I590 Interactive Visual Analytics Week 5 | Sep 21, 2016 Data - - PowerPoint PPT Presentation

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I590 Interactive Visual Analytics Week 5 | Sep 21, 2016 Data - - PowerPoint PPT Presentation

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I590 Interactive Visual Analytics Week 5 | Sep 21, 2016 Data Abstraction Visual Encoding Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI Project 1 Due Tuesday, Sep 27 at 8:59 PM Turn in as online web pages: Video with

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Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI

Week 5 | Sep 21, 2016 Data Abstraction Visual Encoding

I590 Interactive Visual Analytics

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Project 1

Due Tuesday, Sep 27 at 8:59 PM Turn in as online web pages:

Interac>ve, live visualiza>on Design Document Video with narra>on

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Last 2 weeks

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  • C. Ware

Visual Thinking for Design

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Wandell, “Foundations of Vision”

Cones, Rods, Color vision

HyperPhysics, Georgia State University

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Simultaneous contrast

Via Colin Ware

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Wong 2010 Via Miriah Meyer

Simultaneous contrast

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POPOUT

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Visual percep>on Our visual system sees differences, not absolute values, and is aGracted to edges. We can easily see objects that are different in color and shape, or that are in mo5on (popouts) Use color and shape sparingly to make the important informa5on pop out

Last 2 weeks

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This week

  • Data abstrac>on
  • Datasets: Tables, Networks, Fields, Geometry.
  • Variable types: Categorical, Ordinal, and

Quan5ta5ve variables

  • Visual encoding
  • Marks and channels
  • Perceptual accuracy of channels
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Data abstraction

type vs. seman>cs

Transla5on of domain-specific terms into words that are as generic as possible

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Terminology

Dataset Type

collections of units

Data Type

fundamental units

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Data Types

  • Items: are individual units of data. For example: rows in a table, points
  • n a 5meline, nodes, …
  • AKribute: a property rela5ng to items, links, or posi5ons
  • Link: linkage between (typically) two items
  • Posi>on: designa5on of a point or a sub-space in a larger spa5al context
  • Grids: par55oning of space into typically uniform cells
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Dataset Types

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Via Miriah Meyer

Flat Tables

Key: Order ID

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Multidimensional Tables

Alex Lex

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Visualizing Tables

Example Parallel Coordinates

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Dataset Types

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Network / Graph items = people links = friendship

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Network / Graph

Matrix

Mike Bostock

Node-link diagram

Wikipedia

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Hierarchy/ Tree

items = states links = winning paths

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http://orig00.deviantart.net/7534/f/2008/105/2/0/2027d31d532a91b7324f30af187e0819.png

Hierarchy/ Tree

items = species links = evolu5onary ancestry

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http://orig00.deviantart.net/7534/f/2008/105/2/0/2027d31d532a91b7324f30af187e0819.png

items = zip codes links = containment

Hierarchy/ Tree

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Dataset Types

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Grid

position population density temperature attributes

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Fields

Scalar fields

aKribute = one value examples: temperature, eleva5on

Vector fields

aKribute = vector (direc>on) wind map

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Scalar + Vector fields

Fields

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Fields

Scalar fields

aKribute = one (real) value examples: one MRI scan slice (5ssue density)

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Based on a slide by Miriah Meyer

3D Fields

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Based on a slide by Miriah Meyer

grid = MRI scan slices aGributes = 5ssue density

3D Fields

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Visualizing 3D Fields

http://www.fovia.com/gallery/medical/mg_bs.jpg

http://www.dirkreiners.com/images/Research/tooth_tf.jpg

Volume Rendering

tissue density

  • pacity
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Grid Types

Slide by Alex Lex Wikipedia

Uniform Grids

Geometry & topology can be computed

Rec>linear Grid

Nonuniform sampling

Structured Grid Unstructured Grid

More flexibility, store posi5on and connec5on

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Dataset Types

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Geometry

World Atlas

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Geometry

http://farbackoutdoors.com/1593-2/

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Geometry

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Dataset Types

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Sets

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Clusters

Dynamic (5me varying) clusters Sta5c clusters

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Lists

Baseball salary vs. performance Ben Fry

LineUp

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Attribute/Variable Types

✦Categorical (Nominal, Qualita>ve)

A finite set of categories No implicit ordering between categories

✦Ordered

  • Ordinal

Implicit ordering between categories/levels, but no clear magnitude difference. Can compare and determine greater/less than

  • Quan>ta>ve

Meaningful magnitude Can do arithme5c

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Quan>ta>ve Data

Interval vs. Ordinal

✦ Interval

  • Zero does not indicate an absence of detectable measurement
  • We can determine distance between measurement, but not propor5ons
  • Example: temperature, dates

✦ Ra>o

  • The posi5on of zero indicates there is nothing of the measured en5ty
  • Can determine ra5o and propor5ons
  • Example: weight, age
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Derived Data

f(x) + g(x)

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Quiz

What aGribute/variable type (Categorical, Ordinal, Interval, or Ra5o) best fit the following measurements?

  • Speed
  • Facebook reac5ons (Like, Angry, Sad, etc…)
  • Car configura5ons (Compact, Mid-Sedan, SUV)
  • Product Name
  • IQ scores
  • College Majors
  • 50-meter race 5me

Based on a slide by Alex Lex

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Another way to think about Ordered aKributes

  • Sequen5al
  • Diverging
  • Cyclic

temperature eleva5on (above/ below sea level) 5me

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Colormap

categorical vs.

  • rdered

sequential vs. diverging discrete vs. continuous

Match colormap to data type & task

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Color design tools

Helpful to match variable/aGribute type to colormap

Color Brewer HCL Picker

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Project 1 Q&A