Do these make any sense?

2. Do these make any sense?

3. Navigation • Metaphors and methods • Affordances • Ultimately about getting information • Geographic Space • Non-metaphoric navigation

4. The affordance concept • Term coined by JJ Gibson (direct realist) • Properties of the world perceived in terms of potential for action (physical model, direct perception) • Physical affordances • Cognitive affordances

5. World-in-hand

6. Path drawing

7. Flying Vehicle Control

8. Walking interface

9. Walking-on-the-spot interface • Use in virtual reality system • Actually a head bobbing interface. • Real-walking both more natural and better presence than either flying or walking on the spot.

10. Evaluation • Exploration and Explanation • Cognitive and Physical Affordance • Task 1: Find areas of detail in the scene • Task 2: Make the best movie For examples see classic 3D user interaction techniques for immersive virtual reality revisited

11. Non-metaphoric Focus+Context • Problem, how not to get lost: • Keep focus while remaining aware of the context. • Classic paper: Furnas, G. W., Generalized fisheye views. Human Factors in Computing Systems CHI '86 Conference Proceedings, Boston, April 13-17, 1986, 16-23.

12. Non metaphoric Interfaces • ZUIs Bederson • Focus in context

13. Using 3D to give 2D context Perspective wall www.thebrain.com Dill, Bartram, Intelligent zoom

14. http://www.nass.usda.gov/research/Crop_acre97.html Table Lens

15. POI Navigation MacKinlay start • Point of interest. • Select a point of interest • Move the viewpoint to that point. Dist = t C VP + View direction reorientation.

16. COW navigation • COW navigation • Move objects to the center of the workspace. Zoom about the center. • Initially object-based became surface-based • exponential scale changes d = kt • : a factor of 4 per second (10 sec ~ scale by a million) • Better for rotations (people like to rotate around points of interest)

17. COW Navigation in Graph Visualizer 3D COW Viewpoint The Concept: Translate to center of workspace then scale

18. GeoZui3DZooming + 2 dof rotations Translate point on surface to center Then scale. Or translate and scale. (8 x per second)

19. Navigation as a Cost of Knowledge. How much information can we gain per unit time • Intra-saccade (0.04 sec) (Query execution) • An eye movement (0.5 sec) < 10 deg : 1 sec> 20 deg. • A hypertext click (1.5 sec but loss of context) • A pan or scroll (3 sec but we don’t get far) • Walking (30 sec. we don’t get far) • Flying (faster , but can be tuned) • Zooming, t = log (scale change) • Fisheye (max 5x). DragMag (max 30x)

20. How to navigate large 2 ½D spaces? (Matt Plumlee) Zooming Vs Multiple Windows • Key problem: How can we keep focus and maintain context. • Focus is what we are attending to now. • Context is what we may wish to attend to. • 2 solutions: Zooming, multiple windows

21. When is zooming better thanmultiple windows • Key insight: Visual working memory is a very limited resource. Only 3 objects GeoZui3D

22. Task: searching for target patterns that match

23. Cognitive Model (grossly simplified) • Time = setup cost + number of visits*time per visit • Number of visits is a function of number of objects (& visual complexity) • When there are too many multiple visits are needed

24. Prediction Results As targets (and visual working memory load) increases, multiple Windows become more attractive.

25. Generalized fisheye viewsGeorge Furnas A distance function. (based on relevance) • Given a target item (focus) • Less relevant other items are dropped from the display.

28. Custom Navigation in TrackPlot • Data Centered • Magic Keys • Widgets • Time bar • Play mode

29. Map:ahead-upversustrack-up North-up for shared environment Ahead-up for novices View marker gives best of both

30. Mental maps • How do we encode space?

31. Seigel and White • Three kinds of spatial knowledge • Categorical (declarative) knowledge of landmarks. • Topological (procedural) knowledge of links between landmarks • Spatial (a cognitive spatial map). • Acquired in the above order

32. Colle and Reid’s study • Environment with rooms and objects • Test on relative locations of objects • Results show that relative direction was encoded for objects seen simultaneously but not for objects in different rooms • Implications: can generate maps quickly: should provide overviews. (ZUIs are a good idea)

33. Lynch: the image of the city

34. Vinson’s design guidelines • There should be enough landmarks so that a small number are visible. • Each Landmark should be visually distinct from others • Landmarks should be visible at all navigable scales • Landmarks should be placed on major paths and intersections of paths

35. A tight loop between user and dataRapid interaction methods • Brushing. All representations of the same object are highlighted simultaneously. Rapid selection. • Dynamic Queries. Select a range in a multi-dimensional data space using multiple sliders (Film finder: Shneiderman) • Interactive range queries: Munzner, Ware • Magic Lenses: Transforms/reveals data in a spatial area of the display • Drilling down – click to reveal more about some aspect of the data

36. Parallel coordinates • For multi-dimensional discrete data Inselberg

37. Event Brushing - Linked Kinetic Displays Active Timeline Histogram Security Events in Afghanistan Event distribution in space Highlighted events move in all displays Scatterplot - victim vs. city Motion helps analysts see relations of patterns in time and space

39. Worldlets – 3D navigation aids Elvins et al. Worldlets can be rotated to facilitate Recognition Subjects performed significantly better

40. World-in-hand Good for discrete objects Poor affordances for looking scale changes – detail Problem with center of rotation when extended scenes

41. Flying Vehicle Control Hardest to learn but most flexible Non-linear velocity control Spontaneous switch in mental model The predictor as solution

42. Eyeball in hand Easiest under some circumstances Poor physical affordances for many views Subjects sometimes acted as if model were actually present