Do these make any sense?

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

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

World-in-hand

Path drawing

Flying Vehicle Control

Walking interface

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.

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

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

Non metaphoric Interfaces ZUIs Bederson Focus in context

Using 3D to give 2D context Dill, Bartram, Intelligent zoom Perspective wall

Table Lens

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

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)

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

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

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)

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

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

Task: searching for target patterns that match

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

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

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.

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

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

Mental maps How do we encode space?

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

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)

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

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

A tight loop between user and data Rapid 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

Parallel coordinates For multi-dimensional discrete data Inselberg

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

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

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

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

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