A Comparison of Map vs. Text
Directions for a Handheld
Device in a Campus Setting:
A Pilot Study
Liz Atwater
Jason Burke
Andrea Kirk
Department of Psychology
George Mason University
Institute for Systems
Research
University of Maryland
Department of Computer
Science
University of Maryland
December 2001
Map vs. Text Directions

Which one is more effective?
– Less time, less errors
Does route complexity have an effect?
 Use by pedestrians instead of drivers

– Lack of landmarks
– No street names, etc.
Rover

Context-aware, location-aware
– Location awareness via GPS, RF, IR, etc.

Provides information depending on:
– User profile
– Device profile
– Location
– Context

Useful in many domains
– Tourism
– Commerce
Background Research

Williams studies (1999) – pilots finding
nearest airport using maps or text
– Maps are faster and more accurate
– ERF tasks had better results with track-up
– WRF tasks had better results with north-up

Aretz,1991 – ERF vs. WRF
– Ego-centered frame  track-up
– World-centered frame  north-up

Butz, 2001 – landmarks at key decision
points
Experiment – Hypotheses

Hypotheses:
– Null: There is no statistical difference between
completion time, consultation time and number of
errors between text and map directions, regardless
of route complexity.
– H1: Users will complete the tasks faster using map
directions.
– H2:Users will make fewer errors using map
directions.
– H3: Users will need less consultation time using text
directions.
– H4: Completion time will rise with increasing route
complexity.
Experiment – Variables

IVs & Treatments
– Direction type: map vs. text
– Route complexity: low, medium, high
• Low: 3 decision points, 893 ft
• Medium: 5 decision points, 897 ft
• High: 7 decision points, 883 ft

DVs
– Completion time
– Consulting time
– Errors
Experiment – Materials

Subjects
– 7 male, 5 female
– Undergrad & grad UMCP students

Other materials
– Pre & post-task questionnaires
– VZ-2
Experiment – Tasks
Navigate 3 routes using directions
 Within-subjects for routes
 Between-subjects for direction type
 2 stopwatches
 Route permutations:

123
213
312
132
231
321
Screen Shots
Text Implementation
Map Implementation
Results – Completion Time
Main effect for route: significant
 Main effect for direction: ns
 Interaction effect: ns

Completion Time
400
350
Time (s)
300
250
Map
200
Text
150
100
50
0
High
Medium
Route com plexity
Low
Results – Consultation Time
Main effect for route: significant
 Main effect for direction: ns
 Interaction effect: ns

Device Consulting Time
120
100
Time (s)
80
60
Map
40
Text
20
0
-20
High
Medium
Route Com plexity
Low
Results – Errors
Main effect for route: significant
 Main effect for direction: ns
 Interaction effect: ns

Number of Errors
3.5
Number of errors
3
2.5
2
Map
1.5
Text
1
0.5
0
-0.5
High
Medium
Route Com plexity
Low
Observations

Learning seemed to have a significant
effect on the results

Most errors occurred at non-dead ends

People are different
– Huge variance in user performance in
both map and text implementations
– Difficulty judging distances in text version
– Rotate map for track-up bearings
– Looking ahead caused problems
Conclusions

Need many more subjects

Text directions are difficult to describe in
college campus environment

Feedback from “real” context-aware
equipment could improve performance

Track-up display for map could decrease
orientation time

Hybrid to accommodate variations in user
cognitive strengths