.g., for

the MBTA, City of Boston.

  • Discuss the “domain tasks” and classify the tasks.
  • Save your notes for a later exercise!!!

INSTRUCTIONS:

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High-level Low-level Mid-level Low-level

Visualization for Public Transit Development 15m

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Analysis

DATA ABSTRACTION TASK ABSTRACTION VISUAL ENCODING

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GOALS FOR TODAY

  • Learn about visual encodings, esp. arranging tables
  • Learn how to pick appropriate visual representations

based on attribute type and perceptual properties

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VISUAL ENCODING

Now… Later this semester...

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Visualization Building Blocks

Channels: Marks:

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Mackinlay (1986) Munzner’s VAD

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IN-CLASS EXERCISE: ENCODINGS WORKSHEET

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Encoding Match-up

Area Chart Bubble Chart Sector Graph Waterfall Chart Grouped Bar Chart

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Encoding Match-up

Star Plot Venn Diagram Box & Whisker Plot Heat Map Parallel Coordinates

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Arrange Tables

Key: an independent attribute that can be used as a unique index (Tableau Dimension) Value: a dependent attribute (i.e., cell in a table) (Tableau Measures) Categorical or Ordinal Categorical Ordinal, or Quantitative

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Date Precipitation High Temperature May 1, 2016 0” 60 May 2, 2016 0.3” 62 May 3, 2016 1” 55 May 4, 2016 0” 67 Student College HW1 grade (out

  • f 10)

John COS 9 Jane Khoury 10 June Khoury 8 Joe Khoury 8

Example Keys

Key

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Arrange Tables - no key

SCATTER PLOT

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Arrange Tables - one key

BAR CHART LINE GRAPH

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Arrange Tables - two keys

HEATMAP STACKED BAR CHART

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https://bost.ocks.org/mike/miserables/

Arrange Tables - Two Keys (Network)

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http://higlass.io/

Arrange Tables - Two Keys (Network)