Upload
charleen-hampton
View
213
Download
0
Tags:
Embed Size (px)
Citation preview
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
Virtual scene
6 df HandleController
a
Flying Vehicle Control
Virtual scene
JoystickController
d
Walking interface
Virtual scene
TreadmillController
c
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, 16-23.
Non metaphoric Interfaces
ZUIs Bederson Focus in context
Using 3D to give 2D context
Dill, Bartram, Intelligent zoom
Perspective wall
www.thebrain.com
Table Lenshttp://www.nass.usda.gov/research/Crop_acre97.html
POI Navigation MacKinlay
Point of interest. Select a point of interest Move the viewpoint to that point.
VP
+ View direction reorientation.
Dist =start
Ct
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)
COW Navigation in Graph Visualizer 3D Viewpoint
COW
The Concept: Translate to center of workspace then scale
GeoZui3DZooming + 2 dof rotationsTranslate point on surface to centerThen 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) < 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)
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 thanmultiple 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 viewsGeorge Furnas
A distance function. (based on relevance) Given a target item (focus) Less relevant other items are dropped
from the display.
#include <GL/glut.h>
void redraw( void )
void motion(int x, int y) {
rx = x; ry = winHeight - y; }
void mousebutton(int button, int state, int x, int y) {
if (button == GLUT_LEFT_BUTTON && state == GLUT_DOWN)
{ rx = x; ry = winHeight - y;
} }
void keyboard(unsigned char key, int x, int y)
int main(int argc, char *argv[]){
glutMouseFunc( mousebutton);
glutMainLoop(); }
Custom Navigation in TrackPlot
Data Centered Magic Keys Widgets Time bar Play mode
Map:ahead-upversustrack-up
NN
a b c
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 cityLynch’s Types
Examples Function
Path Street, canal,
Transit line
Channel for movement
Edges Fence, Riverbank
District limits
Districts Neighborhood Reference
Region
Nodes Town square,
Public building
Focal point for
travel
Landmarks Statue Reference 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 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
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 aidsElvins et al.Worldlets can be rotated to facilitate RecognitionSubjects performedsignificantly better
World-in-handVirtual scene
6 df HandleController
a
Good for discrete objects
Poor affordances for looking scale changes – detail
Problem with center of rotation when extended scenes
Flying Vehicle ControlVirtual scene
JoystickController
dHardest to learn but most flexible
Non-linear velocity control
Spontaneous switch in mental modelThe predictor as solution
Eyeball in handVirtual scene
6 df HandleController
bEasiest under some circumstances
Poor physical affordances for many views
Subjects sometimes acted as if model were actually present