HOW TO VISUALIZE 1,001,091 *n : CARTOGRAPHIC CONSIDERATIONS WHEN ANIMATING FOR GEOVISUALIZATION
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HOW TO VISUALIZE 1,001,091 *n : CARTOGRAPHIC CONSIDERATIONS WHEN ANIMATING FOR GEOVISUALIZATION. 1,001,091. Jason Dykes Dept. of Information Science City University, London. Sanjay Rana Centre for Advanced Spatial Analysis University College London. a journey to …. rugely cannock lichfield

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Sanjay rana centre for advanced spatial analysis university college london

HOW TO VISUALIZE 1,001,091 *n : CARTOGRAPHIC CONSIDERATIONS WHEN ANIMATING FOR GEOVISUALIZATION

1,001,091

Jason DykesDept. of Information ScienceCity University, London

Sanjay RanaCentre for Advanced Spatial Analysis University College London


A journey to

a journey to …

  • rugely

  • cannock

  • lichfield

  • brownhills

  • tamworth

  • uttoxeter

  • stafford

staffordshire


Cartographic considerations

cartographic considerations

  • aggregation

    • input

    • output

  • cartography

    • aggregation

    • generalization

    • simplification

    • design

    • transformation

1,001,091


Cartographic considerations1

cartographic considerations

  • aggregation

    • input

    • output

  • cartography

    • aggregation

    • generalization

    • simplification

    • design

    • transformation

1,001,091


Cartographic considerations2

cartographic considerations

  • aggregation

    • input

    • output

  • cartography

    • aggregation

    • generalization

    • simplification

    • design

    • transformation

1,001,091


Cartographic considerations3

cartographic considerations

  • aggregation

    • input

    • output

  • cartography

    • aggregation

    • generalization

    • simplification

    • design

    • transformation

1,001,091


Cartographic considerations4

*n?

cartographic considerations

1000*1000


Animation what are we doing

animation - what are we doing?

problems : highlighting issues with animations for visualization

  • why do they arise?

  • what are requirements?

    proposals : presenting some solutions

  • parallels with more conventional established cartography

  • incorporating ideas from other fields

  • explaining why both might be useful

    prototype : implementing with an example :

  • demonstrating

  • requesting feedback

  • addressing the requirement for evaluation (?)


Animation visualization cartography

animation, visualization & cartography

  • Plenty of cartographic animation examples :

    • cartoon, flip-book, film, video, digital

  • Cognitive theory suggests good for :

    • interpreting subtle change

    • noticing distinct change

problems


Animation visualization cartography1

animation, visualization & cartography

  • Do they work?

    • Communication : Slocum et al. (1990) ; Kraak & Kouss' (1992)

    • Visualization : Dorling (1992)

  • Representations tend to use raw data series :

    • high frequency variation – outliers

    • movement (hardware - MacEachren, 1995)

    • Example : AIDS Data Animation Project

  • Digital formats not suited for visualization (interactivity)

    • Slocum (2000)

    • Ogao & Blok (2001)

problems


Animation learning

animation & learning

Morrison, Tversky & Betrancourt (2000)

“Animation : Does it facilitate learning?”

(Several problems and limitations identified)

  • Advances in technology of producing attractive graphics drive development of tools & devices rather than research on their utility.

  • Temporal interpolation will help interpretation by providing

    "details of the microsteps between larger steps"

  • Enquiry needed into the information processing of animations

    "The drawback of animation may not be the cognitive correspondences between the conceptual material and the visual situation but rather perceptual and cognitive limitations in processing a changing visual situation"

problems


1 smoothing reduce spatial noise

1. SMOOTHING : reduce spatial noise

  • Large data sets - visualization for large-scale variation :

    • Outliers easy to detect

    • General patterns harder to discern from raw data

  • Spatial smoothing : (addresses these issues spatially)

    • density estimation (Openshaw et al., 1992)

    • spatial generalization (Paddenburg & Wachowicz, 2001)

    • spatial filter (e.g. Herzog)

    • bivariate quadratic polynomial function

      (increases spatial autocorrelation)

proposals


2 smoothing reduce temporal noise

2. SMOOTHING : reduce temporal noise

  • Large data sets - visualization for large-scale variation :

    • Outliers easy to detect

    • General patterns harder to discern from raw data

  • Temporal smoothing : (addresses these issues temporally)

    • blending / morphing (e.g. 3D Studio Max)

proposals


3 simplification reveal information content

3. SIMPLIFICATION : reveal information content

  • Dransch (2000)- four functions to enhance cognition processes

    • Increase the important information

    • Avoid the overload of a single sense

    • Support double encoding of information

    • Support creation of mental models

  • Morphometric feature networks : (address these issues)

    • Describe information in surface (Fowler & Little, 1979)

    • Preferable to colour maps & contour maps

      (Helman & Hesselink, 1991; Bajaj & Schikore, 1996)

    • Polynomial -> local curvature

proposals


3 simplification reveal information content1

3. SIMPLIFICATION : reveal information content

  • aggregation

    • input

    • output

  • cartography

    • aggregation

    • generalization

    • simplification

    • design

    • transformation

proposals


3 simplification reveal information content2

3. SIMPLIFICATION : reveal information content

  • Surface Features :

    • reduce data density

    • local & global

    • objective (therefore good for comparison and change)

proposals


3 simplification reveal information content3

3. SIMPLIFICATION : reveal information content

proposals

  • Surface Feature Networks :

    • vectors identify features

    • morphometric network


3 simplification reveal information content4

3. SIMPLIFICATION : reveal information content

  • Dransch (2000)- four functions to enhance cognition processes

    • Increase the important information

    • Avoid the overload of a single sense

    • Support double encoding of information

    • Support creation of mental models

  • Morphometric feature networks : (address these issues)

    • Describe information in surface (Fowler & Little, 1979)

    • Preferable to colour maps & contour maps

      (Helman & Hesselink, 1991; Bajaj & Schikore, 1996)

    • Polynomial -> local curvature

proposals


4 enhancement lag aids working memory

4. ENHANCEMENT : lag aids working memory

  • Reduce high frequency events

  • Augments working memory

    • Human visual processing severely limited in terms of capabilities for interpreting parallel information that characterises dynamic processes (Ware, 2000)

  • Removes ‘movement’

proposals


5 design interactivity and controls

5. DESIGN : interactivity and controls

  • structure should provide flexibility for visualization

  • animation :

    • loop

    • playback control

    • linear temporal interpolation

    • duration control (exaggeration / emphasis)

  • interaction :

    • feature query (class & magnitude)

    • magnitude map

    • feature toggle (point / line)

    • feature selection (intelligent zoom / focus)

proposals


Snv surface network visualizer

snv : Surface Network Visualizer

  • Example ‘intellectual design’

  • Investigate validity/benefits of ideas

  • Examine suitability of cartographic metaphor

prototype


Snv surface network visualizer1

snv : Surface Network Visualizer

  • Example ‘intellectual design’

  • Investigate validity/benefits of ideas

  • Examine suitability of cartographic metaphor

prototype


Snv evaluation

snv : evaluation

  • Key component of iterative development of cartographic techniques

  • Peer review

    • web cartography forum

      (Blok & Köbben)

    • specific queries?

  • Testing

    • try each of the four improvements?

    • use in application?

  • Feedback :

    • http://www.soi.city.ac.uk/~jad7/snv/

prototype


Snv developments

snv : developments

  • Iterative design improvements

  • Higher levels of interaction

  • More sophisticated morphing

  • Feature significance / magnitude selection

  • Multi-scale visualization

  • Formal evaluation mechanism

prototype


Conclusion

conclusion

  • problems : anecdotal / empirical / experiential

  • proposals : cartographic perspective

  • prototype : implementation / evaluation

  • framework for animation for geovisualization

    • functionality

    • data sets

    • feedback & evaluation

  • visualization methods should draw on appropriate theoretical literature and exhibit graphic logic

  • visualization tools should implement methods and incorporate evaluation


Framework sequence

stage 1

cartographic parallel

Smoothing

  • generalization / simplification

    Smoothing

  • generalization / simplification

    Simplification

  • selection

  • enhancement

    Enhancement & Design

  • visual hierarchies

  • figure / ground

  • symbolism

framework / sequence

Spatial Continuity

  • spatial & thematic interpolation

    Temporal Continuity

  • temporal interpolation morph/blend

    Address Memory Load

  • reduce information content

  • derive morphometric features

    Aid and Prolong Working Memory

  • graphic lag

  • periodicity

  • interactivity

stage 2

stage 3

stages 4&5

Parallels between traditional cartographic practice, that generates graphics for particular uses and users, and the efforts required to generate animations that are suitable for visualization.


Final thoughts

final thoughts …

  • These 'data' issues (size, scope, time, etc…) don’t undermine visualization or geovisualization

  • They are a useful reminder of what it is, what it’s good for, what it’s bad at, and many of the more general limitations inherent in most of the measurements, models and techniques we use to represent the various phenomena, characteristics and processes about which we wish to acquire knowledge.

… end


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