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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 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

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  1. 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

  2. a journey to … • rugely • cannock • lichfield • brownhills • tamworth • uttoxeter • stafford staffordshire

  3. cartographic considerations • aggregation • input • output • cartography • aggregation • generalization • simplification • design • transformation 1,001,091

  4. cartographic considerations • aggregation • input • output • cartography • aggregation • generalization • simplification • design • transformation 1,001,091

  5. cartographic considerations • aggregation • input • output • cartography • aggregation • generalization • simplification • design • transformation 1,001,091

  6. cartographic considerations • aggregation • input • output • cartography • aggregation • generalization • simplification • design • transformation 1,001,091

  7. *n? cartographic considerations 1000*1000

  8. 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 (?)

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 3. SIMPLIFICATION : reveal information content • aggregation • input • output • cartography • aggregation • generalization • simplification • design • transformation proposals

  16. 3. SIMPLIFICATION : reveal information content • Surface Features : • reduce data density • local & global • objective (therefore good for comparison and change) proposals

  17. 3. SIMPLIFICATION : reveal information content proposals • Surface Feature Networks : • vectors identify features • morphometric network

  18. 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

  19. 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

  20. 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

  21. snv : Surface Network Visualizer • Example ‘intellectual design’ • Investigate validity/benefits of ideas • Examine suitability of cartographic metaphor prototype

  22. snv : Surface Network Visualizer • Example ‘intellectual design’ • Investigate validity/benefits of ideas • Examine suitability of cartographic metaphor prototype

  23. 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

  24. snv : developments • Iterative design improvements • Higher levels of interaction • More sophisticated morphing • Feature significance / magnitude selection • Multi-scale visualization • Formal evaluation mechanism prototype

  25. 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

  26. 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.

  27. 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

  28. fire pictures !

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