1. Do these make any sense?

2. Navigation Moving the viewpoint as a cost of knowledge

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 (Ware and Osborne 1990) 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. World-in-hand Good for discrete objects Poor affordances for looking scale changes – detail Problem with center of rotation when extended scenes

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

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

14. Map: ahead-up versus track-up North-up for shared environment Ahead-up for novices View marker gives best of both

15. Mental maps How do we encode space?

16. Seigel and White Three kinds of spatial knowledge 1) Categorical (declarative) knowledge of landmarks. 2) Topological (procedural) knowledge of links between landmarks 3) Spatial (a cognitive spatial map).  Acquired in the above order

17. 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)

18. Lynch: the image of the city Lynch’s Types ExamplesFunction PathStreet, canal, Transit line Channel for movement EdgesFence, Riverbank District limits DistrictsNeighborhoodReference Region NodesTown square, Public building Focal point for travel LandmarksStatueReference point

19. 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

20. 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. Furnas, G. W., Generalized fisheye views. Human Factors in Computing Systems CHI '86 Conference Proceedings, Boston, April 13-17, 1986, 16- 23.

21. Non metaphoric Interfaces ZUIs Bederson-Zooming Focus in context

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

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

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

25. Center of Workspace 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 = k t : a factor of 4 per second (10 sec ~ scale by a million) Better for rotations (people like to rotate around points of interest)

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

27. GeoZui3D Zooming + 2 dof rotations Translate point on surface to center Then scale. Or translate and scale. (8 x per second)

28. 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) 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)

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