- The Ohio State University

Transcription

INVESTIGATING WAYFINDING USING VIRTUAL ENVIRONMENTS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Ebru Cubukcu, B.C. P. (Hons.), M.C.R.P.
*****
The Ohio State University
2003
Approved by
Dissertation Committee:
Professor Jack L. Nasar, Adviser
Adviser
Professor Steven I. Gordon
Graduate Program in
Professor Kenneth Pearlman
City and Regional Planning Program
ABSTRACT
Wayfinding is the spatial knowledge about one’s current location,
destination, and the spatial relation between them. Wayfinding problems threaten
people’s sense of well-being, and cause loss of time and money. Designers and
planners can improve wayfinding when they understand how physical
environmental factors affect people’s wayfinding performance. This study
explores the effect of personal and physical environmental characteristics on
wayfinding performance. The personal characteristics include gender, age, and
familiarity. The physical environmental characteristics include plan layout
complexity, physical differentiation and its components vertical and horizontal
differentiation.
The experiment had eighteen (2 x 3 x 3) simulated environments, with
two plan layouts (complex and simple), three kinds of vertical differentiation (no
differentiation, object landmarks, and building landmarks) and three kinds of
horizontal differentiation (no differentiation, road width variation, road pavement
variation), and it also had four different question orders.
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166 volunteers (98 male, 68 female) were tested individually. Participants
were randomly assigned to one of the question orders and to one of the simulated
environments with the constraint that there would be equal number of people in
survey types, in plan layout conditions, in vertical differentiation conditions, and
in horizontal differentiation conditions.
The experiment had a learning phase and a test phase. In the learning
phase, participants actively explored one of the simulated environments at their
leisure up to four minutes. In the test phase the participants completed three
spatial knowledge tasks (a direction estimation task, a navigation task, and a
sketching task) and a survey which had questions on gender, age, frequency of
playing computer game, realism of the simulated environment judgement and
wayfinding strategies used in the navigation task.
As expected, the Simple layouts, Higher Physical Differentiation, Vertical
or Horizontal differentiation yielded better wayfinding performance than
Complex layouts, Lower Physical differentiation, and No Vertical or Horizontal
differentiation. Males performed better than Females, and performance improved
with Familiarity.
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DEDICATION
To my parents and my husband
iv
ACKNOWLEDGMENTS
I would like to express my deepest appreciation to my adviser, Professor
Jack L. Nasar, for his expert and timely advice, guidance, inspirations, and
encouragement throughout the research. I thank Professors Steven I. Gordon and
Kenneth Pearlman for serving on the advisory committee and providing their
helpful insights and comments.
I would also like to acknowledge the assistance of Dr. Harry Heft for his
helpful comments on the early draft of this dissertation. I thank Dr. Peter Hecht
for allowing me to involve in wayfinding projects, which I truly enjoyed and
benefited, towards the end of this dissertation.
I would like to thank Dokuz Eylul University for the scholarship, and
Selin Koroglu and Filiz Dincyigit for handling all the administrative work in
Turkey regarding this scholarship. I also thank the Center for Mapping for the
assistantship.
I am also grateful to Jenny Klein, from the Office of Residence Life, who
was very helpful in providing the site to conduct the surveys, and to people who
participated as respondents in this study. I also extend my gratitude to Misun Hur,
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In-Young Yeo, and my other fellow Doctoral students in the Department of City
and Regional Planning for their positive reinforcement; to students in the
Advanced Computing Center for the Arts and Design (ACCAD) for their help to
discover the simulation tool in this dissertation; and to friends I met in Columbus
for their friendship.
Finally, I would like to thank my parents, Fulden and Ziya Demirayak,
my brother, my sisters-in-law and my niece, Yasemin, for being everlasting
supporters of my studies and believing in my capabilities. My special thanks go
to my husband, Mert, for everything.
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VITA
February 24, 1974.………………….Born – Antalya, Turkey
1997…………………………………B.C.P. (Hons.), City Planning
School of Architecture
Middle East Technical University
Ankara, Turkey.
2001…………………………………M.C.R.P., City and Regional Planning
Austin E. Knowlton School of Architecture
Columbus, Ohio, U.S.A.
1998-present………………………...Graduate Research Associate
Dokuz Eylul University
Izmir, Turkey.
2002-present...………………………Graduate Research Associate
The Center For Mapping
Columbus, Ohio, U.S.A.
FIELDS OF STUDY
Major Field: City and Regional Planning
Minor Field: Environmental Psychology
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TABLE OF CONTENTS
Page
Abstract ......................................................................................................................ii
Dedication ................................................................................................................. iv
Acknowledgments ...................................................................................................... v
Vita ...........................................................................................................................vii
List of Tables............................................................................................................xii
List of Figures .......................................................................................................xviii
Chapters
1 Introduction ............................................................................................................. 1
2 Literature Review .................................................................................................... 5
2.1 The Concepts Related to Wayfinding ............................................................... 6
2.2 Significance of the Wayfinding Research......................................................... 9
2.3 The Physical Environmental and Personal Characteristics Affecting
Wayfinding Behavior ...................................................................................... 10
2.3.1 The Plan Layout........................................................................................ 11
2.3.2 The Level of Physical Differentiation ...................................................... 12
2.3.3 The Vertical Differentiation ..................................................................... 13
2.3.4 The Horizontal Differentiation ................................................................. 15
2.3.5 Age............................................................................................................ 15
2.3.6 Gender ...................................................................................................... 18
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2.3.7 Familiarity (Experience)........................................................................... 21
2.3.8 Summary of Factors Effecting Wayfinding Performance ........................ 21
2.4 Tools to Simulate Environment ...................................................................... 22
2.4.1 Use of Photographs and Simulation Booth............................................... 23
2.4.2 Full Scale Models ..................................................................................... 25
2.4.3 Small Scale Models .................................................................................. 26
2.4.4 Computer Models (Virtual Environments)............................................... 28
2.4.5 Summary of Tools for Studying Wayfinding ........................................... 30
2.5 Measures of Wayfinding Performance ........................................................... 31
2.5.1 Self Report Tests ...................................................................................... 31
2.5.2 Memory Tests ........................................................................................... 32
2.5.3 Recognition Tests ..................................................................................... 33
2.5.4 Spatial Orientation Tests .......................................................................... 34
2.5.5 Navigation Tests ....................................................................................... 39
2.5.6 Summary of Wayfinding Measures.......................................................... 41
3 Methodology ......................................................................................................... 43
3.1 General Procedures and Equipment................................................................ 43
3.1.1 Introductory Procedures ........................................................................... 43
3.1.2 Equipment and Setting.............................................................................. 44
3.2 Virtual Environments...................................................................................... 44
3.2.1 Software.................................................................................................... 44
3.2.2 Physical Environmental Characteristics ................................................... 45
3.2.3 Realism of Virtual Environments Judgment ............................................ 57
3.3 Participants and Group Demographics ........................................................... 60
3.4 Experimental Procedures ................................................................................ 63
3.5 Measures ......................................................................................................... 66
3.5.1 Learning Phase ......................................................................................... 66
3.5.2 Test Phase ................................................................................................. 67
4 Results ................................................................................................................... 71
4.1 Relation Between Different Measures of Wayfinding Performance .............. 71
4.2 Relation Between the Self-Reported Navigation Strategy and Different
Tasks Measuring Wayfinding Performance .................................................... 72
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4.3 The Effect of Physical Environmental and Personal Characteristics on
Wayfinding Performance ................................................................................ 74
4.3.1 General Summary ..................................................................................... 75
4.3.2 Statistical Results...................................................................................... 86
4.3.2.1 Overall Spatial Awareness ................................................................. 88
4.3.2.2 Direction Estimation Task.................................................................. 94
4.3.2.3 Navigation Task ................................................................................. 99
4.3.2.4 Sketching Task ................................................................................. 104
5 Conclusion........................................................................................................... 110
Bibliography........................................................................................................... 121
Appendices:
Appendix A The written description about the study ............................................ 136
Appendix B The effect of interaction between physical environmental factors on
error scores ............................................................................................................. 137
Appendix B.1 General Linear Models on Comprehensive Measure
(Overall Spatial Awareness).............................................................. 138
Appendix B.2 General Linear Models on Direction Estimation Task ....... 140
Appendix B.3 General Linear Models on Navigation Task....................... 142
Appendix B.4 General Linear Models on Sketching Task......................... 144
Appendix C The statistical analyses on specific measures (error scores) of
navigation and sketching task ................................................................................ 146
Appendix C.1 The Specific Measures (Error Scores) of Navigation
Task ................................................................................................... 147
Appendix C.2 The Specific Measures (Error Scores) of Sketching Task .. 151
Appendix D The success scores for various tasks.................................................. 156
Appendix D.1 The Measures of Success Scores ........................................ 157
Appendix D.2 The Analyses of Overall Spatial Awareness Success......... 160
Appendix D.3 The Analyses of Direction Success .................................... 162
Appendix D.4 The Analyses of Overall Navigation Success..................... 165
Appendix D.5. The Analyses of Overall Sketching Success ..................... 168
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Appendix D.6. The Statistical Analyses on Specific Measures (Success
Scores) of Navigation Task ............................................................... 171
Appendix D.7. The Statistical Analyses on Specific Measures (Success
Scores) of Sketching Task................................................................. 175
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LIST OF TABLES
Table
Page
Table 3.1: Level of differentiation was determined by the presence of vertical
and horizontal differentiation ........................................................................... 48
Table 3.2: The means of judged realism across individual characteristics .............. 58
Table 3.3: The means of judged realism across physical environmental
characteristics ................................................................................................... 59
Table 3.4: The distribution of participants across eighteen environments............... 62
Table 3.5: The distribution of participants across survey and environment
conditions ......................................................................................................... 62
Table 4.1: Direction estimation, navigation and sketching scores had a
statistically significant correlation with one another........................................ 72
Table 4.2: Remembering the correct turns strategy was associated with
remembering the number of streets, buildings to pass strategy and
keeping track of general directions strategy..................................................... 73
Table 4.3: Different tasks related to different navigation strategies. ....................... 73
Table 4.4: The four tests repeated for each task....................................................... 75
Table 4.5: The significance of plan layout effect on various tasks .......................... 77
Table 4.6: The significance of physical differentiation effect on various tasks....... 79
Table 4.7: The significance of vertical differentiation effect on various tasks ........ 79
Table 4.8: The significance of horizontal differentiation effect on various
tasks .................................................................................................................. 81
Table 4.9: The significance of gender effect on various tasks ................................. 83
Table 4.10: The significance of age effect on various tasks .................................... 84
Table 4.11: The significance of familiarity effect on various tasks ......................... 84
Table 4.12: The significance of exploration speed effect on various tasks.............. 85
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Table 4.13: The significance of game playing effect on various tasks .................... 86
Table 4.14: General Linear Models on overall spatial awareness error for the
first set of analyses. .......................................................................................... 88
Table 4.15: General Linear Models on overall spatial awareness error for the
second set of analyses....................................................................................... 89
Table 4.16: As frequencies of game playing increased overall spatial
awareness errors decreased. ............................................................................. 93
Table 4.17: General Linear Models on direction error for the first set of
analyses. ........................................................................................................... 94
Table 4.18: General Linear Models on direction error for the second set of
analyses. ........................................................................................................... 95
Table 4.19: As game playing increased direction errors decreased ......................... 98
Table 4.20: General Linear Models on overall navigation error for the first set
of analyses. ..................................................................................................... 100
Table 4.21: General Linear Models on navigation error for the second set of
analyses. ......................................................................................................... 101
Table 4.22: More frequent game players had fewer navigation errors than less
frequent game players. ................................................................................... 103
Table 4.23: General Linear Models on overall sketching error for the first set
of analyses. ..................................................................................................... 105
Table 4.24: General Linear Models on overall sketching error for the second
set of analyses................................................................................................. 106
Table 4.25: More Frequent Game Players had fewer sketching errors than
Less Frequent game players ........................................................................... 109
Table 5.1: The significance of the effects of physical environmental
characteristics on various measures of wayfinding performance................... 114
Table 5. 2: The significance of the effects of personal characteristics on
various measures of wayfinding performance................................................ 116
Table B.1: General Linear Models with the interaction between plan layout
and level of physical differentiation............................................................... 138
and vertical and horizontal differentiation. .................................................... 139
and level of differentiation. ............................................................................ 140
and vertical and horizontal differentiation. .................................................... 141
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and level of differentiation ............................................................................. 142
and vertical and horizontal differentiation ..................................................... 143
and level of differentiation ............................................................................. 144
and vertical and horizontal differentiation ..................................................... 145
Table C.1: General Linear Models on Speed for the first set of analyses.............. 147
Table C.2: General Linear Models on Speed for the second set of analyses. ........ 148
Table C.3 General Linear Models on Turn Error for the first set of analyses. ...... 148
Table C.4: General Linear Model on Turn Error for the second set of analyses. .. 149
Table C. 5: General Linear Model on Distance Error for the first set of
analyses. ......................................................................................................... 149
Table C.6: General Linear Model on Distance Error for the second set of
analyses. ......................................................................................................... 150
Table C.7: Binary Logistic Regression on MARKET Sign Location Error (at
an intersection or on the road) for the first set of analyses............................. 151
Table C.8: Binary Logistic Regression on MARKET Sign Location Error (at
an intersection or on the road) for the second set of analyses........................ 152
Table C.9: General Linear Model MARKET Sign Distance Error for the first
set of analyses................................................................................................. 152
Table C.10: General Linear Model MARKET Sign Distance Error for the
second set of analyses..................................................................................... 153
Table C.11: General Linear Model on Route Turn Error for the first set of
analyses. ......................................................................................................... 153
Table C.12: General Linear Model on Route Turn Error for the second set of
analyses. ......................................................................................................... 154
Table C.13: General Linear Model on Route Segment Error for the first set of
analyses. ......................................................................................................... 154
Table C.14: General Linear Model on Route Segment Error for the second set
of analyses. ..................................................................................................... 155
Table D.1: In simple environments more participants were successful in more
tasks than complex ones. ................................................................................ 160
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Table D.2: As differentiation increased from Low to Moderate to High, the
percentage of respondents successfully completed more tasks increased...... 160
Table D.3: In environments with vertical differentiation more participants
were successful in more tasks than those without it....................................... 160
Table D.4: In environments with horizontal differentiation more participants
were successful in more tasks than those in environments without it............ 161
Table D.5: Males showed a higher success rate than females................................ 161
Table D.6: More frequent game players showed higher success rate than less
Table D.7: In simple environments more participants were successful than in
complex ones.................................................................................................. 162
percentage of successful respondents increased............................................. 162
were successful than in environments without it............................................ 162
were successful than in environments without it and road pavement
variation produced better success rates. ......................................................... 162
Table D.11: Males showed a higher success rate than females.............................. 163
Table D.12: More frequent game players showed higher success rates than
less frequent game players. ............................................................................ 163
Table D.13: Binary Logistic Regression on direction success for the first set of
analyses. ......................................................................................................... 163
Table D.14: Binary Logistic Regression on direction success for the second
set of analyses................................................................................................. 164
Table D.15: In Simple environments more participants were successful than in
complex ones.................................................................................................. 165
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Table D.21: Binary Logistic Regression on overall navigation success for the
first set of analyses. ........................................................................................ 166
Table D.22: Binary Logistic Regression on overall navigation success for the
Table D.23: In Simple environments more participants were successful than in
complex ones.................................................................................................. 168
Table D.29: Binary Logistic Regression on overall sketching success for the
Table D.30: Binary Logistic Regression on overall sketching success for the
Table D.31: Binary Logistic Regression on success based on speed for the
Table D.32: Binary Logistic Regression on success based on speed for the
Table D.33: Binary Logistic Regression on success based on turn error for the
Table D.34: Binary Logistic Regression on success based on turn error for the
Table D.35: Binary Logistic Regression on success based on distance error for
the first set of analyses. .................................................................................. 173
Table D.36: Binary Logistic Regression on success based on distance error for
the second set of analyses............................................................................... 174
Table D.37: Binary Logistic Regression on success based on map selection for
the first set of analyses. .................................................................................. 175
Table D.38: Binary Logistic Regression on success based on map selection for
the second set of analyses............................................................................... 176
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Table D.39: Binary Logistic Regression on success based on locating
MARKET sign exactly for the first set of analyses........................................ 177
Table D.40: Binary Logistic Regression on success based on locating
MARKET sign exactly for the second set of analyses................................... 178
Table D.41: Binary Logistic Regression on success based on drawing the
sequence of route turns for the first set of analyses. ...................................... 179
sequence of route turns for the second set of analyses................................... 180
route segments for the first set of analyses..................................................... 181
route segments for the second set of analyses................................................ 182
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LIST OF FIGURES
Figure
Page
Figure 2.1: The hierarchical structure of the study area, wayfinding……………... 4
Figure 2.2: Full scale models (Sanoff 1991)………………………………………. 22
Figure 2.3: The left one is the simulator at Lund (Sanoff 1991; p 146), right one is
the simulator at the Institute of Urban and Regional Development at the
University of California at Berkley. (Altman and Wohlwill, 1977;
p.81)……………………………………………………………………….…… 23
Figure 3.1: The same house plan was repeated in all environments. From top left
moving clockwise images show plan view (not seen by subjects), front view,
right view and left view...……………………………………………………… 41
Figure 3.2: The arrows at intersections showed possible directions one can take
and a message reminds users that they can change direction…………….…… 42
Figure 3.3: An example for calculating Interconnection density (ICD) value…….. 43
Figure 3.4: The plan layout and schematic drawings of the simple and complex
settings in O’Neill’s study……………………………………………………... 44
Figure 3.5: The plan layout and schematic drawings of the simple and complex
settings in this study……………………………………………………….…... 45
Figure 3.6: The START and MARKET signs in all environments………………... 46
Figure 3.7: Environments with vertical differentiation had two types of
landmarks. TYPE A had four object landmarks shown from top left moving
clockwise (one kind of lamp, another kind of lamp, a flower pot and a flag) at
choice points. (All environments were in full color)…………………………. 47
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Figure 3.8: Environments with vertical differentiation had two types of
landmarks. TYPE B had four building landmarks that differ from one another
and the surrounding buildings shown from top left moving clockwise (a gray
brick building, an orange brick building, a white building and a yellow
building) at choice points. (All environments were in full color)…………….. 48
Figure 3.9: The location of landmarks in the Simple and Complex environments... 49
Figure 3.10: In environments with road hierarchy the most efficient route
between START and MARKET signs were wide or had asphalt pavement and
all other roads were narrow or had cobblestone pavement……………………. 50
Figure 3.11: Environments with horizontal differentiation had two types of road
hierarchy. For TYPE A (left column) road width varied and for TYPE B
(right column) road pavement varied………………………………………….. 51
Figure 3.12: In the sketching tests, participants were asked to pick one of the four
maps, that they thought best represents the environment they experienced.
(Top row: A = Correct Complex Plan, B = Correct Simple Plan; bottom row:
C = Distracter for Complex Plan, D = Distracter for Simple Plan.)…………... 59
Figure 4.1: The standardized mean error scores for each task is higher in the
Complex environments than in the Simple ones………………………………. 77
Figure 4.2: The mean error scores for each task increases as level of physical
differentiation decreases from High to Moderate to Low……………………... 78
Figure 4.3: The mean error scores for each task is lower in environments in which
vertical differentiation is Present than the ones in which vertical
differentiation is Absent……………………………………………………….. 80
Figure 4.4: The mean error scores for each task is lower in environments in which
horizontal differentiation is Present than the ones in which horizontal
differentiation is Absent……………………………………………………….. 81
Figure 4.5: Males had fewer mean errors than Females in all three tasks………… 82
Figure 4.6: The standardized mean error scores in each task decreases as game
playing frequency increases…………………………………………………… 86
Figure 4.7: The significance of difference across different physical differentiation
conditions on spatial awareness. Overlapping boxes indicate an insignificant
difference and separate boxes indicate a significant difference……………….. 91
xix
Figure 4.8: The significance of difference across different Vertical Differentiation
conditions on overall spatial awareness Overlapping boxes indicate an
insignificant difference and separate boxes indicate a significant difference…. 92
Figure 4.9: The significance of difference across different Horizontal
Differentiation conditions on overall spatial awareness. Overlapping boxes
indicate an insignificant difference and separate boxes indicate a significant
difference………………………………………………………………………. 92
Figure 4.10: The significance of difference between different types of horizontal
differentiation conditions (Overlapping boxes indicate an insignificant
difference and separate boxes indicate a significant difference) on direction
estimation……………………………………………………………………… 98
Figure 4.11: The significance of difference between different types of Vertical
differentiation conditions (Overlapping boxes indicate an insignificant
difference and separate boxes indicate a significant difference) on direction
estimation……………………………………………………………………… 109
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CHAPTER 1
INTRODUCTION
Wayfinding requires knowledge about one’s current location, destination, and
the spatial relation between them: spatial knowledge. When people lack such
knowledge, they become disoriented, if not totally lost. Disorientation can have
serious consequences for people. It can lead to physical exhaustion, stress, anxiety
and frustration, all of which threaten their sense of well being and limit their mobility
(Bell et al., 1996; Carpman and Grant, 2002; Evans, 1980; Lynch, 1960). It may lead
people to avoid or leave a place. In contrast, easy wayfinding may evoke positive
feelings and a desire to visit.
Arthur and Passini (1992) suggested the design process should include
wayfinding requirements as an integral part. Planners and designers should produce
“wayfinding plans” like
“HVAC plans.”
In the past few years, organizational
administrators have been considering wayfinding as a management issue and
consulting professionals to improve wayfinding in their facilities (Carpman and
Grant, 2002). Reduction in wayfinding problems can improve the image of a well-
1
maintained facility. It can also translate into dollars – benefits of increased
productivity and reduced mobility costs. Easing wayfinding is particularly important
for office buildings, airports, colleges, hospitals, libraries, museums, shopping malls,
transit stations, entertainment parks, and zoos.
Empirical studies can provide information that designers and planners can use
to improve wayfinding. Toward this end, many studies attempted to understand the
relation between several personal and physical environmental characteristics and
spatial knowledge or wayfinding behavior (see reviews of Evans, 1980; Moore,
1979). Personal characteristics were studied extensively, but physical characteristics
were rarely studied. Yet for planners and designers understanding the influence of
physical environmental characteristics can help minimize the wayfinding difficulties.
Moreover, many studies focus on one variable, but wayfinding involves many
variables working simultaneously.
My study explores the simultaneous effect of various personal and physical
characteristics on wayfinding performance, and focuses more on the physical
characteristics. Here I summarize the hypotheses, later I give a detail explanation
(see chapter 2.3). For the personal characteristics, research showed age effect with
better wayfinding performance for older children than younger children (Fenner et al.,
2000; Heth et al., 1997) and for younger adults than older adults (Burns, 1998;
Weber, 1978); gender effect with males having better wayfinding performance
(Devlin and Bernstein, 1995, 1997; Lawton 1996); and familiarity effect with higher
2
familiarity producing better wayfinding performance (Ruddle et al., 199; Stanton et
al., 1996, 1998). I hypothesized that Males and people with More Experience with the
setting would show better wayfinding performance than Females and people with
Less Experience with the setting. I expected that the performance would not be
affected by age because my study targeted a narrow age group, young adults (18-55).
I studied this age group as they represent the majority of the population. For the
physical characteristics, research showed people tend to perceive wayfinding as
difficult in complex layouts (Abu-Obeid, 1998; O’Neill 1991a; Weisman, 1981) and
in environments with high degree of uniformity (lack of differentiation) (AbuGhazzeh, 1996; Passini et al. 2000). I hypothesized that environments with a Simple
layout, higher Physical Differentiation, Vertical and Horizontal Differentiation
should produce better wayfinding performance than environments with Complex
layout, lower Physical Differentiation and with No Vertical or Horizontal
Differentiation. In general, the findings supported the hypotheses. They suggest that
to improve wayfinding, planners and designers should use simple layouts with some
differentiation. Other researchers also suggested similar guidelines for planners and
designers, however they rarely discussed how to manipulate plan layout complexity
and physical differentiation and empirically tested the effect of physical factors on
wayfinding behavior with considering other personal factors. This study showed ways
to objectively measure and manipulate plan layout complexity and physical
differentiation (see Chapter 3.2) for wayfinding before or after construction.
3
As a demonstration of a methodology to study wayfinding behavior, it also
provides a model for future studies on spatial behavior. This study used virtual
environments (VEs), computer generated three dimensional environments to simulate
physical settings. VEs have been used in many other applications (described later) but
have rarely been used in research exploring spatial behavior in different settings. This
study showed that VEs could be extended to such applications. VEs allow control of
physical characteristics and allow users to navigate as if they are in real
environments.
Chapter 2 reviews the research on wayfinding. It discusses the concepts and
terms related to wayfinding. It reviews the personal and physical environmental
characteristics related to wayfinding behavior, and it reviews methodological issues,
the ways to simulate a physical setting and measure wayfinding behavior.
Chapter 3 discusses the methods adopted in the present study. The study used
virtual environments to simulate a residential neighborhood. To measure wayfinding
behavior, it used multiple measures including pointing an invisible destination,
finding the shortest route from one location to another and sketch mapping the
experienced environment.
Chapter 5 presents the results. Chapter 6 discusses the strengths and
limitations of the present study and outlines areas for further research and for design
and planning application.
4
CHAPTER 2
LITERATURE REVIEW
“Wayfinding” is a research area in “perception and cognition”, which is a subfield of “environmental psychology” (Figure 2.1) (Bell et al., 1996; Ittelson et al.,
1974; Stokols and Altman, 1987). A broad field of enquiry, wayfinding encompasses
a range of disciplines, such as psychology, geography, and planning (Foreman and
Gillett, 1997).
Psychology
Sociology
Anthropology
Geography
Architecture
…
Planning
Environmental Psychology
Environmental
Stress
Hazard
Perception
Weather, air
pollution, noise
…
Perception and
Cognition
Wayfinding
Figure 2.1: The hierarchical structure of the study area, wayfinding.
5
Perhaps as a consequence of this diversity of disciplines, approaches to
understand wayfinding vary. To clarify this study’s approach, this chapter first
describes the concepts related to wayfinding, such as environmental perception and
cognition, spatial knowledge, and cognitive map. Then it discusses the significance of
wayfinding studies. Next, it explores the physical environmental and personal
characteristics that affect wayfinding behavior. Then, it reviews the tools that have
been used to simulate physical environment. Finally, it discusses the ways to measure
wayfinding performance.
2.1 The Concepts Related to Wayfinding
Perception refers to the experience of world, which happens in a moment of
time and requires little or no information processing, while cognition refers to the
comprehension of the environment that involves more information processing, and
requires some mental activity (Bell et al., 1996; Heft, 1996; Nasar, 1998). Perception
and cognition depend on one another (Moore and Golledge, 1976). Perception leads
to a reconstruction of cognitive structures and is influenced by such structures.
With perception and cognition, people develop spatial knowledge about the
physical environment to maintain orientation and find their way from one location to
another. Gardner (1983, 1993) argued that humans have multiple types of
intelligence. Researchers studying wayfinding classified the spatial knowledge into
6
three types: (1) landmark knowledge, (2) route or procedural knowledge, and (3)
survey or configurational knowledge (Belingard and Peruch, 2000; Pick and
Lockman, 1981; Siegel and White, 1975; Throndyke and Hayes-Roth, 1982).
Landmark knowledge refers to the concrete knowledge about places; route knowledge
(or procedural knowledge) refers to the knowledge of routes that connect places; and
survey knowledge (or configurational knowledge) refers to an integrated
understanding of the layout of the space with the interrelationships of the elements
contained therein. A number of researchers merged landmark and route knowledge
into one category (route knowledge) yielding two types of spatial knowledge, route
and survey knowledge (Abu-Obeid, 1998; Aginsky et al., 1997; Lawton, 1996;
Rossano et al., 1999; Sholl et al., 2000; Witmer et al., 1996).
Research differs on the sequence of the development of spatial knowledge.
Some studies found that route knowledge precedes survey knowledge (Abu-Obeid,
1998; Belingard and Peruch, 2000; Hart and Moore, 1973; Lawton, 1996; Shemyakin,
1962; Siegel and White, 1975), while others found that survey knowledge developed
first, (Hirtle and Hudson, 1991; Stevens and Coupe, 1978; Wilton, 1979), and still
others found that people developed and access both types of knowledge
simultaneously (Cole and Reid, 1998; Foley and Cohen, 1984; Lindberg and Garling,
1982; Taylor and Tversky, 1996). The conflicting results may be an artifact of the
test situation. People may learn an environment, from a map or navigation. When
learning it from a map, people tend to develop survey knowledge. However, when
learning it from navigation, people tend to develop route knowledge (Rossano et al.,
7
1999; Taylor and Tversky, 1996; Thorndyke and Hayes-Roth, 1982). The degree to
which the setting has distinguishable landmarks or paths may also affect the type of
spatial knowledge developed (Evans, 1980).
The study of cognitive mapping (information about physical environment) is
related to spatial knowledge, because it tells how people mentally represent the
physical environment in their minds (Garling et al., 1984; Garling and Evans, 1991;
Garling and Golledge, 1993; Moore and Golledge, 1976). Information about how
people imagine the physical environment can be used to design, plan and manage
environments that facilitate easier use and more satisfaction during navigation
(Lynch, 1976; Moore and Golledge, 1976).
Cognitive maps not only contain information about places and their spatial
relationships, but also contain attributive values and meaning (Kitchin, 1994; Garling
et al., 1984). Comparison of cognitive maps with cartographic maps found that
similar to cartographic maps, cognitive maps are based on Euclidean geometry.
People represent elevation differences in cognitive maps as such difference appear in
cartographic maps. (Garling and Golledge, 1989). However, people’s cognitive
representations are not perfect replicates of cartographic maps (Moore and Golledge,
1976). They are inaccurate and distorted (Downs and Stea, 1973; Kitchin, 1994;
Moore and Golledge, 1976). The accuracy depends in part on the familiarity and
experience with the environment (Garling et al., 1984; Golledge et al., 1985), and the
8
process through which a person acquires environmental knowledge, through map or
navigation (Moore, 1979).
Cognitive maps are important aids to wayfinding (Garling et al., 1984; Garling
and Golledge, 1989; Passini, 1984a, 1984b). Wayfinding is defined as a behavior
(Carpman and Grant, 2002) to reach a spatial destination or to navigate and orient in
spatial environments (Devlin and Bernstein, 1995, 1997; Passini, 1984a, 1984b;
Prestopnik and Roskos-Ewoldsen, 2000).
The wayfinding process is a kind of
problem solving. It involves 1) decision making, 2) decision execution, and 3)
information processing (Passini, 1984a, 1984b; Passini et al., 2000).
2.2 Significance of the Wayfinding Research
Wayfinding is prerequisite of satisfaction of other higher level goals
(Weisman, 1981). It may not represent the primary performance goal but it certainly
is necessary to perform tasks within an environment (Colle and Reid, 1998).
Disorientation produces frustration, irritation, anxiety, and stress (Carpman and
Grant, 2002; Evans, 1980; Lang 1987; Lawton, 1994). It can threaten our sense of
well-being (Lynch, 1960), and limit personal mobility (Burns, 1998). Disorientation
also has costs in terms of time and fuel, which contributes to congestion (Burns,
1998; Passini, 1980). In confusing places, such as hospitals and campuses, staff may
waste time directing and leading people to locations (Hecht, 2000; Peponis et al.,
9
1990). Wayfinding difficulties may lead people to avoid places such as shopping
malls, museums, and convention centers (Carpman and Grant, 2002). It can make
people late for important occurrences such as business meetings or planes, which may
cause loss of opportunity and money (Carpman and Grant, 2002). More serious
consequences can result when ambulance drivers, firefighters have difficulty finding
their way around (DeParle, 1989 as cited in Carpman and Grant, 2002).
Although wayfinding has been a topic of interest for many disciplines
including environmental psychology and geography, it is still not completely
understood (Carpman and Grant, 2002). Organizational administrators, interior
designers, architects, and planners can improve wayfinding when they understand
how the physical environment affect wayfinding performance. The present study
attempts to develop such an understanding. The next section discusses the physical
environmental and personal characteristics that may affect wayfinding behavior.
2.3 The Physical Environmental and Personal Characteristics
Affecting Wayfinding Behavior
What physical environmental and personal characteristics may influence
wayfinding behavior? First consider the physical environmental characteristics. My
study considers four kinds of physical environmental features: (1) layout of the
physical environment, (2) level of physical differentiation, (3) vertical differentiation
10
(presence of landmarks), and (4) horizontal differentiation (presence of road
hierarchy).
2.3.1 The Plan Layout
Researchers have suggested that the legibility or complexity of plan layout
may effect wayfinding performance and cognitive mapping (Abu-Obeid, 1998;
Appleyard, 1970; Garling et al., 1981; Garling and Golledge, 1989; Lynch, 1960;
O’Neill, 1991a, 1991b; Passini, 1980; Weisman, 1981). Findings agree that people
easily comprehend the physical environments if the plan layout is legible and simple.
Weisman (1981) compared people’s self reports of wayfinding performance in a
number of settings that vary in plan layout. People tended to perceive wayfinding as
more difficult in settings that were more complex and less legible. O’Neill (1991a)
found that people drew more accurate sketches and found their way to a specific
destination more accurately in simple layouts.
Researchers assessed the legibility or complexity of a plan layout through
both subjective and objective measures. Weisman (1981) asked people to rate
(subjectively) a sample of corridor diagrams according to degree of complexity. AbuObeid (1998) subjectively assigned three campus layouts to simple grid layout, semigrid layout, and compositive network. O’Neill (1991a) developed an objective
measure of floor plan complexity which was based on the average number of paths at
each decision point (see chapter 3 for a more detailed description). It gives designers
11
and planners specific information about what to manipulate to produce the desired
effect. Thus, I used O’Neill’s objective measure. I expected the simple environments
to produce better wayfinding performance than complex ones.
2.3.2 The Level of Physical Differentiation
Higher physical differentiation may effect wayfinding behavior because it
facilitates extracting and understanding of physical information (Abu-Ghazzeh, 1996;
Abu-Obeid, 1998; Appleyard, 1969; Evans et al., 1982; Garling et al., 1986; Garling
and Golledge 1989; Passini et al., 2000; Weisman, 1984). Passini et al. (2000)
interviewed Alzheimer patients and nursing home staff to identify the architectural
interior design features that caused wayfinding difficulties for patients. He found that
monotony of architectural composition increased wayfinding difficulties. Abu-Obeid
(1998) interviewed students from three universities with different campus designs.
Students at campus with a repetitive design (i.e. lacking differentiation) showed
poorer performance in sorting pictures to represent a route in the campus. AbuGhazzeh (1996) interviewed students to rank the physical setting variables that
caused spatial orientation and wayfinding problems at campus. The results showed
that high degree of uniformity (lack of differentiation) was the major factor in feeling
lost or disoriented.
No study tested the effect of the level of physical differentiation on
wayfinding behavior in a controlled environment. The previous studies measured the
12
level of physical differentiation subjectively. For example Abu-Obeid (1998)
assigned three different campus designs to low, moderate and high level of
differentiation. However, each campus differs from one another in several other
factors, such as the plan layout, size, or topography.
Weisman (1981) suggested that physical differentiation refers to the extent to
which one location looks different from others. Evans et al. (1982) suggested that
physical differentiation can be achieved through changes. The present study achieved
differentiation in two ways, vertical and horizontal changes. LOW differentiation
environments had no vertical or horizontal changes, MODERATE differentiation
environments had either vertical or horizontal changes, and HIGH differentiation
environments had both vertical and horizontal changes. I expected that wayfinding
performance would improve as physical differentiation increased from LOW to
MODERATE to HIGH. Now consider vertical and horizontal differentiation.
2.3.3 The Vertical Differentiation
Lynch and Rivkin (1976) and Wagner et al. (1981) demonstrated that when
walking around people look at vertical elements such as buildings, and window
displays. The permanent and distinctive vertical elements are remembered more
(Appleyard, 1969; Evans et al., 1982; Lynch, 1960) and are important in navigation
and orientation (Cornell et al., 1989; Evans, 1980; Evans, 1984a, 1984b; Heth et al.,
1997; Passini, 1980; Ruddle et al., 1997; Tlauka and Wilson, 1994). Lynch (1960)
13
referred to such distinctive vertical elements as landmarks. Studies agree on the
positive effects of landmarks on wayfinding. Cornell et al. (1989) found that
landmarks played an important role in wayfinding success of 6 and 12 year old
children. Heth et al. (1997) found that compared to 8-year-old children, 12-year-old
children used landmarks more when recognizing the places while reversing a route.
Doherty et al. (1989) found that children and adults showed better performance in
scene recognition task when there were landmarks. Ruddle et al. (1997) found that
people navigate more accurately in simulated environments that had landmarks than
those without landmarks. Tlauka and Wilson (1994) argued that landmarks are
helpful but not sufficient to successfully navigate from one location to another.
My review of literature highlighted three important issues to consider in
relation to landmarks: the type, the attributes and the location of landmarks. For the
type, researchers refer to two types of landmarks, global (Darken and Sibert, 1993)
and local (Heth et al., 1997). Local landmarks, such as a flower pot or a lamp, are
visible within a restricted locality (Ruddle et al., 1998). Global landmarks, such as a
mountain, are visible from far away and from many places (Ruddle et al., 1998). For
the attributes, the most important attributes of buildings for landmark qualities
include form, visibility (Appleyard, 1969) and uniqueness (Abu-Ghazzeh, 1996;
Evans et al., 1982). For the location, researchers argued that landmarks are more
effective when placed at transition points (Allen, 1982; Heth et al., 1997). My
dissertation used local landmarks, because planners and designers can rarely
manipulate global landmarks. I gave landmarks unique forms and I located them at
14
intersections. I expected wayfinding performance to improve with the presence of
this vertical differentiation.
2.3.4 The Horizontal Differentiation
Lynch and Rivkin (1976) and Wagner et al. (1981) demonstrated that when
walking around, people look at the ground as well. People notice the differentiation
on the pavement. However, no one has empirically tested if this horizontal
differentiation enhances people’s wayfinding performance, as does the vertical
differentiation. Road hierarchy is an important factor in determining the legibility of a
physical environment (Lynch, 1960). It also produces horizontal differentiation and
may enhance wayfinding performance. My dissertation used variation of road
pavement and road width to produce road hierarchy and horizontal differentiation. I
expected horizontal differentiation to produce better wayfinding performance.
Now consider personal characteristics that may produce wayfinding
performance differences. My dissertation considered three kinds of personal
differences: (1) age, (2) gender, and (3) familiarity (experience).
2.3.5 Age
Studies compared wayfinding abilities within children (Acredolo, 1977;
Acredolo et al., 1975; Fenner et al., 2000; Heth et al., 1997; Piaget and Inhelder,
1967; Siegel et al., 1978), between children and adults (Bell, 2002, Cornell et al.,
1989, 1992) and within adults (Burns, 1998; Evans et al., 1984a; Ohta and Kirasic
15
1983; Passini et al., 1990; Weber et al., 1978). Most studies found better wayfinding
performance for older children than younger children (Fenner et al., 2000; Heth et al.,
1997) and for younger adults than older adults (Burns, 1998; Weber et al., 1978).
Fewer studies found no difference between younger and older children (Bell, 2002;
Lehnung et al., 2001), or between younger and older adults (Brown et al, 1998).
Heth et al. (1997) examined children’s place recognition. The results showed
that older children (12 year-old) used more reliable, stationary landmarks, were more
attentive to spatial relations between designated landmarks, and showed better
performance in recognizing being on or off the route, than younger children (8 yearold). Fenner et al. (2000) examined children’s wayfinding behavior. Younger (5 and 6
years-old) children produced greater wayfinding errors, when replicating a route in
forward and reverse directions, than older children (9 and 10 years-old). Lehnung et
al. (2001) compared children’s understanding of spatial environment. 11-years-old
children needed fewer trials to learn the sequence of landmarks than 5 and 7-yearsold ones, but there was no difference between ages 5 and 7 –years-old children.
Older children also showed better performance in pointing landmarks than younger
ones. Bell (2002) compared children’s and adults’ sketching ability. 7 and 9 year old
children performed similarly in locating object, but adults performed better than the
children. Weber et al. (1978) asked young adults to explore a nursing home for five
minutes then compared their recognition of places with older residents. Younger
adults recognized the places more accurately than older residents. Burns (1998)
distributed a postal questionnaire survey, asking about wayfinding problems, to a
16
large sample of UK motorist drivers. Self-reports showed that older drivers tended to
perceive wayfinding as being more difficult than non-elderly. Brown et al. (1998)
asked people aged 20 to 78 to give directions to a stranger while looking at a map.
When giving directions with access to a map, young and old adults used equal
number of landmarks, road names relational turns and cardinal directions which were
equally accurate.
One explanation of contradicting findings on age relates to the test situation.
Tests may differ in the load placed on memory. Older adults and younger children
may show poorer performance on tasks that require memory. However, when
memory demands are minimal or absent, the age effect on wayfinding behavior may
disappear. For example, providing a map reduces the load on memory by eliminating
the need for cognitive maps. Brown et al. (1998) found that when provided a map
older and younger adults did not differ. Pointing with a compass puts a higher
demand on memory than pointing with finger. Lehnung et al. (2001) found that 5year old group may show similar accuracy with 9 and 11-years-old children when
they point with a finger but poorer performance when they point with a compass.
Another explanation of contradicting findings may relate to variation in age
breakpoints. Each study compared different age groups.
This dissertation targeted only one age group, young adults. As a result of the
narrow range of ages, I expected no effect of age on wayfinding performance.
17
2.3.6 Gender
The effect of gender on wayfinding behavior is unclear in the literature. Some
found gender differences in wayfinding tasks (Appleyard, 1976; Brown et al., 1998;
Burns, 1998; Devlin and Bernstein, 1995, 1997; Evans, 1980; Galea and Kimura,
1993; Holding, 1989; Holding, 1992; McGuiness and Sparks, 1983, Miller and
Santoni, 1986; Prestopnik and Roskos-Ewoldsen, 2000; Schmitz, 1997; Ward et al.,
1986) and others found no such difference (Carr and Schissler, 1969; Cousins et al.,
1983; Kirasic et al., 1984; Montello and Pick, 1993; Prestopnik and RoskosEwoldsen, 2000; Sadalla and Montello, 1989; Schmitz, 1997; Taylor and Tversky,
1992).
Several studies find males performing better than females. Devlin and
Bernstein (1995) had participants learn a campus with different cue sources (a series
of photographs, a series of textual direction information, a map and combination of
these cues). The participants were then shown another series of photographs and were
asked to pick some to represent a route connecting A to B. Results showed that
females had more errors than males. In a similar study, Devlin and Bernstein (1997)
had participants study a neighborhood map. The participants were then asked to
locate a number of landmarks on the map and indicate which path to follow to get
from A to B. The results showed that males were faster than females in showing
which path to follow. Lawton (1996) found gender difference in pointing task. He
18
gave participants a “landmark learning” tour in a building. After the tour, participants
pointed to the landmarks at a location, which was not on the route they learnt. Results
showed lower error scores for males. While these studies showed males performing
better than females, many studies found no differences related to gender.
Looking at a pointing task, Sadalla and Montello (1989) found no gender
difference. In their study, participants walked a number of paths containing one turn.
At the final point, participants pointed to the start point, the original direction of
travel and also estimated the angle at the turn. Males and females performed similar
in all three pointing tasks. Schmitz (1997) found some similarities and differences
between females and males. He asked participants to explore a maze in forward and
reverse directions. Then participants represented the maze in drawing or writing.
Results showed that females were slower in exploring the maze than males. Females
used more landmarks and fewer directions in their representations but overall females
and males used similar number of elements to represent the maze. Prestopnik and
Roskos-Ewoldsen (2000) employed a pointing task and a self-reporting test, related to
sense of direction and use of wayfinding strategies. Self-reporting test showed no
gender difference. Pointing task showed no gender difference in response time but
male superiority in error scores.
If there is a gender difference it may relate to biological factors, such as
differences in brain organization (Kimura, 1992 as cited in Lawton 1996). However
gender differences may also relate to other factors, which lead to contradictory
19
findings. First, researchers suggested that females depend more on route knowledge,
while males depend more on survey knowledge (Lawton, 1994 and 1996). Using
different types of wayfinding strategies (Galea and Kimura, 1993; Schmitz, 1997;
Sholl et al., 2000; Ward, 1986) may cause differences in some tasks but not in others.
If we accept the argument that route knowledge precedes survey knowledge (AbuObeid, 1998; Belingard and Peruch, 2000; Hart and Moore, 1973; Lawton, 1996;
Shemyakin, 1962; Siegel and White, 1975) then we may expect no gender difference
on tasks that require route knowledge but male superiority on tasks that require
survey knowledge. If we accept the argument that route and survey knowledge
develop simultaneously (Cole and Reid, 1998; Foley and Cohen, 1984; Lindberg and
Garling, 1982; Taylor and Tversky, 1996;), we may expect female superiority on
tasks that require route knowledge and male superiority on tasks that require survey
knowledge. Second, studies showed that females tend to feel higher anxiety and fear
(Devlin and Bernstein, 1997; Prestopnik and Roskos-Ewoldsen, 2000; Schmitz 1997),
and less confidence (Lawton 1996) while completing wayfinding tasks, and they
perceive wayfinding as more difficult (Burns, 1998). Such feelings may lead to
poorer wayfinding tasks performance (Evans et al., 1984b; Garling and Golledge,
1989). Third, in some cultures males have greater opportunity to travel and thus
develop better directional skills (Evans, 1980).
In such cultures, the greater
experience for males may give them better wayfinding scores than females.
20
Despite the contradicting findings on gender, in this study I expected male
superiority in spatial knowledge because I used tests of survey knowledge to measure
it (see section 2.5).
2.3.7 Familiarity (Experience)
In general, studies found positive effects of experience on spatial knowledge
or wayfinding performance (Ruddle et al, 1998; Stanton et al., 1996, 1998).
Ruddle et al. (1998) did a controlled study in a simulated environment. They
had participants explore a simulated environment repeatedly. The number of times a
participant navigated in the environment determined the level of familiarity. The
spatial knowledge tasks included finding a route, estimating direction and distance.
Participants developed more accurate spatial knowledge with increased familiarity.
Stanton et al. (1996, 1998) had children explore simulated environments at fortnightly
intervals. Children’s performance on spatial tasks, such as estimating direction and
sketching (drawing an outline of the environment, placing the objects on a prepared
outline plan) improved with repeated exploration, especially for large scale
environments. In this dissertation, I expected that wayfinding performance would
improve with increases in physical environment experience.
2.3.8 Summary of Factors Effecting Wayfinding Performance
I have shown that wayfinding may relate to personal characteristics of the
wayfinder and the physical characteristics of the environment. Personal
21
characteristics include age, gender, familiarity (experience). Physical characteristics
include plan layout, physical differentiation and its components vertical and
horizontal differentiation.
Most studies looked at the effect of each factor alone. To better understand
wayfinding, we need to consider personal and environmental factors simultaneously.
In two early review papers, Moore (1979) and Evans (1980) concluded that although
personal factors were widely explored, physical environmental factors were
understudied. My review of the literature shows a continuing insufficient attention to
the influence of physical environmental characteristics on wayfinding behavior.
Hence, this study focused on the physical characteristics, but considered them and
personal characteristics simultaneously. It did the tests in controlled conditions.
To study wayfinding in a controlled physical setting, researchers must decide
on at least two kinds of factors, 1) the ways to simulate the environment, 2) the ways
to measure wayfinding responses. I used Virtual Environments to simulate the
environment and multiple measures of survey knowledge to test wayfinding
performance. The following sections discuss my choices and the other possible
options to simulate a physical setting and measure wayfinding performance.
2.4 Tools to Simulate Environment
Researchers have observed and tested people’s wayfinding behavior in real or
simulated settings. Real environments have variety that is hard to control.
Manipulating the structure of real environment is difficult or even impossible
22
(Belingard and Peruch, 2000). However, to test the effect of specific physical
environmental factors, one needs to control the variety in the physical setting.
Controlling the physical structure is possible in simulated settings. Simulation, in this
study, refers to visually representing something real.
Visual simulations include: (1) photographs, (2) small-scale three dimensional
models, (3) full-scale three dimensional models, (4) three dimensional computer
models. Each tool varies in level of detail, color, quality of reproduction, and
conditions in which the simulation is depicted, and in which it is seen (Sanoff, 1991).
2.4.1 Use of Photographs and Simulation Booth
Photographs may simulate on-site experience and have been widely used in
environmental psychology, in studies of environmental preference (Nasar, 1988),
perception (Appleyard et al., 1964; Bosselman 1998) and wayfinding (e.g. O’Neill,
1991a). A perceiver traveling along a route perceives sequence of transitions that
connect successive vistas (Cullen, 1961; Heft, 1996). This sequence of transitional
information is particularly important for wayfinding behavior. However, pictures do
not show continuous movement of transitions. Heft and Nasar (2000) questioned the
static nature of photographs because they lack the dynamic experience of moving
through places. Their comparisons of people responses to dynamic displays
(videotaped segments along a route), and static displays (freeze frames of each
segment) found differences between two display modes.
23
Reseachers have presented a series of photographs to simulate movement in a
drive (Appleyard et al., 1964) or walk (Bosselman, 1998; Cullen, 1961; O’Neill,
1991a). A series pictures would feel like watching a movie. However, the slides in
such simulations restrict the observer’s ability to scan the environment extensively
(Sanoff, 1991). Possible views that one can see in a real setting are not shown in these
sequential photos (Bosselman, 1998).
An alternative way to simulate movement through photographs is using a
simulation booth. A simulation booth has screens encircling the viewer to give three
dimensional view of an environment. It also provides opportunity for active
exploration. Winkel and Sasanoff (1966) (as cited in Sanoff 1991) compared the
movement pattern of people in a real environment and in a simulation booth. The
results confirm the similarities between path selection in each condition, but
interviews after the simulated trip revealed that people might not have acquired a
comprehensive image of the space in the simulated environment.
24
2.4.2 Full Scale Models
Full scale models have been used to assess a proposed designs (Marans, 1993;
Sanoff, 1991), to understand people’s perception of an existing environment (Sadalla
and Oxley, 1984) and to observe people’s wayfinding behavior (Passini et al., 1990;
Schmitz, 1997, Waller et al. 1998). Marans (1993) built a model of a hospital room.
Different user groups (nurses, doctors and visitors) explored this model and assessed
the use quality prior to construction. Sadalla and Oxley (1984) built rooms of the
same size but different shapes and asked people to estimate their size to see if shape
effected perception of size. Passini et al. (1990) built a maze and asked people to do
several wayfinding tasks.
A full scale model provides opportunity to control factors that vary in the real
environment while letting people move as if they are in a real setting. However full
scale models may look like a maze, and it requires large spaces, time and money
(Figure 2.2). With full scale models, one can simulate an indoor space but not a large
outdoor space (Sanoff, 1991).
25
Figure 2.2: Full scale models (Sanoff 1991)
2.4.3 Small Scale Models
Building and modifying small scale models are relatively easy and less costly
than full-scale models. However, their static nature and inability to allow views to
experience movement at the eye level represent major drawbacks that may
differentiate them from experience in full scale places (Sanoff, 1991).
To simulate movement researchers have put a small camera in a model. A
camera moving around a small-scale model records the scenes on a videotape
transmits the visual information to a television screen. Movement direction and speed
of the camera can be controlled (Figure 2.3).
26
Figure 2.3: The left one is the simulator at Lund (Sanoff 1991; p 146), right one is the simulator at the
Institute of Urban and Regional Development at the University of California at Berkley. (Altman and
Wohlwill, 1977; p.81)
Such simulators have been used to understand and foresee the impact of plans
(Appleyard and Craik - as cited in Bosselmann 1998; Janssens and Kuller, 1986 - as
cited in Sanoff, 1991) and to predict the people’s behavior (Carpman et al., 1985).
Appleyard and Craik had people evaluate a setting in three ways; an actual tour by
driving through the real setting in van, a video recorded in the real setting and a video
recorded using a model to simulate driving. The results demonstrated that people
found the simulated environment realistic. Carpman et al. (1985) built a small scale
model of a hospital setting with two different design, having a parking deck accessed
directly from a drop-off entrance drive or having a parking deck accessible only from
the main road. In both designs a sign is leading people to access the parking deck
through the main road. They simulated a drive by recording a videotape in each
design. Visitors were shown these simulations and were asked where they would turn
to park if they were coming alone to visit a patient. Results showed that in the design
with a direct access from the drop-off circle, people tended to ignore the sign and
27
chose to turn to the drop-off circle to enter the parking deck because they see the
entrance located adjacent to the drop-off circle. With this simulation Carpman et al.
(1985) could observe people’s behavior to two different designs before construction,
and could see the potential problem of crowdedness at the drop-off circle if there is an
entrance to the parking structure from it.
With such simulations, respondents reported that they felt like exploring a real
environment. However, such simulators were rarely used as a research tool, perhaps
because of its high costs (Bosselman 1998; Carpman et al. 1985; Sanoff, 1991)
2.4.4 Computer Models (Virtual Environments)
Computer generated three-dimensional environments are called virtual
environments (VEs). In these simulations, the user can visualize and interact with the
virtual three-dimensional spatial environment in real time. VEs are used in research
related to physical environment to control the physical characteristics (Arthur et al.,
1997; Rossano et al., 1999; Wilson et al., 1997a) or when it is hard to gather subjects
in the real one (Ishikawa et al., 1998).
Because this technology is relatively new1, there are some concerns about its
limitations related to its cost, its ability to represent real world experience and its
ability to transfer spatial information to real world experience.
1
The ideas behind this technology began to emerge about 20-30 years ago (see Kalawsky, 1993;
Rehingold, 1991, for reviews, as cited in Wilson, 1997.
28
For cost, it varies because the hardware and software requirements vary. Until
recently the necessary hardware and software was limited to high end technologies;
however, recent improvements have lowered costs enough to make it available in
many offices and have improved the image quality enough to make it an efficient tool
for environmental research (Aginsky et al., 1997; Garling et al., 1997; Ishikawa et al.,
1998; Peruch 1998; Rossano et al., 1999; Wilson et al., 1997a).
For similarity between the real and VE experience, studies explored if people
could develop spatial representations in VEs and if people’s navigation behavior in
VE is similar to the one in the real environments. Studies consistently showed that
people developed good spatial representation of VEs (Regian et al., 1992; Peruch et
al., 1995). Although, one study showed that people developed route knowledge
slower in a VE than in an equivalent real environment (Witmer et al., 1996), others
showed that people who navigated in a virtual environments showed similar accuracy
in spatial knowledge tests to that of people navigated an equivalent real-world
building (Rassona et al. 1999; Ruddle et al, 1997, 1998; Throndyke and Hayes- Roth,
1982; Wilson et al., 1996).
For transfer of spatial information from VEs to real environments, studies
tested if people could perform some spatial tasks in the real environment by just
exploring a virtual simulation of the real environment. Studies showed that people did
transform the spatial information they gained while exploring a virtual replica of the
real environment (Bliss et al., 1997; Cromby et al., 1996a, 1996b; Wilson et al. 1996).
29
For example, Wilson et al. (1996) found that students who explored a virtual building
containing only the major features of real building, such as corridors and doors, could
estimate directions to the objects in the real environment. Cromby et al. (1996b)
found that children who explored a virtual supermarket were able to find the items in
the real supermarket. Bliss et al. (1997) found that firefighters who learned a building
from a simulation were able to find their direction in the real building.
2.4.5 Summary of Tools for Studying Wayfinding
This study used simulated environments rather than real environments
because it allowed control of environmental factors. From the possible simulation
tools, this study used computer generated models for three reasons: (1) they are
dynamic and active where people can navigate as if they are in real environment; (2)
they are flexible and easy to manipulate or control the physical features of the
environment; and (3) they are affordable.
30
2.5 Measures of Wayfinding Performance
Researchers have measured wayfinding performance in five ways: 1) self
report tests, 2) memory tests, 3) recognition tests, 4) spatial orientation tests, and 5)
navigation tests.
2.5.1 Self Report Tests
Self report tests include tasks such as reporting subjective judgments of
wayfinding ability or navigational strategy. Weisman (1981) and Abu-Ghazzeh
(1996) developed a questionnaire to measure respondent’s own judgments of
wayfinding ability. It asked about wayfinding behavior (e.g. have you ever had
trouble finding your way?), and perceived knowledge and understanding of the
setting (e.g. how confident would you be of the directions you’d give to such a
stranger?). Lawton (1994, 1996) developed a questionnaire to measure respondents’
navigational strategy while finding their way around an unfamiliar building. The
questionnaire listed possible strategies, and the respondents rated the likelihood of
using each strategy on a 5-point scale. Some strategies related to route knowledge,
such as using room numbers and signs, others related to survey knowledge, such as
using global reference points. Prestopnik and Roskos-Ewoldsen (2000) used
Lawton’s (1994) questionnaire. Dogu and Erkip (2000) combined Weisman (1981)
and Lawton (1996) questionnaire to examine people wayfinding behavior in a
shopping mall.
31
Long questionnaires are not the only way to measure self-judgements of
wayfinding behavior. Researchers also asked wayfinders to rate their sense of
direction (Scholl 1988; Kozlowski
and Bryant, 1977; Prestopnik and Roskos-
Ewoldsen, 2000) and to tell about their wayfinding decisions (Murokashi and Kawai,
2000; Passini, 1984b). Respondents gave reasons for their choice of route whenever
there were choices.
Self-report tests do not require much effort, time and money. However they
have been criticized for lack of consistency with the actual behavior (Ericson and
Simon, 1984; Marlowe et al., 1965 as cited in Judd et al., 1991). People tend to
exaggerate or give socially desirable answers (Lam and Cheng, 2002).
2.5.2 Memory Tests
Memory tests include tasks such as describing places or routes after a trip. For
example Appleyard (1969), Carr and Schissler (1969), and Lynch and Rivkin (1976)
had people tell which points or places they remember best after a trip, not necessarily
in an order. Such a test tells only about people’s knowledge about the presence of
particular places. Wayfinders may need this information to verify being on the right
trail, but they also need to know the connection between and sequence important
places. As a result some researchers had people describe a route between any given
two points or tell what they had seen in sequential order after the trip (Appleyard,
1969; Carr and Schissler, 1969).
32
Memory tests are inexpensive and easy to administer, but they may reflect the
individual’s language ability rather than spatial knowledge. An individual may only
report recalling places that are easy to describe in words. One study compared what
people really looked at (items recorded with a head-mounted Polymetric eyemovement recorder) with what people remembered looking at (Carr and Schissler,
1969). It found agreement, but people tended to report items that were easy to name
even though they had not look at them.
2.5.3 Recognition Tests
Recognition tests include tasks such as naming the objects during a trip,
recognizing a scene with pictures after the trip and sorting the pictures to show the
route. For example, Brunswik (1944) and Wagner et al. (1981) followed people and
stopped them at some intervals to ask what they were looking at. Magliano et al.
(1995), Aginsky et al. (1997), Wilson (1999) and Murakoshi and Kawai (2000)
showed participants pictures from the test environment and distracter pictures similar
to the ones in the test environment but from different locations that the participant had
never seen. They asked the participants to tell whether they had seen it in the
environment or not. Heth et al. (1997) escorted children from an origin to a
destination (original route) and then from the destination to the origin (return route),
but the return route had loop branches attached to the original route. On the return
route, they stopped children at some intervals and asked whether they were on or off
33
the original path. Such tests measure if people recognize being somewhere when they
are actually there. People may recognize being there but not know what they will see
next. To test such knowledge, Magliano et al. (1995) showed pairs of pictures from
the route and asked participants to decide which of the two pictures came first along
the route. Abu-Obeid (1998) asked students to arrange a series of pictures to show a
route.
Like memory tests, recognition tests are inexpensive and easy to administer.
Recognition tests measured during a trip may not reflect the real life experience
because the participant would attend the surrounding environment more in the test
situation than in the real life (Lynch and Rivkin, 1976). Also, tests with pictures may
be biased when the picture is taken from a different perspective than the observer’s
perspective.
Both memory and recognition tests measure wayfinding performance
indirectly. They tell if an individual remembers a certain place and has information
about a route between those places, but they do not reveal the individual’s knowledge
about how those places are spatially related.
2.5.4 Spatial Orientation Tests
Spatial orientation tests include three kinds of tasks: (1) drawing a sketch
map, (2) estimating distances between visible or invisible locations, and (3)
estimating directions of invisible locations. First consider sketch maps. Kitchin
34
(1997) discussed five variations of sketching: (1) the basic sketch map technique,
where the researcher gives the respondent a blank sheet of paper to sketch a map. (2)
the normal sketch mapping technique, where the researcher imposes constraints to
obtain required data (3) the cued sketch mapping technique, where the researcher
gives a portion of the map and asks the respondent to complete the specific features,
(4) the longitudinal sketch mapping technique, where the researcher asks respondents
to draw the map on layers of carbon tracing paper and turn the sheets over at some
time intervals to study how sketch map evolves, and (5) the cloze sketch mapping
technique, where the researcher covers a base map in a grid with some squares
deleted and has respondents fill the information in the blank squares.
Appleyard (1969) and Kitchin (1997) asked residents to draw a map of the
city on a blank paper. Carr and Schissler (1969), Murakoshi and Kawai (2000), and
Aginsky et al. (1997) showed participants a route and asked them to draw the route on
a blank sheet with or without commenting on the location and characteristics of the
various elements. Schmitz (1997) had participants explore a maze repeatedly and ask
them to represent the maze graphically. O’Neill (1991a) showed a series of pictures
depicting paths through a building, showing decision, starting and destination points,
then asked people to draw a sketch map of the floor plan and mark the location of
start and destination points. Wilson (1999) provided a plan view of the environment
and asked participants to name locations and indicate the position on the plan.
Rossano et al. (1999) laid out a number of white post-board cut-out shapes on a desk,
and asked participants to pick a shape as the experimenter reads name of a building
35
from the learned environment. Then the participants were asked to arrange the
selected shapes on a blank sheet. Rossano and Reardon (1999) gave participants a
sheet on which two buildings in the environment was represented with a rectangle
and asked them to locate the third building. Kitchin (1997) gave a map which had
blank boxes and asked residents to fill the blank boxes with the name of landmarks
provided in a list.
Sketch maps may reflect the individual’s drawing ability more than spatial
knowledge. However, one study found a weak correlation between the accuracy of
sketch map and artistic ability (Rothwell, 1976 as cited by Evans, 1980). Sketch maps
may also have metric and topological distortions. People tend to draw turns as right
angle (even if they are not), enlarge the areas that include lots of turns at the expense
of straight roads, distort the relative length of road segments (Aginsky et al., 1997).
Interpreting the accuracy of a sketch map is challenging. Passini (1984b) argued that
the sketches should be assessed according to their utility value instead of their
cartographic value. Researchers used different approaches to measure the accuracy of
sketches. They rated the overall accuracy2 (Aginsky et al., 1997; Murakoshi and
Kawai, 2000;) and complexity (Appleyard, 1970; Rovine and Weisman, 1989),
judged the accuracy of placement of path segments, location of intersection choice
(O’Neill, 1991a), counted the frequency of path, node, landmarks, and turns (Aginsky
et al., 1997; Rovine and Schmitz, 1997; Weisman, 1989), calculated the difference
2
Two researchers rated the maps independently. Maps with nearly correct placements of the route
were rated 4; maps that were toplogically correct with some distortions were rated 3; maps with almost
complete elements but with toplogically incorrect layouts were rated 2; and fragmental maps or blank
maps were rated 1.
36
between correct and incorrect turns (Aginsky et al., 1997), measured the relative
distance between landmark pairs (Aginsky et al., 1997), and assessed the topological
accuracy considering if the landmark was in the appropriate sequence with respect to
tour and the path between landmarks showed the appropriate turns (Rovine and
Weisman, 1989) or considering the number of breaks in the map (Aginsky et al.,
1997). No one measure is better than the others. Thus a combination of such measures
would give better measure. Using sketch maps, researchers also calculated distance
estimates (Biel, 1982; Jansen-Osman and Berendt 2002; Rossano et al., 1999; Wilson,
1999) and direction estimates (Colle and Reid, 1998; Gillian, 1994; Rossano et al.,
1999; Tlauka and Wilson 1996) between landmarks.
Now consider distance estimations. Researchers used many tasks (in addition
to sketching) including verbal estimation, drawing straight lines, reproducing a walk
or comparing the route choices (see Montello, 1991 for a review). Belingard and
Peruch (2000) asked participants to estimate the straight line distance between the
current and target locations. Peruch et al. (1989) asked general public and taxi drivers
to estimate straight line distance and travel distance verbally in either distance or time
units. Biel (1982) asked children to decide which one of the two landmarks were
closer to the current location. Jansen–Osman and Berendt (2002) had people explore
three routes. Given a straight line representing the length of one of the three routes,
the participants were asked to mark the length of the other two routes. Sadalla and
Magel (1980) and Sherman et al. (1979) had participants re-walk a straight line
distance at the same length as the test distance. Nasar (1983) examined the estimated
37
distance to places by asking which of the two parking garages people chose to park
to minimize the distance to their office. Nasar (1985) also had people estimate
walking and crow-flies distance to hidden and visible destinations. Distance estimate
is easy to measure. The performance in distance estimates were usualy calculated as
the difference between the true distance and the estimated distance.
Finally consider direction estimates. Researchers used two kinds of pointing
task (in addition to sketching): (1) simulated pointing tests and (2) actual pointing test
(Throndyke and Hayes-Roth, 1982; Rossano et al., 1999). In simulated test,
participants were asked to imagine their current location and position, and then point
to a target location. In actual test, people were asked to point to a target location when
they were in the real environment. Again, direction estimate is easy to measure. The
performance was measured as time to respond, with quick response indicating better
performance. The accuracy was calculated as the difference between the true
direction and the estimated direction.
People may point to a target by changing their looking direction, drawing on a
paper, using a keypad or a handheld pointer. Belingard and Peruch (2000) requested
participants to rotate their view to face an invisible target. Murakoshi and Kawai
(2000) told people to show direction of a target with an arrow. Rossano et al. (1999)
asked participants to mark along the perimeter of the circle to show the direction to
the target after explaining that the center of the circle represents the current location
and an arrow extending up from the circle represents the looking direction.
38
Prestopnik and Roskos-Ewoldsen (2000) explained participants that numbers on the
keypad represent the 450 angles surrounding them. Participants were then asked to
pick a number to show if the target is in front, back, left, right, front left, front right,
back left, or back right of them. Rossano et al. (1999) Sholl et al. (2000), and Lawton
(1996) used a hand held pointer. The pointer is mounted within a circular dial.
Participants rotate the pointer arm to show the direction of the target.
Spatial orientation tests measures how people represent spatial environment
but not how they navigate. Anooshian (1996) argued that ‘people often report
knowing the identities and locations of landmarks while having little or no knowledge
of how to navigate from one landmark to another.
2.5.5 Navigation Tests
Navigation test include tasks such as finding places, replicating a route,
reversing a route, finding the shortest path between two places and describing a route
to a stranger. Abu-Ghazzeh (1996) asked people to find some destinations in three
different buildings that they had never visited. Peponis et al. (1990) had participants
freely explore the setting and they had them search for specific locations. Rossano et
al. (1999) asked people to lead the way from one building to another in a campus
after studying a map of the setting or exploring the computer simulation of the
setting. O’Neill (1991a) asked participants to replicate the route they learned with a
series of pictures. Fenner et al. (2000) guided children along a route in a campus then
39
asked them to walk along the route without assistance in forward and reverse
directions. Schmitz (1997) had people find their way in a maze first from origin to
destination then from destination to origin. Murakoshi and Kawai (2000) guided
people to a destination with some detours then asked them to go back to origin by
using the shortest possible route. Rovine and Weisman (1989) took people on a tour
and pointed out some important target locations throughout the tour, then asked
participants to select the most direct route from a target to another one. Ward et al.
(1986) and Brown et al. (1998) asked people to give directions to a hypothetical
stranger while looking at the map or after memorizing the map. Passini et al. (1990)
did a series of navigation tests. First, they guided people in a maze, after which they
asked them to retrace the path on their own. Second, they showed people two routes
which were intersecting, and asked them to retrace a combined route (the first half of
the first trip and second half of the second route). Third, they had them learn a route
from a small scale-model, and then had them execute a route in the maze. Finally,
they showed a route and asked participants to find a shortcut back to the departure.
Navigation tests show people’s wayfinding behavior more accurately than self
reporting tests, but they might be time consuming. Accuracy in navigation test can be
measured in many ways. Researchers recorded the success in taking the most direct
route (Rovine and Weisman, 1989), amount of time spent to complete the task
(O’Neill, 1991a; Murakoshi and Kawai, 2000), the route and the distance (AbuGhazzeh, 1996; Murakoshi and Kawai, 2000; Rovine and Weisman, 1989), the speed
(Schmitz, 1997) number of backtrackings (Abu-Ghazzeh, 1996; O’Neill, 1991a),
40
number of turns (Rovine and Weisman, 1989), number of wrong turns (Murakoshi
and Kawai, 2000; O’Neill, 1991a; Rossano et al. 1999), and number of times people
asked for direction or used maps (Abu-Ghazzeh, 1996; Rossano et al. 1999).
2.5.6 Summary of Wayfinding Measures
There are numerous tests to measure an individual’s wayfinding performance.
Each test has a different requirement which causes variation in the performance. For
example, people may find their way accurately, but they may not be able to draw
comparable sketch maps3 (Clayton and Woodyard, 1981; Downs and Siegel, 1981;
Hart 1981; Passini 1984b). Passini (1984b) explained this contradiction with a
different cognitive memory requirement. The cognitive memory requirement is higher
for sketching than route finding, because route finding requires recognition while
sketching requires recall. Individuals recognize (notice) places they encounter.
However, they recall (remember) places by retrieving the environmental information
stored previously.
The contradiction occurs not only between the tests but also between the tasks
(within each test). For each test, different tasks may measure different spatial
knowledge. For example, for spatial orientation tests, estimating travel distances or
directions between successive locations measures route knowledge, while estimating
crowflies distances and directions measures survey knowledge (Goldin and
3
Note, this differentiation may apply to recognition and memory tests.
41
Throndyke, 1982; Golledge, 1987). For navigation tests, reproducing a route
represents route knowledge, while taking a shortcut represents survey knowledge
(Loomis et al., 1993; Presson and Montello, 1994).
Any single measure may have a bias. By using distinct and different measures
one can reduce the effects of each bias (Judd et al., 1991; Kitchin, 1996). In this
study, I used multiple measures of survey knowledge to understand wayfinding
performance. I asked people to report the strategy they used (self report), point to an
unseen place (spatial orientation test- estimating direction task), pick the correct map
among possible ones and mark some locations on that map (spatial orientation testsketching task) and find the shortest route between two locations (navigation testfinding the shortest route task).
42
CHAPTER 3
METHODOLOGY
3.1 General Procedures and Equipment
Interviews took place in five university dormitories at The Ohio State
University; Morill Tower, Steeb Hall, Park Hall, Stradley Hall and Jones Tower, all
but Jones Tower house undergraduate students. The experimental session lasted
between five to ten minutes per participant. The tests took place on weekdays and
weekends, from 9:30 am to 7:30pm, in January 2003. The completion of 166 tests
took 70 hours.
3.1.1 Introductory Procedures
The study met the university’s human subjects requirements. Posters informed
passers-by that by taking a five minute computer-based survey they would help a
Ph.D. student and receive a cookie and a soft drink. The participants received a brief
written description about the study (Appendix A). The description indicated that the
participant could withdraw at any time without penalty.
43
3.1.2 Equipment and Setting
A graphic PC-based desktop workstation (Pentium II, 32MB graphics card,
resolution 640X480X256, 17 inch monitor) was set up on a desk in a quiet location,
close to the entrance in each building. I faced the computer screen to a wall so
participants could not see the simulated environment before the test. Participants were
seated facing 50 cm from the center of the screen, to achieve a visual field of
approximately 45 degrees. A camcorder, placed on the same desk with the desktop
workstation, videotaped the computer screen to track the navigation.
3.2 Virtual Environments
3.2.1 Software
The simulated environments were built using a three-dimensional modeling
program, GTK Radiant. A real-time three-dimensional environment generator game
engine, QUAKE III ARENA software, produced perspective views to simulate
ground level walk-paced movement through the simulated environment. The
viewpoint was set at a height of 5.6 feet, average eye level. The game engine
displayed the scenes in color at a rate of approximately 20 frames per second.
Participants controlled their movement in the simulated environment via keyboard. It
provided left right rotations (left right arrows) and forward backward translations (up-
44
down arrows), with movement restricted to the horizontal plane. Other researchers
who used the keyboard for controlling motion demonstrated that users easily
mastered this form of interaction (Tlauka and Wilson, 1994; 1996; Wilson et al. 1996,
1997; Jansen-Osman and Berendt, 2002; Belingard and Peruch, 2000). The direction
of the observer’s gaze paralleled their direction of movement (i.e., no side view was
available).
3.2.2 Physical Environmental Characteristics
I created eighteen simulated environments. The environments contained
houses, trees, sidewalks, open green spaces, parking and background skyline to
represent relevant aspects of a residential area. I replicated a two-story house plan,
surrounded with a tree, a street lamp and a parking space (Figure 3.1). The
environments contained repeated units of this house plan. Repeating a design is a
common planning approach for some real developments and apartment complexes.
This study attempted to simulate such environments because residents and visitors of
those uniform developments or apartment complexes often have difficulties to find
their way.
I derived the texture maps from digital photographs of real buildings and real
objects and overlaid these textures onto the modeled objects to achieve detail and
realism. Vivid colors, real-world textures, and visual elements gave a strong
impression of a real residential setting.
45
House
Tree
Lamp
Sidewalk
Road
Figure 3.1: The same house plan was repeated in all environments. From top left
moving clockwise images show plan view (not seen by subjects), front view, right
view and left view.
A collisions-detection algorithm was used to prevent walking through walls
and to constrain walking to the roads. At each intersection choice point a message
reminded the participants that they need to choose the next direction. Arrows placed
at the intersections indicated the possible directions the participant could choose
(Figure 3.2).
46
Figure 3.2: The arrows at intersections showed possible directions one can take and a message reminds
users that they can change direction.
Each model differed on three physical factors: the plan layout (simple or
complex), vertical differentiation (without landmark, with landmark type A-object or
landmark type B-building), and horizontal differentiation (without road hierarchy,
with road hierarchy type A-road width variation or road hierarchy type B-road
pavement variation).
I classified the eighteen environments into three groups according to the level
of physical differentiation, the extent to which parts of environment looks different
from others (Table 3.1). The “low differentiation” group included the environments
without any landmark or road hierarchy. The “moderate differentiation” group
included the environments with either a landmark (one of two kinds) or road
hierarchy (one of two kinds). The “high differentiation” group included the
environments with both a landmark (one of two kinds) and road hierarchy (one of two
kinds).
47
Table 3.1: Level of differentiation was determined by the presence of vertical and horizontal
differentiation
Level of Differentiation *
Low Differentiation
Moderate Differentiation
High Differentiation
With Vertical Differentiation
(Landmark)
No
Yes (one of two kinds)
No
With Horizontal
Differentiation
(Road Hierarchy)
No
No
* Same set of conditions for simple and complex environments
For plan layout, half of the environments had a complex layout and half had a
simple layout. I used O’Neill’s (1991a) “Inter Connection Density” (ICD) measure to
determine simple and complex plan layout. The measure is based on the density of
interconnections at choice points. Figure 3.3 shows an example of the calculation of
ICD from a plan. The number of connections at each intersection, or choice point, is
listed to the right of the plan (ie. at intersection A, one has two choices). ICD is
calculated as the mean number of connections. Hence the plan has an ICD of 2.33.
A
B
C
D
A=2
B=2
C=3
D=3
E=2
F=2
+______
14
F
E
14/6 = 2.33 = mean ICD
Figure 3.3: An example for calculating Interconnection density (ICD) value
48
Using this measure, O’Neill (1991a) identified three settings inside the State
University of New York at Buffalo Lockwood Library, with different ICD measures
to examine the relationship between plan layout complexity and wayfinding
performance. Figure 3.4 shows the plan layouts and the schematic drawings of the
simplest and the most complex settings used in that study. In the schematic drawings,
you can see that the simple setting had ten intersections, and the complex one had
eleven intersections (the small and large circles). Both settings had five choice points
(the large circles), one START point, and one DESTINATION point. The simple
setting had an ICD of 2.4 and the complex setting had an ICD of 2.54.
Complex
Schematic Drawing
Plan Layout
Simple
Destination
LEGEND
Destination
Choice Points
Intersections
Start
START and DESTINATION
Start
Figure 3.4: The plan layout and schematic drawings of the simple and complex settings in O’Neill’s
study.
49
My study replicated O’Neill’s (1991a) simplest and most complex plan
layouts for outdoor virtual environments (Figure 3.5). My environments comprised a
rectangular area of 312 x 273 meters (1,024 x 896 feet). The complex plan layout
contained 27,017 meters (88,640 feet) of road, and the simple plan layout contained
24,189 meters (79,360 feet) of road. As a collusion detection algorithm prevented
navigation through some roads, the complex plan had 8,993 meters (29,504 feet) of
walkable road, and the simple one had 6,574 meters (21,568 feet). Thus, simple plan
had 73% of walkable road of the complex plan.
Complex
Plan Layout
Simple
Schematic
LEGEND
Roads
Market
Market
Walkable Roads
START and MARKET
Start
Start
Choice Points
Figure 3.5: The plan layout and schematic drawings of the simple and complex settings in this
study.
50
To represent the start and destination points from O’Neill’s (1991a) study, the
virtual environments had two signs, START and MARKET (Figure 3.6).
Figure 3. 6: The START and MARKET signs in all environments.
For vertical differentiation, the environments differed according to the
presence of landmarks. One third of the environments did not have landmarks while
two thirds did. To give the results greater generalizability there were two types of
landmarks. Half of the environments “with landmark” had object landmarks (type
A), such as a lamp, a flag, and a flowerpot (Figure 3.7). The other half had building
landmarks (type B) differing in color and shape from one another and from the other
buildings (Figure 3.8). Each type (A and B) had four landmarks.
51
Figure 3.7: Environments with vertical differentiation had two types of landmarks. TYPE A had
four object landmarks shown from top left moving clockwise (one kind of lamp, another kind of
lamp, a flower pot and a flag) at choice points. (All environments were in full color).
52
Figure 3.8: Environments with vertical differentiation had two types of landmarks. TYPE B had four
building landmarks that differ from one another and the surrounding buildings shown from top left
moving clockwise (a gray brick building, an orange brick building, a white building and a yellow
building) at choice points. (All environments were in full color).
Because landmarks are more effective when they are located at changes in
course or direction of traveling (Allen, 1982, Abu-Obeid, 1998), I located them at
choice points (Figure 3.9).
53
LEGEND
Roads
Walkable Roads
START and Market Signs
Landmarks
Figure 3.9: The location of landmarks in the Simple and Complex environments.
For horizontal differentiation, the environments differed according to the
presence of road hierarchy. One third of the environments did not have road hierarchy
while two thirds did. To give the results greater generalizability, I used two types of
road hierarchy. In half of the environments “with road hierarchy,” I varied road width
(Type A, narrow or wide) to create road hierarchy. In the other half, I varied the
pavement (Type B, asphalt or cobblestone) to create road hierarchy (Figure 3.11). In
environments with no road hierarchy, all roads had the same width (wide) and
pavement (asphalt). In environments with road hierarchy, the most efficient route
between START and MARKET signs was wide or had asphalt pavement and all other
roads were narrow or had cobblestone pavement. Figure 3.10 shows the hiearchy of
roads in the Simple and Complex plans.
54
LEGEND
Roads
(narrow or cobbelstone)
Roads
(wide or asphalt)
Walkable Roads
START and Market
Figure 3.10: In environments with road hierarchy the most efficient route between START and
MARKET signs were wide or had asphalt pavement and all other roads were narrow or had cobblestone
pavement.
55
Type A (Width differentiation)
Type B (Pavement differentiation)
Perspective view at intersection
Perspective view at intersection
Perspective view at wide road
Perspective view at asphalt road
Perspective view at narrow road
Perspective view at cobblestone road
Figure 3.11: Environments with horizontal differentiation had two types of road hierarchy. For TYPE
A (left column) road width varied and for TYPE B (right column) road pavement varied.
56
3.2.3 Realism of Virtual Environments Judgment
Participants rated the simulated environments as realistic (M = 4.87, SD =
1.43, where 1=not realistic at all, 7=very realistic, n=160). Most respondents rated the
realism above average (63%). Some rated it as average (20%) and fewer rated it as
below average (17%). Participants who gave low scores often said that the simulation
was realistic but what was simulated was not realistic. Although some found the
computer graphics quality very high, they gave low realism scores because of the
difference between their own neighborhood and the simulated environment. They
said their neighborhood was more spacious and had more variation in landscape and
housing types compared to the simulated environment.
I examined if judged realism differs across different personal and physical
environmental groups. Table 3.2 shows the means for the judged realism across
different personal groups. The judged realism did not differ at a significant level
across gender (F (1,158) = 0.027) or computer game playing frequency (F (6,153) =
0.690).
57
Table 3.2: The means of judged realism across personal characteristics
Judged Realism
Mean (SD)
Personal Characteristics
Gender
Female (n=65)
Male (n=95)
Computer game playing frequency
Never
1 (n=23)
2 (n=33)
3 (n=27)
4 (n=15)
5 (n=31)
6 (n=14)
All the time 7 (n=17)
4.85 (1.28)
4.88 (1.54)
5.13 (1.40)
4.55 (1.25)
4.93 (1.03)
5.27 (1.53)
4.84 (1.51)
5.00 (1.66)
4.65 (1.94)
Table 3.3 shows the means for the judged realism across different physical
environmental groups. The judged realism did not differ at a significant level across
plan layout (F (1,158) = 0.027), level of physical differentiation (F (2,157) = 1.638),
vertical (F (1,158) = 3.367) and horizontal hierarchy (F (1,158) = 0.513), the type of
landmark (F (2,157) = 2.127) or road hierarchy (F (2,157) = 0.693).
58
Table 3.3: The means of judged realism across physical environmental characteristics
Judged Realism
Mean (SD)
Physical Characteristics
Level of Physical Differentiation
Low Differentiation (n=40)
Moderate Differentiation (n=80)
High Differentiation (n=40)
Plan Layout
Complex (n=80)
Simple (n=80)
Vertical Differentiation
Without Landmark (n=80)
With Landmark (n=80)
Landmark Type A (n=40)
Landmark Type B (n=40)
Horizontal Differentiation
Without Hierarchy (n=80)
With Hierarchy (n=80)
Road Hierarchy Type A (n=40)
Road Hierarchy Type B (n=40)
4.60 (1.48)
4.85 (1.48)
5.18 (1.25)
4.89 (1.39)
4.85 (1.48)
4.66 (1.42)
5.08 (1.42)
4.93 (1.42)
5.23 (1.42)
4.79 (1.53)
4.95 (1.33)
4.80 (1.39)
5.10 (1.28)
I also examined the first order correlation between judged realism and
computer game frequency. They did not have a statistically significant Pearson
correlation (r=-.016, p>.05)
59
3.3 Participants and Group Demographics
166 volunteers (98 male, 68 female) participated in the study. Most
participants (about 85%) were students in a range of programs at the Ohio State
University and a small portion (about 15%) were staff. All had normal or corrected to
normal vision. I dropped 6 participants from the sample because they did not
complete the whole survey. For the remaining 160 volunteers (95 male, 65 female)
the ages ranged from 18 to 48, but most participants (83%, 133 people) were under
25. Only 9 participant (6%) were older than 30. When asked how often they played
computer games the participants rated themselves on averages as playing between
rarely and sometimes (M=3.67, SD=1.92 where 1=never, 7=all the time).
Studies found that males are more likely to play computer games (Greenfield,
Brannon, and Lohr, 1994; Greenfiled et al., 1994; Phillips, Rolls, Rouse, and
Griffiths, 1995; Subrahmanyam and Greenfiled, 1994; Colwell, Grady, and Rhaiti,
1995). My study paralleled those results. A statistically significant difference for
gender in reported computer game playing frequency (F(1,158) = 49, p<.01), revealed
that males reported playing computer games more often than did females (Males:
m=4.45, SD=1.9; Females: m=2.54, SD=1.4).
I used factorial research design. The experiment had eighteen (2 x 3 x 3)
simulated environments, with two plan layouts (complex and simple), three kinds of
vertical differentiation (no differentiation, type A differentiation, type B
differentiation) and three kinds of horizontal differentiation (no differentiation, type
60
A differentiation, type B differentiation), and it also had four different question orders
for a total of 72 (18 x 4) different combinations of question orders and environments.
160 participants were tested individually, they were randomly assigned to one of the
question orders and to one of the simulated environments with the constraint that
there would be equal number of people in survey types (40 people each), in plan
layout conditions, (simple and complex, 80 people each), in vertical differentiation
conditions, (with and without landmarks, 80 people each) and in horizontal
differentiation conditions, (with and without road hiearchy, 80 people each). There
were equal number of people in each “with landmark” types (Type A-object, Type Bbuilding, 40 people each), and in each “with road hierarchy” types (Type A-width,
Type B-pavement, 40 people each). There were 40 people in “Low Differentiation”
condition, 80 people in “Moderate Differentiation” condition and 40 people in “High
Differentiation” condition. Table 3.4 shows the distribution of participants across
eighteen environments. Table 3.5 shows the distribution of participants to each survey
order and to each environment condition.
61
Table 3.4: The distribution of participants across eighteen environments
Environment
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Level of
Differentiation
Low
Moderate
High
Complex
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
No
No
No
No
With Vertical
Differentiation*
No
No
No
No
Yes (Type A)
Yes (Type B)
No
No
Yes (Type A)
Yes (Type B)
Yes (Type A)
Yes (Type A)
Yes (Type B)
Yes (Type B)
Yes (Type A)
Yes (Type A)
Yes (Type B)
Yes (Type B)
With Horizontal
Differentiation**
No
No
Yes (Type A)
Yes (Type B)
No
No
Yes (Type A)
Yes (Type B)
No
No
Yes (Type A)
Yes (Type B)
Yes (Type A)
Yes (Type B)
Yes (Type A)
Yes (Type B)
Yes (Type A)
Yes (Type B)
n
20
20
10
10
10
10
10
10
10
10
5
5
5
5
5
5
5
5
* Type A = Object landmark, Type B = Building landmark
** Type A = Road width variation, Type B = Road pavement variation
Table 3.5: The distribution of participants across survey and environment conditions
Survey 1 Survey 2 Survey 3 Survey 4
ENVIRONMENT TYPE*
(Sketching (Direction (Navigation (Direction
Navigation Navigation Sketching Sketching
Direction) Sketching) Direction) Navigation)
TOTAL
Without Landmark – Without Road Hierarchy
5
5
5
5
Without Landmark – With Road Hierarchy
5
5
5
5
Road Hierarchy Type A
3
3
2
2
Road Hierarchy Type B
2
2
3
3
With Landmark – Without Road Hierarchy
5
5
5
5
Landmark Type A
3
3
2
2
Landmark Type B
2
2
3
3
With Landmark – With Road Hierarchy
5
5
5
5
Landmark Type A - Road Hierarchy Type A
2
1
1
1
Landmark Type A - Road Hierarchy Type B
1
2
1
1
Landmark Type B - Road Hierarchy Type A
1
1
2
1
Landmark Type B - Road Hierarchy Type B
1
1
1
2
TOTAL
20
20
20
20
*Same set of conditions for simple and complex environments.
166 people participated in the study, but the table does not include the 6 participants who were dropped
from the analysis because they did not complete the whole survey.
62
20
20
10
10
20
10
10
20
5
5
5
5
I tested if variability in participant characteristics, gender and computer game
playing differed across environmental conditions and survey form. Age and the
reported computer playing frequency did not differ significantly across environment
groups (plan layout, level of physical differentiation, vertical and horizontal
differentiation) or survey forms.
Gender did not differ significantly across the
different survey types or across some environment groups (plan layout, horizontal
differentiation), but differed across two types of environments, level of physical
differentiation (‫א‬2(2, N =160) = 11.4, p =.003) and vertical differentiation (‫א‬2(1, N
=160) = 9.3, p = .002). Most females were
tested in environments with low
differentiation (38%) or moderate differentiation (45%) while most males were tested
in environments with moderate differentiation (53%) or with high differentiation
(30%). Most females were tested in environments without any landmark (64%) while
most of the males were tested in environments with landmark (60%).
3.4 Experimental Procedures
The testing had a learning phase and a test phase. In the learning phase,
participants actively explored one of the virtual environments (VEs) at their leisure.
They started exploring the VE facing the start sign. I instructed them to attend to the
physical environment because they would answer questions on environmental
knowledge. Exploration time was limited to four minutes, but they were allowed to
63
stop exploring if they said they were ready to take the test. I videotaped the computer
screen to keep track of each participant’s exploration route in the learning phase.
The test had four sets of questions: a direction estimation test, a navigation
test, a sketching test and questions on gender, age, frequency of playing computer
game, realism of the simulated environment judgement and wayfinding strategies
used in the navigation test. The order of tests varied with the constraint that the
navigation test preceded the sketch map test (participants would have had to navigate
the route to sketch it).
For the direction estimation test, the computer automatically set the viewpoint
in the VE to face the START sign. I gave participants a piece of paper with a circle
on which 10o angles were identified and told them that the circle center represented
the current location in the VE and a line on the north of it represented the looking
direction. I asked them to place mark along the circle perimeter to show the direction
to the MARKET sign from the START sign.
For the navigation test, I asked participants to navigate to the MARKET sign
from the START sign as soon as possible. As in the direction estimation task, the
START point came up automatically.
After the navigation test, I asked participants to rate the degree to which they
used (where 1 = I did not use, 7 = I used a lot) each of four strategies: (1) visualizing
a map or layout of the area in my mind, (2) keeping track of the general direction, (3)
remembering the correct direction to turn at particular streets, (4) remembering how
64
many streets (buildings, trees, lights) to pass before making each turn. An open-ended
question also asked them to report any other strategy they may have used.
For the sketching test, participants saw four maps, illustrating the roads and
the START sign (Figure 3.12). The maps included one correct and one distracter map
for each plan layout, simple and complex. Distracter maps omitted two roads and had
an extra road compared to the correct maps. I asked each participant to choose the
map that they thought best represented the virtual environment they explored. Then I
asked the participant to mark the location of the MARKET sign on the map and to
draw the road they took in the navigation test from the START sign to the MARKET
sign.
Simple Plan
Correct
Complex Plan
B
C
D
Distracter
A
Figure 3.12: In the sketching tests, participants were asked to pick one of the four maps, that they thought
best represents the environment they experienced. (Top row: A = Correct Complex Plan, B = Correct Simple
Plan; bottom row: C = Distracter for Complex Plan, D = Distracter for Simple Plan.).
65
3.5 Measures
3.5.1 Learning Phase
From my movies of each participant’s exploration route in the learning phase,
I derived two measures of exploration pattern; (1) exploration time and (2)
exploration route. I measured exploration time in seconds. I drew each participant’s
exploration route on a plan layout and calculated the total distance covered by each
participant in feet.
The length of walkable roads differed in the Complex and the Simple
environments. Using exploration time and exploration route data, I derived two
additional exploration measures, speed (a measure of exploration pattern), and the
number of tours (a measure of familiarity). Speed was calculated for each participant
by dividing the total exploration distance to total exploration time. Number of tours
was calculated by dividing the distance covered by each participant to the total length
of walkable roads in that environment.
Participants spent an average of 214.4 seconds (SD = 23.9 seconds), with a
range of 128-281 seconds, exploring the environment before they reported feeling
ready for tests. Their average speed was 223.7 feet/seconds (SD = 54.0 feet/second),
with a range of 71-326 feet/seconds. To get familiar with the environment they
covered an average distance of 14,529 meters (SD = 3,641 meters) ( m = 47,670 feet,
66
SD = 11,948 feet), with a range of 4,726-22,355 meters (15,504-73,344 feet). The
average number of tours was 1.90 (SD = .50) with a range of .53 – 3.224.
3.5.2 Test Phase
I scored each task multiple ways, some related to success scores, others
related to error scores. Success and error scores were related and error scores yielded
a more detailed data for the uncompleted tasks. Hence the remainder of the study
reports the error scores (see Appendix D for the description and analyses of success
scores).
For direction estimation test, I calculated Direction Error as the absolute
difference between the participant’s estimated angle of direction and the true
direction.
For navigation test, I calculated four error scores based on (1) speed, (2)
extra distance walked, (3) extra turns taken and (4) backtrackings. To calculate Speed
Error, first I calculated speed by dividing the distance covered to find the MARKET
sign by the amount of time spent finding it. Then I standardized the speed scores with
the following equation (I also used the same equation to standardize any score for the
remainder measures):
4
On average they toured the environment two times. They explored at least half of the possible routes
and they toured the environment at most three times.
67
Standardized Scores =
( ActualScore − Minimumscore )
(MaximumScore − MinimumScore )
Standardized scores ranged from 0 to 1. Subtarcting the standardized speed
scores from “1” turned speed scores into speed error scores. I calculated the Distance
Error by dividing the extra distance by the shortest possible distance (Extra distance
equals to the difference between the distance traveled to complete the task and the
shortest possible distance.). For the Turn Error I summed those turns incongruent
with the most efficient route to finish. For the Backtracking Error I summed the
number of times a participant turn back to retrace the same path.
I standardized each error score to calculate an “overall navigation error.” The
sum of the standardized error scores of the speed, the distance error, and the turn error
produced overall navigation error score. Backtracking error score was dropped,
because of the low variation (see Results section).
For the sketching test, I calculated five error scores based on (1) map
selection, (2) position of the MARKET sign (at an intersection or on road) (3)
distance of the MARKET sign to START, (4) route segments (5) route turns. First
score tells the extent to which participants know the layout of the environment, the
next two scores tells the extent to which they know the location of a landmark, the
final two scores tells the extent to which they know the route they took in the
navigation task. For the Map Selection Error I scored the correct map as “0,” the
distracter map of the correct layout as “0.5,” and the wrong map as “1.” For the
68
MARKET sign location error I looked if the participant located the MARKET
correctly at an intersection or on the road. In complex layout the MARKET sign was
on the road whereas in simple environments it was at an intersection. If the
participant drew the MARKET sign at a correct position, on the road in complex
environments and at the intersection in simple environments, I scored it as “0.”
Otherwise I scored it as “1.” For the MARKET sign distance error, first I calculated
the participants estimated distance as the crow-fly distance between the START sign
and the participant’s drawing of the MARKET sign. Because the walkable roads in
simple and complex environments varied, I calculated the distance error by dividing
the absolute difference between participant’s estimated distance and the true distance
by the true distance. For route errors, I compared the participant’s drawing of the
route between the START and MARKET signs to the route they walked in the
navigation test. The Route Segment Error equaled to the sum of the number of
segments walked during navigation task but not drawn on the map and the number of
segments not walked during navigation task but drawn on the map produced the route
segment error. The Route Turn Error equaled to the sum of the number of turns made
during the navigation task but not drawn on the map and the number of turns not
made during the navigation task but drawn on the map produced route turn error.
I standardized each error score to calculate an “overall sketching error.” The
sum of the standardized scores of the map selection error, MARKET sign location
and distance error, route turn and segment error produced overall sketching error
score.
69
I also calculated a composite measure, “an overall spatial awareness
measure,” using the responses to three tests (direction, navigation and sketching). To
do this, I first standardized the overall scores for each task (direction estimation,
navigation and sketching). Then I calculated an overall spatial awareness error score
with the following equation:
Overall Spatial Awareness Error = Standardized Scores (Direction Estimation Error +
Overall Navigation Error + Overall Sketching Error)
The overall spatial awareness error score ranged between 0 and 3, because
each standardized score ranged between 0 and 1.
70
CHAPTER 4
RESULTS
4.1 Relation Between Different Measures of Wayfinding
Performance
Recall that I used multi-measures of wayfinding performance. I asked people
to point an invisible destination (direction estimation task), to find the shortest route
to a destination (navigation task), to pick the layout of the environment among
possible ones, to mark an important location on the selected layout and to draw the
route between locations (sketching task). Responses on each task, should relate to one
another, because they all measure the same construct, wayfinding performance
(survey knowledge). Pearson correlations supported the expectation (Table 4.1).
71
Table 4.1: Direction estimation, navigation and sketching scores had a statistically significant
correlation with one another
Direction estimation (Direction Error)
Navigation (Overall Error)
Sketching (Overall Error)
Direction
estimation
(Direction
Error)
1.00
.293**
.297**
Navigation
(Overall
Error)
Sketching
(Overall
Error)
1.00
.327**
1.00
** p<.01 after Bonferroni5 adjustment for multiple claims (p<.003)
4.2 Relation Between the Self-Reported Navigation Strategy and
Different Tasks Measuring Wayfinding Performance
After they performed the navigation task, I asked people how often they used
the following strategies; (1) visualizing a map, (2) keeping track of the general
direction, (3) remembering the correct turns, and (4) remembering the number of
streets, turns. According to the hypothesis different task performances should relate to
different strategies.
First, I analyzed the relation between different strategies. The Pearson
Correlation in Table 4.2 show that participants who used remembering the correct
turns strategy more often also used keeping track of the general direction and
remembering the number of streets, buildings to pass strategies more often.
5
For multiple tests experimentwise error rate is the sum of procedurewise errors. Bonferroni correction
prevents experimentwise error rate climbing to an unacceptably high level. The Bonferroni correction
divides the upper limit of the significance level of the individual test by the number of tests. For
example if the significance of each test is .01 (procedurewise) and there are three test, then adjusted
significance level of each test is (experimentwise) .01/3 = .003. If each of the three tests are conducted
at the .003 level then the overall experimentwise error rate will be kept within acceptable limits.
72
Table 4.2: Remembering the correct turns strategy was associated with remembering the number of
streets, buildings to pass strategy and keeping track of general directions strategy.
Visualizing
a map
Visualizing a map
Keeping track of the general direction
Remembering the correct turns
Remembering the number of streets, buildings to pass
1.00
0.16
0.11
-0.02
Keeping
Remembering
track of
the correct
General
turns
direction
1.00
0.36**
-0.09
1.00
0.21*
* p<.05 after Bonferroni adjustment for multiple claims (p<.0125)
** p<.01 after Bonferroni adjustment for multiple claims (p<.0025)
Second, I analyzed the relation between the reported strategy and different
measures of the wayfinding performance. The first row of table 4.3 shows that lower
direction errors were associated with more frequent use of visualizing the map
strategy (negative significant correlation). The second row shows that lower
navigation errors were associated with more frequent use of all strategies except
remembering the number of streets, buildings to pass strategy. The third row shows
no significant correlation between sketching task and any strategy.
Table 4.3: Different tasks related to different navigation strategies.
Visualizing
a map
Direction (Direction Error)
Navigation (Overall Error)
Sketching (Overall Error)
-0.21*
-0.22*
-0.12
Remembering
Keeping
Remembering
the number of
track of
the correct
General
streets building
turns
direction
to pass
-0.07
-0.12
-0.12
-0.27**
-0.30**
-0.17
0.08
-0.09
-0.10
* p<.05 after Bonferroni adjustment for multiple claims (p<.0071)
** p<.01 after Bonferroni adjustment for multiple claims (p<.0014)
73
4.3 The Effect of Physical Environmental and Personal
Characteristics on Wayfinding Performance
For physical environmental characteristics, I hypothesized that environments
with Simple layout, Higher level of Physical Differentiation, Vertical or Horizontal
Differentiation (with road hierarchy) would produce better wayfinding performance
than would environments with Complex layout, Low level of Physical Differentiation
and No Vertical or Horizontal differentiation. For personal characteristics, I
hypothesized that Males and people who are More Familiar with the setting (toured
the environment more before the test) or who play Computer Games more Often
would show better performance than Females and people who are Less Familiar or
people who play Computer Games Rarely. Because the sample had a narrow age
range of young adults, I did not expect to find differences in performance associated
with age.
I took into account the effect of all factors when testing the effect of each
factor. Note the physical environmental factors depended on one another, vertical and
horizontal differentiation determined the level of physical differentiation. Thus, I used
two sets of analysis. The first set tested the effect of plan layout and the level of
physical differentiation and the second set tested the effect of plan layout, vertical and
horizontal differentiation. For each set of analysis I tested two models, because the
number of tours while exploring (a measure of familiarity) and the exploration speed
(a measure of exploration pattern) were related to one another. Hence the analysis
included four tests (Table 4.4)
74
Table 4.4: The four tests repeated for each task
Factors
Gender
Age
Familiarity (Number of Tours)
Exploration Pattern (Speed)
Game Playing
Plan Layout
Physical Differentiation
Analysis 1
(with tours)
b
a
a
a
a
a
Analysis 1
(with speed)
a
a
a
a
a
a
Analysis 2
(with tours)
a
a
a
Analysis 2
(with speed)
a
a
a
a
a
a
a
a
a
a
a
Because each task was scored multiple ways, some related to success scores,
others related to error scores (see chapter 3), these four tests were repeated for both
success and error scores. Generally, I reported the results for error scores, because
analysis of errors allowed for a more detailed study of the uncompleted tasks (see
Appendix D for detailed discussion of success scores). When there is a conflict
between results for success and error scores, I reported both.
Because this study had many measures, environmental conditions and
statistical tests, I first present a summary of findings on effect of each factor on
various tasks of wayfinding and then present the specific statistics for each task.
4.3.1 General Summary
This section does not provide the full statistics, only tables showing which
variables were significant. I followed the same procedure in reporting the general
75
results for each factor. First I present the mean standardized error scores across
conditions then I discuss the significance of difference across conditions for each task
and for the composite measure (overall spatial awareness measure).
Figure 4.1 shows the mean standardized error scores for Simple and Complex
layout conditions. The results supported the hypothesis. In each task, Simple
environments produced fewer errors than Complex ones. Table 4.5 shows the
statistical significance of the tests across the Simple and Complex layouts. On a
composite measure, overall spatial awareness (Table 4.5, last column), plan layout
produced a significant effect in all four tests. On specific measures (Table 4.5,
columns 2-4), the difference achieved significance on all three tasks (direction
estimation, navigation and sketching). For navigation and sketching, this effect
disappeared when familiarity (number of tours before the tests) was included in the
model. Success scores paralleled the results for error scores except for sketching (see
Appendix D). For sketching success scores the effect of plan layout remained
significant when familiarity was included in the analysis.
76
0.50
Standardized Error Scores
0.40
0.40 Direction
0.32 Overall
0.30
0.20
0.31 Sketching
0.24
0.23
0.22
0.25 Navigation
0.18
0.10
0.00
Simple
Complex
Figure 4.1: The standardized mean error scores for each task is higher in the Complex
environments than in the Simple ones.
*To compare overall spatial awareness score with the three tasks I used standardized scores for
each test and I divided the overall spatial awareness score by three
Table 4.5: The significance of plan layout effect on various tasks
Direction
Analysis 1 with tours **
Analysis 1 with speed **
Error Scores
Composite Measure
Overall
Overall
(Spatial Awareness Error)
Navigation Sketching
**
**
**
**
**
**
**
**
** significant at 0.01 level
Now consider the comparisons between different levels of physical
differentiation. As expected, in each task, as differentiation increased from low to
moderate to high, the mean error decreased (Figure 4.2). On the composite measure
(Table 4.6, last column), physical differentiation produced a significant effect on all
77
relevant tests. On specific measures (Table 4.6, columns 2-4), this effect achieved
significance for direction estimation and sketching. Success scores paralleled the
results for error scores except for sketching (see Appendix D). For sketching success
scores it did not achieve significance.
0.50
0.49
Direction
0.39 Overall
0.40
0.38Sketching
0.28
0.30
0.22
0.20
0.20
0.29Navigation
0.25
0.20
0.17
0.10
0.00
High
Moderate
Low
Figure 4.2: The mean error scores for each task increases as level of physical differentiation decreases
from High to Moderate to Low.
* To compare overall spatial awareness score with the three tasks I used standardized scores for each
test and I divided the overall spatial awareness score by three.
78
Table 4.6: The significance of physical differentiation effect on various tasks
Direction
Analysis 2 with tours NA
Analysis 2 with speed NA
Error Scores
Overall
Overall
ms
*
NA
NA
NA
NA
Composite Measure
**
**
NA
NA
ms marginally significant at 0.10 level
* significant at 0.05 level
NA not applicable
Now consider the comparisons between environments with and without
vertical differentiation. As expected, in each task participants in environments with
vertical differentiation had fewer errors than those in environments without it (Figure
4.3). On the composite measure (Table 4.7, last column), vertical differentiation
produced a significant effect in all tests. On specific measures (Table 4.7, columns 24), this effect achieved significance for direction estimation and sketching. Success
scores paralleled the results for error scores (see Appendix D).
Table 4.7: The significance of vertical differentiation effect on various tasks
Direction
Analysis 2 with tours ms
Analysis 2 with speed ms
Error Scores
Overall
Navigation
NA
NA
NA not applicable
79
Overall
Sketching
NA
NA
**
**
Composite Measure
NA
NA
**
**
0.50
0.40
0.38 Direction
0.34Sketching
0.32 Overall
0.30
0.25
0.20
0.25 Navigation
0.21
0.18
0.10
0.00
Present Vertical Differentiation Absent
Figure 4.3: The mean error scores for each task is lower in environments in which vertical
differentiation is Present than the ones in which vertical differentiation is Absent.
Horizontal differentiation did not perform as well as Vertical differentiation,
but it did effect the composite score in the expected directions. Participants in
environments with horizontal differentiation had fewer errors than those in
environments with no differentiation (Figure 4.4). On the composite measure (Table
4.8, last column), horizontal differentiation produced a significant effect in all tests.
On specific measures (Table 4.8, columns 2-4), only direction estimation achieved
statistical significance. For all measures, success scores produced the same results as
error scores (see Appendix D).
80
0.50
0.40
0.39Direction
0.31 Overall
0.30
0.20
0.26
0.30 Sketching
0.24
0.23
0.24 Navigation
0.19
0.10
0.00
Present
Absent
Figure 4.4: The mean error scores for each task is lower in environments in which horizontal
differentiation is Present than the ones in which horizontal differentiation is Absent.
* To compare overall spatial awareness score with the three tasks I used standardized scores for
each test and I divided the overall spatial awareness score by three.
Table 4.8: The significance of horizontal differentiation effect on various tasks
Direction
Error Scores
Overall
Navigation
NA
NA
Composite Measure
Overall
Sketching
NA
NA
NA
NA
*
*
NA not applicable
The analysis also looked at personal characteristics. For Gender, as expected
in each task males had fewer errors than females (Figure 4.5). On the composite
81
measure (Table 4.9, last column), gender produced a significant effect in all tests. On
specific measures (Table 4.9, columns 2-4), significant effects emerged for direction
estimation and navigation, but the effect for direction error disappeared when
physical differentiation was included in the analysis. Success scores yielded different
results than error scores (see Appendix D). For success scores significant effect
emerged for all three measures in all four tests.
0.50
0.40 Direction
0.40
0.35 Overall
0.34 Sketching
0.30
0.20
0.29 Navigation
0.26
0.23
0.21
0.16
0.10
0.00
Male
Female
Figure 4.5: Males had fewer mean errors than Females in all three tasks.
82
Table 4.9: The significance of gender effect on various tasks
Direction
Analysis 1 with tours
Analysis 1 with speed
Analysis 2 with tours ms
Analysis 2 with speed ms
Error Scores
Overall
Overall
*
ms
*
ms
Composite Measure
*
ms
*
*
Unexpectedly, Age did yield differences. As age decreased performance
improved6. On the composite measure (Table 4.10, last column), age produced a
significant effect, but this effect disappeared when exploration speed and physical
differentiation was included in the analysis. On specific measures (Table 4.10,
columns 2-4), the age effect achieved significance only for navigation task. Success
scores paralleled the results for error scores, except for navigation (see Appendix D).
For navigation success, the age effect disappeared when exploration speed was
included in the analysis.
6
For success scores, mean age was lower for participants who successfully complete each task. A
positive relation between age and error scores suggested that as age increases mean error increases.
The correlation was significant on navigation, sketching and composite measures.
83
Table 4.10: The significance of age effect on various tasks
Direction
Error Scores
Overall
Overall
**
*
**
*
Composite Measure
ms
ms
ms
For Familiarity, people who toured the environment more before the test
performed better7. On the composite measure (Table 4.11, column 5), familiarity
produced a significant effect in all relevant tests. On specific measures (Table 4.11,
column 2-4), the effect achieved significance on all tasks, except direction estimation.
Success scores yielded similar results as error scores, except for sketching task (see
Appendix D). For sketching success the effect did not achieve significance.
Table 4.11: The significance of familiarity effect on various tasks
Direction
Error Scores
Overall
Navigation
**
NA
**
NA
Overall
Sketching
*
NA
*
NA
Composite Measure
*
NA
*
NA
NA not applicable
7
For success scores, mean number of tours was higher for participants who successfully complete each
task. A negative relation between number of tours and error scores suggested that as number of tours
increases mean error decreases. The correlation was significant on all measures.
84
Exploration Speed also affected performance. People who explored at higher
speeds showed better performance8. On the composite measure (Table 4.12, last
column 5), exploration speed produced a significant effect in all relevant tests. On
specific measures (Table 4.12, columns 2-4), the effect achieved significance on all
tasks, except direction estimation. Success scores yielded similar results as error
scores, except for sketching task (see Appendix D). For sketching success the effect
did not achieve significance.
Table 4.12: The significance of exploration speed effect on various tasks
Direction
Error Scores
Overall
Navigation
NA
**
NA
**
Overall
Sketching
NA
ms
NA
ms
Overall Spatial
Awareness Error
Score
NA
*
NA
ms
NA not applicable
For game playing, the pattern is not clear. While more frequent game players
had lower error scores than less frequent game players (Figure 4.6), game playing
produced a significant effect on only the sketching error scores (Table 4.13). For
success scores the effect did not achieve significance on any task (see Appendix D).
8
For success scores, mean speed was higher for participants who successfully complete each task. A
negative relation between speed and error scores suggested that as speed increases mean error
decreases. The correlation was significant on all measures except direction estimation measure.
85
0.50
0.42 Sketching
0.40 0.38
0.37
0.33
0.34
0.30
0.29
0.27
0.45
Overall
0.34
0.33
0.27
0.25
0.26
0.20
0.33
0.33
0.27
0.25
0.19
0.15
0.10
0.19 Direction
0.19
0.17
0.15
0.16
0.12
0.10
Navigation
0.00
1
2
3
4
5
6
Game Playing Frequency (1 = never, 7 = all the time)
7
Figure 4.6: The standardized mean error scores in each task decreases as game playing frequency
increases.
Table 4.13: The significance of game playing effect on various tasks
Direction
Overall Spatial
Error Scores
Awareness Error
Overall
Overall
Navigation Sketching Score
*
*
*
*
4.3.2 Statistical Results
The analyses so far looked at and summarized the direction and significance
of the effect for each physical and personal characteristic across all measures. Now
86
the analyses focus on each measure and the full statistics for the overall spatial
awareness error, direction error, overall navigation and sketching error scores. For
each task, I followed the same procedure in reporting the results. First, I reported the
results for the physical environmental factors (plan layout, physical differentiation,
vertical and horizontal differentiation). Second, I reported the results for the personal
factors (gender, age, familiarity and computer game playing frequency).
I also used the same analytical procedures in data. Recall that each task
(direction estimation, navigation and sketching) was scored multiple ways, some
related to success scores, others related to error scores. As success scores yielded
frequencies of success, I used Binary Logistic regression and analyzed the effect of
all factors simultaneously9. I also used Chi Square tests and analyzed the effect of
each factor separately. As error scores yielded interval number, I used General Linear
Model (GLM) and analyzed the effect of all factors simultaneously. When there are
multiple levels10 (e.g. level of physical differentiation) I used Benferroni test to make
all pairwise comparisons between groups and adjust the pairwise significance level to
balance familywise confidence levels. Because success and error scores yielded
9
I did not use Binary Logistic Regression for overall success scores because too few participants
successfully completed the all three tasks.
10
When there are more than 2 levels of a factor, GLM does not tell which means are different from
which other. Post Hoc Range tests can determine which means differ. There are a number of Post Hoc
Range tests based on different assumptions. When equal variances are assumed, the most commonly
used ones includes Tukey’s HSD, Benferroni and Dunnett’s tests. When un-equal variances are
assumed, Tamhane’s T2 is the most commonly used test. Tukey, Benferroni and Tamhane tests
provides pairwise multiple comparasions, while Dunnett’s test compares each level against a single
level, a control mean. When testing a large number of pairs of means, Tukey's HSD test is more
powerful than the Bonferroni test. For a small number of pairs, Bonferroni is more powerful. (SPSS
Tutorial) It is based on Students t statistics (Ramsey and Schafer, 1997).
87
similar results, I reported only error scores. See Appendix D for analysis of success
scores.
4.3.2.1 Overall Spatial Awareness
Because all three tasks were related (see section 4.1), the composite measure,
the overall spatial awareness measure, prove to be a valid measure. Overall Spatial
awareness success and error scores were calculated based on success and error scores
of the three tasks. In this section I discuss the results for overall spatial awareness
error measure, see Appendix D for overall success measure. Table 4.14 and Table
4.15 show the GLM results on overall error for the first and second set of analyses.
Table 4.14: General Linear Models on overall spatial awareness error for the first set of analyses.
Overall Spatial
Awareness Error Scores
df
MS
F
Source (n = 157)
Model 1 (with tours)
Gender
Age (year born)
Game Playing
Familiarity (number of tours)
Plan Layout
Model 2 (with speed)
Gender
Age (year born)
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
88
1
1
1
1
1
2
0.92
0.74
0.39
1.10
1.87
1.50
4.44*
3.56ms
1.89
5.34*
9.02**
7.26**
1
1
1
1
1
2
0.81 3.86ms
0.57
2.72
0.39
1.88
0.84
4.03*
4.74 22.71**
1.66 7.95**
Table 4.15: General Linear Models on overall spatial awareness error for the second set of analyses.
Overall Spatial
Awareness Error Scores
df
MS
F
Source (n = 157)
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
1
1
1
1
1
1
1
0.97
4.64*
0.76 3.63ms
0.39
1.86
1.12
5.36*
1.85 8.81**
1.75 8.33**
1.01
4.81*
1
1
1
1
1
1
1
0.88
4.14*
0.59 2.80ms
0.39
1.86
0.82 3.88ms
4.71 22.26**
1.82 8.58**
1.21
5.72*
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Plan layout produced the expected effects. Environments with the Simple
layout produced lower error scores (M = 0.65, SD = 0.45, n=79) than environments
with the Complex layout (M = 0.96, SD = 0.61, n=78). After accounting for the
personal characteristics and physical differentiation (Table 4.14) and after accounting
for the personal characteristics, vertical and horizontal differentiation (Table 4.15)
this effect achieved significance.
I also tested the effect of interaction between plan layout and other physical
environmental factors (physical differentiation, vertical and horizontal differentiation)
on the overall error score (see Appendix B). Only the interaction between Plan
Layout and Horizontal differentiation achieved statistical significance. It suggested
89
that the impact of horizontal differentiation on wayfinding performance differs in
different layouts.
Level of Physical Differentiation produced the expected effects. The mean
error decreased as level of Physical Differentiation increased from LOW (M = 1.16,
SD = 0.55, n = 38) to MODERATE (M = 0.74, SD = 0.54, n = 80) to HIGH (M =
0.59, SD = 0.42, n = 39). After accounting for the personal characteristics and plan
layout, this effect achieved statistical significance (Table 4.14). Bonferroni tests11
showed that errors were significantly higher in environments with Low
Differentiation than those with Moderate and High Differentiation. However the
difference in scores did not achieve statistical significance across Moderate and High
physical differentiation (Figure 4.7).
11
Tukey and Tamhane tests gave the same result
90
High
Moderate
Low
Error Rate
Figure 4.7: The significance of difference across different physical differentiation conditions on spatial
awareness. Overlapping boxes indicate an insignificant difference and separate boxes indicate a
significant difference.
Vertical and Horizontal differentiation produced the expected effects.
Environments with Vertical (M = 0.64, SD = 0.52, n= 79) or Horizontal
Differentiation (M = 0.68, SD = 0.45, n = 79) produced lower error scores than those
with No Vertical (M = 0.97, SD = 0.54, n = 78) or No Horizontal Differentiation (M
= 0.92, SD = 0.63, n = 78). After accounting for the other personal and environmental
factors, this effect achieved significance (Table 4.15). Considering the type of vertical
differentiation, Bonferroni tests12 showed that error rates were significantly higher in
No Vertical Differentiation than any type of Vertical Differentiation (object or
building landmark) (for object landmark: M = 0.69, SD = 0.54, n = 40; for building
landmark: M = 0.59, SD = 0.51, n = 39), and the difference did not achieve statistical
significance across the two types of Vertical Differentiation (Figure 4.8). Considering
the type of horizontal differentiation, Bonferroni tests13 showed that error rates were
significantly higher in No Horizontal differentiation than any type of Horizontal
12
13
91
differentiation (width or pavement variation) (for road width variation: M = 0.68, SD
= 0.43, n = 40; for pavement variation: M = 0.69, SD = 0.48, n = 39), and the
difference did not achieve statistical significance across the two types of Horizontal
Differentiation (Figure 4.9).
(Building Landmark)
(Object Landmark)
No Vertical Differentiation
Error Rate
Figure 4.8: The significance of difference across different Vertical Differentiation conditions on overall
spatial awareness Overlapping boxes indicate an insignificant difference and separate boxes indicate a
significant difference.
(Road width)
(Road pavement)
No Horizontal
Differentiation
Error Rate
Figure 4.9: The significance of difference across different Horizontal Differentiation conditions on
overall spatial awareness. Overlapping boxes indicate an insignificant difference and separate boxes
indicate a significant difference.
92
As for personal characteristics, gender and familiarity (number of tours)
produced the expected effect on overall scores. Males had fewer errors (M = 0.64, SD
= 0.53, n = 94) than females (M = 1.04, SD = 0.51, n = 63). Overall error decreased
as the number of tours people took before the test increased (r = -.426, p<.01). After
accounting for other personal and environmental factors the gender and familiarity
effect achieved statistical significance (Table 4.14 and Table 4.15).
The age produced unexpected effect on overall error. Overall wayfinding error
increased as age increased (r = .227, p<.01). After accounting for other personal
factors and physical environmental factors, this effect achieved significance, but it
disappeared when physical differentiation and exploration speed included in the
analysis (Table 4.14 and Table 4.15).
For game playing frequency, the pattern is not clear but in general as
frequencies of game playing increased errors decreased (Table 4.16). However this
difference did not achieve significance after accounting for other personal
characteristics and physical environmental factors (Table 4.14 and Table 4.15).
Table 4.16: As frequencies of game playing increased overall spatial awareness errors decreased.
Source
Game playing frequency
Never 1 (n = 21)
2 (n = 32)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
All the time 7 (n = 17)
Mean (SD)
1.14 (0.59)
0.87 (0.55)
0.78 (0.64)
1.03 (0.50)
0.75 (0.48)
0.47 (0.36)
0.46 (0.37)
93
4.3.2.2 Direction Estimation Task
I measured direction estimation test in two ways; (1) direction success14, and
(2) direction error15. In this section I discuss the results for direction error measure
(see Appendix D for direction success measure). Table 4.17 and Table 4.18 show the
GLM results on direction error for the first and second set of analyses.
Table 4.17: General Linear Models on direction error for the first set of analyses.
Direction Error Scores
MS
F
df
Source (n = 160)
Model 1 (with number of tours)
Gender
Age (year born)
Game Playing
Plan Layout
Model 2 (with exploration speed)
Gender
Age (year born)
Game Playing
Exploration Pattern (exploration speed)
Plan Layout
1 8259.86
1 1275.43
1
224.83
1
466.08
1 26229.68
2 17888.10
2.59
0.40
0.07
0.15
8.23**
5.61**
1 8288.20
2.60
1 1393.85
0.44
1
180.06
0.06
1
58.93
0.02
1 34498.25 10.81**
2 18803.84 5.89**
** p < 0.01
14
Half of the participants were successful in their direction estimates (estimate within 20o of the true
direction)
15
Absolute difference between the true direction and the participant’s estimate varied widely in
accuracy, ranging from 0 o to 179 o (M = 56 o, SD = 60o).
94
Table 4.18: General Linear Models on direction error for the second set of analyses.
Direction Error Scores
df
MS
F
Source (n = 160)
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
1 9599.31 2.99ms
1 1475.10
0.46
1
220.54
0.07
1
500.26
0.16
1 26153.35 8.16**
1 11295.45 3.52ms
1 23795.27 7.42**
1 9620.64 3.00ms
1 1593.48
0.50
1
177.14
0.06
1
74.14
0.02
1 34614.20 10.79**
1 11728.01 3.66ms
1 24932.91 7.77**
** p < 0.01 and ms 0.05<p<0.10
Plan layout produced the expected effects on direction error. Environments
with Simple layout produced lower error scores (M = 41.43, SD = 48.22, n = 80) than
environments with Complex layout (M = 71.19, SD = 68.64, n = 80). After
accounting for personal characteristics and physical differentiation (Table 4.17) and
after accounting for the personal characteristics, vertical and horizontal differentiation
(Table 4.18) this effect achieved significance.
I also tested the effect of interaction between plan layout and other physical
on direction error score (see Appendix B). Only the interaction between plan layout
and horizontal differentiation achieved statistical significance. It suggested that the
95
impact of horizontal differentiation on wayfinding performance differs in different
layouts.
Level of Physical Differentiation produced the expected effects on direction
error scores. Mean error decreased as level of Physical Differentiation increased from
LOW (M = 87.83, SD = 62.89, n = 40) to MODERATE (M = 50.60, SD = 60.33, n =
80) to HIGH (M = 36.20, SD = 48.30, n = 40). After accounting for the personal
characteristics and plan layout, this effect achieved statistical significance (Table
4.17). Bonferroni tests16 showed that errors were significantly different (higher) in
environments with Low differentiation than those with Moderate and High
Differentiation. However the difference in scores did not achieve statistical
significance across Moderate and High Physical Differentiation (see Figure 4.7 in
section 4.3.2.1).
Vertical and Horizontal differentiation produced the expected effects on
direction error scores. For the direction error scores (Table 4.26), environments With
Vertical (M = 44.73, SD = 57.29, n = 80) or Horizontal (M = 42.08, SD = 52.56,
n=80) differentiation produced lower error scores than those with No Vertical (M =
67.89, SD = 62.70, n = 80) or No Horizontal (M = 70.54, SD = 65.67, n = 80)
differentiation. After accounting for other personal characteristics and physical
characteristics this effect achieved statistical significance (Table 4.18). For type of
vertical differentiation, Bonferroni tests17 showed that error rates were significantly
16
17
96
higher in no vertical differentiation than any type of vertical differentiation (object or
building landmark) (for object landmark: M = 41.83, SD = 59.42, n = 40); for
building landmark: M = 47.63, SD = 55.68, n = 40) and the difference did not achieve
statistical significance across two types of vertical differentiation (Figure 4.8, as in
section 4.3.2.1). For the type of Horizontal Differentiation, results showed an
unexpected effect. Bonferroni tests18 showed that error rates were significantly higher
in No Horizontal Differentiation than in environments With Road Pavement variation
(M = 38.72, SD = 54.92, n = 40). However, the difference did not achieve statistical
significance between environments With Road Width variation (M = 45.43, SD =
50.56, n =40) and No Road hierarchy or Road Pavement variation (Figure 4.10).
(Pavement Variation)
(Width Variation)
No Horizontal
Differentiation
Error Rate
Figure 4.8: The significance of difference between different types of horizontal differentiation
conditions (Overlapping boxes indicate an insignificant difference and separate boxes indicate a
significant difference) on direction estimation.
18
97
Now consider personal characteristics. Gender produced the expected effects,
with Males having fewer errors (M = 45.77, SD = 57.82, n = 95) than Females (M =
71.71, SD = 62.64, n = 65). After accounting for plan layout, vertical and horizontal
differentiation and personal factors, the effect of gender achieved significance (Table
4.18) and this effect disappeared with level of Physical Differentiation included in the
analysis (Table 4.17).
As expected, after accounting for other personal factors and physical
environmental factors, Age effect did not achieve significance for direction error
scores.
The results for Familiarity (number of tours) and Game Playing ran contrary
to expectations. After accounting for other personal factors and physical
environmental factors, Familiarity (number of tours) did not achieve significance for
direction error scores. For Game Playing, in general as Game Playing increased
errors decreased (Table 4.19). After accounting for other personal and physical
factors this effect did not achieve significance.
Table 4.19: As game playing increased direction errors decreased
Source
Computer game play frequency
Never 1 (n = 23)
2 (n = 33)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
Mean (SD)
60.74 (58.41)
58.18 (58.54)
58.93 (64.32)
79.80 (70.41)
59.61 (67.86)
34.71 (47.11)
33.53 (46.40)
98
4.3.2.3 Navigation Task
I measured an overall navigation success and error measures based on the
following specific scores.
success based on the speed19,
The overall
navigation
success score
success based on the distance error20,
success based on the turn error21,
success based on the backtracking error,
X
the speed22,
The overall
navigation
error score
the distance error23,
the turn error24,
X
the backtracking error,
Backtracking (scores 4 and 8) was dropped in calculating overall scores,
because of the low variation (only 10 people, or 10%, retracked the path).
19
58% of the participants found their way over the average speed
64% of the participants did not walk extra distance
21
68% of the participants did not make an inefficient turn
22
Participants’ speed ranged from 95 feet/sec to 336 feet/sec (M = 257 feet/sec, SD = 51 feet/sec).
23
Distance error ranged from 0 to 4.80 (M = 0.47, SD = 0.93). This indicates that, on average the extra
distance walked is half of the shortest possible distance and the maximum extra distance walked is five
times the shortest possible distance.
24
Turn error ranged from 0 to 8 (M = .74, SD = 1.51), or participants made maximum eight extra turns
and average of one extra turn.
20
99
In this section, I discuss the results for overall navigation error measure (see
Appendix D for analysis of overall navigation success measure and Appendix C for
analysis of specific wayfinding scores). Table 4.20 and Table 4.21 show the GLM
results on overall navigation error for the first and second set of analyses.
Table 4.20: General Linear Models on overall navigation error for the first set of analyses.
Navigation Error Scores
df
MS
F
Source (n = 157)
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
100
1
1
1
1
1
2
0.63
1.13
0.38
1.39
0.31
0.13
4.07*
7.31**
2.45
8.95**
1.99
0.81
1
1
1
1
1
2
0.42 2.78ms
0.75
4.93*
0.32
2.13
1.82 11.97**
2.10 13.83**
0.16
1.04
Table 4.21: General Linear Models on navigation error for the second set of analyses.
Navigation Error Scores
df
MS
F
Source (n = 157)
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
1
1
1
1
1
1
1
0.63
1.15
0.38
1.40
0.30
0.21
0.03
4.06*
7.41**
2.45
9.01**
1.97
1.37
0.22
1
1
1
1
1
1
1
0.43 2.85ms
0.76
5.01*
0.32
2.13
1.80 11.81**
2.09 13.75**
0.21
1.37
0.07
0.49
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Plan layout produced the expected effects on overall navigation error scores.
Environments with Simple plan layout produced lower error scores (M = 0.43, SD =
0.38, n = 79) than environments with Complex plan layout (M = 0.61, SD = 0.52, n =
78). After accounting for other environmental and personal factors, the effect of plan
layout achieved statistical significance (Table 4.20 and Table 4.21). This effect
disappeared when number of tours was included in the analysis.
None of the interactions between plan layout and other physical
achieved significance on navigation error score (see Appendix B).
101
Level of physical differentiation produced the expected direction of effect on
navigation. Higher differentiation yielded lower navigation errors (for LOW
Differentiation: M = 0.70, SD = 0.45, n = 38; for MODERATE Differentiation: M =
0.49, SD = 0.46, n = 80; for HIGH Differentiation: M = 0.41, SD = 0.43, n = 39), but
after accounting for plan layout and personal characteristics this difference did not
reach statistical significance (Table 4.20)
Vertical and Horizontal differentiation produced the expected direction of
effects on navigation. Environments with Vertical or Horizontal differentiation
produced lower error scores (for Vertical Differentiation: M = 0.43, SD = 0.42, n =
78; for Horizontal Differentiation: M = 0.47, SD = 0.47, n = 78) than those with No
Vertical (M = 0.61, SD = 0.49, n = 79) or No Horizontal differentiation (M = 0.57,
SD = 0.45, n = 79), but after accounting for the other personal and physical
characteristics, both effects did not reach statistical significance (Table 4.21).
Now consider personal characteristics. Gender produced the expected effects
on navigation. Males showed lower error scores (M = 0.39, SD = 0.37, n = 94) than
females (M = 0.71, SD = 0.52, n = 63). After accounting for the other personal and
physical environmental factors this effect achieved statistical significance (Table 4.20
and Table 4.21).
Contrary to expectations, age effect achieved statistical significance for
navigation (Table 4.20 and Table 4.21). Navigation error increased as age increased (r
= .294, p<.01).
102
Familiarity produced the expected effect. Overall navigation error decreased
as the number of tours people took before the test to learn the setting increased (r = .425, p<.01), and after accounting for other personal and environmental factors the
familiarity effect achieved statistical significance (Table 4.20 and Table 4.21).
For game playing, in general more frequent game players had fewer errors
than less frequent game players (Table 4.22). However, after accounting for other
personal and environmental factors, game playing effect did not achieve statistical
significance (Table 4.20 and Table 4.21).
Table 4.22: More frequent game players had fewer navigation errors than less frequent game players.
Source
Never 1 (n = 23)
2 (n = 33)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
Mean (SD)
0.88 (0.57)
0.64 (0.53)
0.45 (0.43)
0.60 (0.50)
0.38 (0.28)
0.25 (0.20)
0.36 (0.30)
103
4.3.2.4 Sketching Task
I measured an overall sketching success and error measures based on the
following specific scores.
success based on map selection25,
success based on locating the MARKET sign exactly26,
success based on drawing the route segments27,
success based on drawing the sequence of route turns28,
Overall
Sketching
Success
Score
map selection error
MARKET sign location error (intersection or road)29,
Overall
Sketching
Error
Score
MARKET sign distance error30,
route segment error31,
route turn error32,
In this section, I discuss the results for overall sketching error measure (see
Appendix D for analysis of overall sketching success measure and Appendix C for
25
39% of the participants selected the correct map.
39% located the MARKET sign exactly.
27
33% drew the route segments exactly.
28
61% of the participants correctly drew the route turn sequence.
29
67%of the participants correctly located the MARKET sign at an intersection or on the road.
30
MARKET sign distance error ranged from 0 to 0.77 (M= 0.12, SD=0.16).
31
The number of segments incorrectly (participant walked but failed to draw + participant did not walk
but drew) drawn ranged from 0 to 27 (M=5.03, SD = 6.06)
32
The number of turns incorrectly drawn ranged from 0 to 15 (M=1.9, SD= 3.3).
26
104
analysis of specific sketching scores). Table 4.23 and Table 4.24 show the GLM
results on overall sketching error for the first and second set of analyses.
Table 4.23: General Linear Models on overall sketching error for the first set of analyses.
Sketching Error Scores
df
MS
F
Source (n = 160)
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
105
1
1
1
1
1
2
0.11
0.03
1.02
1.02
0.24
0.58
0.55
0.13
5.10*
5.12*
1.22
2.92ms
1
1
1
1
1
2
0.08
0.02
1.03
0.69
1.41
0.69
0.41
0.10
5.10*
3.44ms
7.02**
3.45*
Table 4.24: General Linear Models on overall sketching error for the second set of analyses.
Sketching Error Scores
df
MS
F
Source (n = 160)
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
1
1
1
1
1
1
1
0.09
0.04
1.02
1.04
0.24
1.55
0.03
0.46
0.20
5.19*
5.30*
1.21
7.89**
0.14
1
1
1
1
1
1
1
0.08
0.38
0.04
0.18
1.04
5.24*
0.63 3.18ms
1.39 6.99**
1.66 8.34**
0.07
0.35
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Plan layout produced the expected effects on sketching. Environments with
Simple plan layout produced lower error scores (M = 0.56, SD = 0.49, n = 80) than
environments with Complex plan layout (M = 0.72, SD = 0.49, n = 80). After
accounting for other physical environmental factors and personal factors, the effect of
plan layout achieved statistical significance. However, the effect disappeared when
number of tours was included in the analyses (Table 4.23 and Table 4.24).
None of the interactions between Plan Layout and other physical
environmental
factors
(Physical
Differentiation,
Vertical
and
Horizontal
differentiation) achieved statistical significance on sketching error scores (see
Appendix B).
106
Level of physical differentiation produced the expected effects on sketching.
Higher Differentiation produced lower sketching errors (for LOW Differentiation: M
= 0.88, SD = 0.52, n = 40; for MODERATE Differentiation: M = 0.59, SD = 0.48, n
= 80; for HIGH Differentiation: M = 0.51, SD = 0.41, n = 40). After accounting for
plan layout and personal characteristics this difference reached statistical significance
(Table 4.23).
Bonferroni test33 showed that errors were significantly higher in
environments with Low Physical Differentiation than that with Moderate and High
Physical Differentiation. However, the difference did not achieve statistical
significance for the comparisons across Moderate and High Physical Differentiation
(see Figure 4.7 in section 4.3.2.1).
Vertical and Horizontal differentiation produced the expected effects on
sketching. Environments with Vertical or Horizontal differentiation produced lower
error scores (for Vertical Differentiation: M = 0.50, SD = 0.44, n = 80); for
Horizontal Differentiation: M = 0.60, SD = 0.46, n = 80) than those with No Vertical
(M = 0.79, SD = 0.51, n = 80) or Horizontal differentiation (M = 0.69, SD = 0.53, n =
80). After accounting for the other personal and physical environmental
characteristics
the
difference
reached
statistical
significance
for
Vertical
Differentiation but not for Horizontal Differentiation (Table 4.24). For type of
vertical differentiation, the analysis produced mixed results. As expected, Bonferroni
tests34 showed that error rates were significantly higher in no vertical differentiation
33
34
107
than environments with building landmark (M = 0.43, SD = 0.39, n = 40), and the
scores did not differ between environments with object landmark (M = 0.56, SD =
0.48, n = 40) and building landmark. Unexpectedly the scores did not differ between
environments with no landmark and object landmark (Figure 4.11).
(Building Landmark)
(Object Landmark)
No Vertical Differentiation
Error Rate
Figure 4.9: The significance of difference between different types of Vertical differentiation
conditions (Overlapping boxes indicate an insignificant difference and separate boxes indicate a
significant difference) on direction estimation.
Now consider the personal characteristics. Gender and Familiarity produced
the expected direction of effects. Males showed lower error scores (M = 0.53, SD =
0.51, n = 95) than Females (M = 0.80, SD = 0.42, n = 65). Overall sketching error
decreased as the number of tours people took before the test increased (r = -.356,
p<.01). After accounting for other personal and physical environmental
characteristics the effect of Gender did not achieve significance but the effect of
Familiarity achieved statistical significance (Table 4.23 and Table 4.24).
As expected after accounting for other personal and environmental factors the
Age effect did not achieve statistical significance on sketching error.
108
As expected, More Frequent Game Players had fewer errors than Less
Frequent game players (Table 4.25). This effect achieved statistical significance after
accounting for other personal and physical characteristics (Table 4.23 and Table
4.24).
Table 4.25: More Frequent Game Players had fewer sketching errors than Less Frequent game players
Source
Never 1 (n = 23)
2 (n = 33)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
Mean (SD)
0.97 (0.56)
0.67 (0.46)
0.62 (0.52)
0.77 (0.47)
0.62 (0.46)
0.40 (0.40)
0.28 (0.28)
In sum, the results support the hypotheses on physical environmental factors
and partially supported the hypothesis on personal factors. Participants in
environments with higher levels of physical differentiation, simple layout, vertical
and horizontal differentiation performed better (lower error and higher success rates).
For personal characteristics, males, younger people and people who became more
familiar with the setting showed better performance on all tasks. Game playing
produced mixed results.
109
CHAPTER 5
CONCLUSION
This study is unique in three ways. First, it developed a composite measure to
test wayfinding performance. Second, it tested the combined effect of physical
environmental and personal characteristics on wayfinding performance in a controlled
study. Third, it used a new technology, Virtual Reality, which has great potential for
research on wayfinding as well as other research on design and policy.
First consider the composite measure of wayfinding. I measured wayfinding
performance with three measures of survey knowledge of a space. While each
measure may capture a different aspect of spatial knowledge, the results found the
three measures as interrelated. This suggested that a composite measure combining
the three into an overall spatial awareness measure would be a more valid measure
than any one measure taken separately. People may know the location of and
direction to a destination but may not know the route to it, or they may find the best
route but not be able to draw it. The use of multiple measure reduces the effect of the
unique bias in each measure (Campell and Fiske, 1959; Judd et al., 1991). Further
110
research should test the advantage of using this comprehensive measure over separate
measures of survey knowledge used by earlier studies.
My composite measure was based on three tasks related to survey knowledge.
Future research may take into account other tasks, such as estimating distance
(Belingard and Peruch, 2000; Jansen-Osman and Berendt, 2002; Peruch et al., 1989).
A similar recommendation applies to measuring landmark and route knowledge. For
landmark knowledge, future research might test other measures such as recognition of
scenes or naming the landmarks (Aginsky et al., 1997; Appleyard, 1969; Carr and
Schissler, 1969; Lynch and Rivkin, 1976; Murakoshi and Kawai, 2000; Wilson,
1999) and for route knowledge future research might test other measures such as
giving the directions or reproducing a known route (O’Neill, 1991a; Fenner et al.,
2000; Ward et al., 1986; Brown et al., 1998). Future research may also compare a
composite measure for each spatial knowledge type (landmark, route and survey).
For physical environmental characteristics, the results for the composite
measure (Table 5.1 column 6) of spatial awareness supported my hypothesis: the
Simple layouts, Higher Physical Differentiation, Vertical or Horizontal differentiation
yielded better wayfinding performance than Complex layouts, Lower Physical
differentiation, and No Vertical or Horizontal differentiation, and the results held
after taking into account the other personal and physical environmental
characteristics. Researchers have found similar results for layout complexity (AbuObeid, 1998; Weisman, 1981), but their definition of layout complexity was based on
111
subjective judgements. This study used an objective measure of complexity - the
number of interconnections at decision points (O’Neill, 1991a), and my findings
agree with those of O’Neill’s, suggesting that this objective measure has external
validity. For the level and type of physical differentiation this study showed that a
little differentiation (either Vertical or Horizontal) made a major difference in
people’s understanding of spatial layout but that additional differentiation (either
Vertical or Horizontal differentiation) did not produce much improvement. Although,
previous researchers (Passini et al., 2000; Abu-Ghazzeh, 1996) also discussed that
physical monotony increases wayfinding difficulties, no published study previous to
mine tested the effect of differentiation in a systematic and controlled way. My results
provided clear evidence that lack of differentiation caused disorientation. The
direction estimation measure (Table 5.1 column 3) paralleled the results of the
composite measure. The navigation measure (Table 5.1 column 4) only differentiated
performance on plan layout, but this effect disappeared with familiarity (number of
tours before the test) included in the analysis and the navigation measure did not
achieve significant difference for physical differentiation and its components, vertical
and horizontal differentiation. The failure on the navigation task may result from a
measurement artifact. The measures of navigation included a speed variable. My
observations indicated that although people knew the route in the differentiated
environments, they stopped to look at the differentiation. When navigation score is
calculated without the speed variable the sample did not show much variance on
navigation score, 100 of 160 people had error score of “0.” This lack of variability
112
may have accounted for the insignificant finding. When speed is one variable that
measures navigation performance, future research should control the difference
between stopping to look at the differentiation and stopping due to the confusion
about the route to follow. The sketching scores (Table 5.1 column 5) differed
significantly for plan layout, but this effect disappeared with familiarity (number of
tours) in the analysis. Sketching scores also differed for physical differentiation and
one of its components, vertical differentiation, but it did not differ for the other
component, horizontal differentiation. This may suggest that vertical differentiation
(presence of landmarks) is more effective than horizontal differentiation (presence of
road hierarchy) in sketching. Accuracy in sketching task requires recall and certain
familiarity with the setting parallels previous studies. Some studies found that
environmental learning is primarily based on vertical elements (landmarks) (Hart and
Moore, 1973; Siegel and White, 1975 as cited in Evans, 1980), and that vertical
elements defined space better than horizontal ones (Thiel et al., 1986; Hayward and
Franklin, 1984), but others found that people rely on horizontal elements (paths) in a
novel environment but when they are more familiar with the setting they rely more on
vertical elements (landmarks) (Appleyard, 1970, 1976; Lynch, 1960 as cited in
Evans, 1980).
113
Table 5.1: The significance of the effects of physical environmental characteristics on various
measures of wayfinding performance.
The factor
Hypothesis*
Complex <
Simple
Low <
Physical
Moderate <
differentiation
High
No Vertical
Vertical
Diff. < Vertical
differentiation
Diff.
No Horizontal
Horizontal
Diff. <
differentiation
Horizontal Diff.
* poor performance < good performance
** S Significant
Sc Conditional significance
Plan Layout
Direction
Estimation
**
Navigation
**
Sketching
**
Overall
Spatial
Awareness
**
S
Sc
Sc
S
S
S
S
S
S
S
S
S
For personal characteristics, as expected, on the composite measure (Table 5.2
column 6) Males performed better (lower error scores) than Females, and
performance improved with Familiarity. Unexpectedly, Age produced a significant
effect and Game Playing did not. The effect of Age disappeared when physical
differentiation and exploration speed included in the analysis. On specific measures,
Males did better than Females on direction estimation and navigation, but physical
differentiation removed the difference on direction estimation (Table 5.2, column 3-5,
row 2). The failure of gender effect on sketching task may relate to the confounding
effect between game playing and gender. The data showed that males tended to play
computer games more often than females. The significant effect of game playing on
sketching task might have masked the effect of gender on sketching task. As number
of tours (Familiarity) increased, navigation and sketching scores improved (Table 5.2,
114
column 3-5, row 3) and scores did not change significantly for direction estimation
task. Perhaps accurately estimating direction is so hard that it might require much
time to learn. Future research may test the effect of familiarity on direction
estimation, with a wider range of familiarity, over a longer time period. Increases in
age were associated with decreases in navigation performance (Table 5.2, column 35, row 4), and increases in game playing frequency were associated with
improvements in sketching (Table 5.2, column 3-5, row 5). The unexpected effect of
Age on the navigation task might suggest that even within the narrow age group, the
older people had less experience with computers, producing the lower scores of
navigation on computer. Although, I controlled for game playing (related to computer
experience), I did not measure or study the effect of computer use. Further research
may test the relation between age, game playing and computer use with a wider range
of age in respondents. Frequency of computer game playing also produced
unexpected results. It did not have any effects except for sketching error. Again the
confounding effect between game playing frequency and gender may be the cause of
this unexpected effect. The significant effect of gender on direction estimation and
navigation task might have masked the effect of game playing on both tasks and also
on overall spatial awareness measure. Future work needs to test males and females
with similar levels of game playing experience. Moreover, future studies might also
seek a more refined measure for game playing frequency based on experiencing
action video games rather than my more general measure of computer game playing.
115
Table 5. 2: The significance of the effects of personal characteristics on various measures of
wayfinding performance.
The factor
Hypothesis*
Direction
Estimation
**
Female < Male
Sc
Less Tours <
More tours
No difference
Less frequent <
Game playing
More frequent
* poorer performance < better performance
** S Significant
Sc Conditional significance
Gender
Familiarity (number
of tours)
Age
Navigation
**
Sketching
**
S
S
S
Overall
Spatial
Awareness
**
S
S
Sc
S
S
Future research on physical factors should test more and different kinds of
plan layouts, landmarks and road hierarchy. Particularly, for landmarks this study
tested only local landmarks, future research should also look at the global landmarks.
It should also test other physical factors, such as naturalness, visibility and signage,
all of which may affect wayfinding.
This study used a hypothetical residential neighborhood. Whether the results
apply to different kinds of neighborhoods or different places, such as campuses,
airports, hospitals, remains to be seen, but there is little reason to expect the results
not to generalize. Moreover, when one makes such changes to optimize mobility, test
should be done to see how well they work after implementation in real environments.
Future research may also look at different populations. In this study the
participants were direct learners, who actively explored with no purpose except
learning the setting. Previous studies discussed the differences between map and
116
direct learners (Throndyke and Hayes-Roth, 1982; Moeser, 1988; Giraudo and
Pailhous, 1994, Taylor and Tversky, 1996; Rossano and Moak, 1998; Rossano et al.
1999), between active and passive navigators (Carr and Schissler, 1969; Appleyard,
1970; Peruch et al., 1995; Williams et al, 1996), and between people with goals35 and
no goals while learning (Magliano et al., 1995; Rossano and Reardon 1999).
Research for different purpose might capture these different groups. Researchers also
found cultural difference between people’s perception (Altman and Chemers, 1980;
Gulick, 1963) and preference (Nasar, 1984; Canter and Thorne, 1972; Sonnenfeld,
1969) of physical environments which may apply to wayfinding behavior. Some
objects, such as landmarks or road pavement variation, may be a clear orientation cue
for one culture but not for another culture (Lozano, 1992). Using controlled
conditions like this one, future research should explore wayfinding behavior in
different cultures.
I used virtual reality (VE) as a simulation tool. VEs have a range of use in
other areas including entertainment, manufacture, architecture. However, they are
rarely used to study wayfinding behavior in different settings. Studies used VEs to
train various groups of people (firefighters, pilots) to develop spatial knowledge.
Such studies consistently showed that people who navigated in VEs showed similar
accuracy in spatial knowledge tests to that of people who navigated in equivalent
real-world buildings (Ruddle et al., 1997, 1998; Throndyke and Hayes-Roth, 1982;
35
Having a goal destination in mind or having a goal to attend landmarks, routes or the configuration
of the environment.
117
Rossona et al. 1999; Wilson et al. 1996). Moreover studies also showed that the
spatial information gained in VEs transfers to real environments (Cromby et al.,
1996b; Bliss et al., 1997; Wilson et al., 1996). Thus, the findings through simulation
should apply to on-site experience.
Using VEs is particularly beneficial when it is hard to gather participants for a
study that involves movement (Ishikawa et al. 1998). Because movement in virtual
environments does not require physical effort people may be more willing to do some
spatial tasks in VEs than in real ones. My observations showed that people enjoyed
participating in a study that used VEs. Many of them described it as like playing a
computer game and found the spatial knowledge tasks as challenging and interesting.
In addition, VEs are relatively inexpensive compared to other simulation tools such as
full scale mock-ups, or simulation booths; and once a VE is created it is relatively
easy to change the features for a variety of tests.
There are numerous hardware and software products that can produce
convincing three-dimensional virtual environments. This study used a personal
computer to display visual images and a gaming software to create simulations. A
personal computer is a non-immersive36 visual display device. Although some
researchers argued that immersive systems, such as Head Mounted Displays (HMD),
shutter glasses, increased the feeling of realism (Romano and Brna, 2001, Youngblut
36
The level of immersion is related to the extent the VR system isolates the user from the real
environment. Immersion refers to one’s perception of being enveloped by, included in, interacting with
the environment (Witmer and Singer, 1998). It is argued that the higher the level of immersiveness the
closer the virtual environment experience to natural experience (). The VEs are more realistic if the
level of immersion is high.
118
et al., 1996), they usually have side affects. People who experience a simulated
environment through immersive hardware reported to suffer from nausea and
dizziness (Wilson et al., 1997b). Such feelings occur due to the delay between
movements of the user’s head and updating of the screen image. However, no study
reported such discomfort for people experiencing VE through desktop computers, and
my study also found no such problems. Hence, desktop computers tended to be more
advantageous than immersive systems when participants’ physical and mental state is
considered. Research rarely used gaming software to study spatial behavior. This
study suggests that the gaming software is a very promising tool for research on
spatial knowledge, because it is commercially available, and inexpensive. It also has
an user-friendly interface which makes it easy to learn and use.
Future research may compare different factors in the simulation, such as
colors, moving speed, and width of view, which affect the accuracy of the VE relative
to real environments. Moreover the side-effects on participant should also be
investigated.
Finally, this study has implications for policy design and planning in relation
to wayfinding. To improve wayfinding and spatial knowledge, designers should have
simpler layouts with some differentiation. They can use the objective measure of
complexity (average number of interconnections at choice points) to assess
complexity before or after construction. Designers and planners can also use the VE
technology to test and refine designs. It allows one to easily control physical factors.
119
It can be used to understand the physical environmental requirements to ease
wayfinding difficulties for different populations, such as children, elderly, Alzheimer
patients, visually impaired people; and it can explore a range of other relationships
between the environment and behavior. For planners and designers understanding the
effect of physical factors is more important than understanding the effect of personal
factors. As Lynch (1960) and Nasar (1998) noted members of people have a
substantial agreement on understanding of the physical environment. With research
like this one, urban designers can increase their sensitivity to such group images and
can shape environments to improve the quality of life for millions of people.
120
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APPENDIX A
THE WRITTEN DESCRIPTION ABOUT THE STUDY
The computer based survey will take approximately 5-7 minutes. There are no
right or wrong answers.
The procedure is as follows: You will explore a virtual residential
environment (neighbourhood) up to four minutes. A camcorder directed at the
monitor will record your movements in the Virtual Environment. Please pay attention
to the physical environment. After the exploration period you will be asked questions
that focus on environmental knowledge.
By participating in this survey you will both help a student and entertain
yourself, by playing a computer game. Your answers will be kept confidential and
anonymous. You can choose not to answer any question that makes you feel
uncomfortable and you can withdraw your answers from the study if you wish.
136
APPENDIX B
THE EFFECT OF INTERACTION BETWEEN
PHYSICAL ENVIRONMENTAL FACTORS ON ERROR
SCORES
137
Appendix B.1 General Linear Models on Comprehensive Measure (Overall
Spatial Awareness)
Table B.1: General Linear Models with the interaction between plan layout and level of physical
differentiation.
Gender
Age
Game Playing
Plan Layout (PL)
Level of Differentiation (LD)
Interaction (LD * PL)
Gender
Age
Game Playing
Plan Layout (PL)
Type III
Sum of
Squares
df
Mean
Square
0.95
0.71
0.35
0.95
1.55
3.07
0.44
1
1
1
1
1
2
2
0.95
0.71
0.35
0.95
1.55
1.53
0.22
0.85
0.56
0.35
0.67
4.00
3.37
0.43
1
1
1
1
1
2
2
0.85 4.10*
0.56 2.71
0.35 1.70
0.67 3.23
4.00 19.16**
1.68 8.08**
0.21 1.02
* p < 0.05, ** p < 0.01
138
F
4.59*
3.45
1.69
4.57*
7.48*
7.42**
1.05
Table B.2: General Linear Models with the interaction between plan layout and vertical and horizontal
differentiation.
Type III
Sum of df
Squares
Gender
Age
Game Playing
Plan Layout (PL)
Vertical Differentiation (VD)
Horizontal Differentiation (HD)
Interaction (PL * VD)
Interaction (PL * HD)
Gender
Age
Game Playing
Plan Layout (PL)
Mean
Square
F
1.08
0.71
0.36
1.01
1.90
1.75
1.03
0.02
1.12
1
1
1
1
1
1
1
1
1
1.08
0.71
0.36
1.01
1.90
1.75
1.03
0.02
1.12
1.02
0.58
0.38
0.61
4.62
1.84
1.23
0.01
1.04
1
1
1
1
1
1
1
1
1
1.02 4.90*
0.58 2.80
0.38 1.83
0.61 2.96
4.62 22.30**
1.84 8.86**
1.23 5.93*
0.01 0.05
1.04 5.03*
* p < 0.05, ** p < 0.01
139
5.29*
3.45
1.78
4.96*
9.28**
8.55**
5.01*
0.11
5.49*
Appendix B.2 General Linear Models on Direction Estimation Task
Table B.3: General Linear Models with the interaction between plan layout and level of
differentiation.
Type III
Sum of df
Squares
Gender
Age
Game Playing
Plan Layout (PL)
Gender
Age
Game Playing
Plan Layout (PL)
Mean
Square
F
8751.48
1008.69
360.95
190.06
20703.52
36643.67
9697.52
1 8751.48
1 1008.69
1 360.95
1 190.06
1 20703.52
2 18321.84
2 4848.76
2.76
0.32
0.11
0.06
6.54*
5.79**
1.53
9043.32
1183.79
292.42
5.15
26623.88
38329.55
9919.75
1 9043.32
1 1183.79
1 292.42
1
5.15
1 26623.88
2 19164.78
2 4959.87
2.85
0.37
0.09
0.00
8.40**
6.05**
1.57
* p < 0.05, ** p < 0.01
140
differentiation.
Type III
Sum of df
Squares
Gender
Age
Game Playing
Plan Layout (PL)
Gender
Age
Game Playing
Plan Layout (PL)
Mean
Square
F
11536.11
1071.72
305.56
220.73
27108.28
11242.06
24133.45
0.08
16407.02
1 11536.11
1 1071.72
1 305.56
1 220.73
1 27108.28
1 11242.06
1 24133.45
1
0.08
1 16407.02
12076.20
1334.21
224.17
37.55
33324.44
11807.78
25204.67
7.95
16652.40
1 12076.20 3.85
1 1334.21 0.42
1 224.17 0.07
1
37.55 0.01
1 33324.44 10.61**
1 11807.78 3.76
1 25204.67 8.03*
1
7.95 0.00
1 16652.40 5.30*
* p < 0.05, ** p < 0.01
141
3.68
0.34
0.10
0.07
8.64**
3.58
7.69*
0.00
5.23*
Appendix B.3 General Linear Models on Navigation Task
Table B.5: General Linear Models with the interaction between plan layout and level of differentiation
Type III
Sum of df
Squares
Gender
Age
Game Playing
Plan Layout (PL)
Gender
Age
Game Playing
Plan Layout (PL)
Mean
Square
F
0.60
1.11
0.36
1.31
0.21
0.26
0.12
1
1
1
1
1
2
2
0.60
1.11
0.36
1.31
0.21
0.13
0.06
0.40
0.73
0.31
1.73
1.70
0.33
0.10
1
1
1
1
1
2
2
0.40 2.58
0.73 4.78*
0.31 2.00
1.73 11.25**
1.70 11.08**
0.16 1.07
0.05 0.32
* p < 0.05, ** p < 0.01
142
3.87
7.10*
2.29
8.41**
1.36
0.84
0.38
differentiation
Type III
Sum of df
Squares
Gender
Age
Game Playing
Plan Layout (PL)
Gender
Age
Game Playing
Plan Layout (PL)
Mean
Square
F
0.67
1.13
0.36
1.30
0.32
0.21
0.04
0.00
0.11
1
1
1
1
1
1
1
1
1
0.67
1.13
0.36
1.30
0.32
0.21
0.04
0.00
0.11
0.46
0.76
0.32
1.66
2.07
0.21
0.08
0.01
0.06
1
1
1
1
1
1
1
1
1
0.46 3.01
0.76 4.97*
0.32 2.05
1.66 10.76**
2.07 13.47**
0.21 1.38
0.08 0.50
0.01 0.05
0.06 0.38
* p < 0.05, ** p < 0.01
143
4.26*
7.23*
2.33
8.33**
2.04
1.37
0.23
0.03
0.70
Appendix B.4 General Linear Models on Sketching Task
Table B.7: General Linear Models with the interaction between plan layout and level of differentiation
Type III
Sum of df
Squares
Gender
Age
Game Playing
Familiarity (Exploration Cover)
Plan Layout (PL)
Gender
Age
Game Playing
Plan Layout (PL)
Mean
Square
F
0.12
0.03
1.01
0.95
0.26
1.15
0.02
1
1
1
1
1
2
2
0.12
0.03
1.01
0.95
0.26
0.58
0.01
0.60
0.13
5.04*
4.72*
1.31
2.86
0.06
0.10
0.02
1.03
0.64
1.41
1.37
0.03
1
1
1
1
1
2
2
0.10
0.02
1.03
0.64
1.41
0.69
0.02
0.48
0.10
5.04*
3.13
6.93*
3.37*
0.08
* p < 0.05, ** p < 0.01
144
differentiation
Type III
Sum of df
Squares
Gender
Age
Game Playing
Plan Layout (PL)
Gender
Age
Game Playing
Plan Layout (PL)
Mean
Square
F
0.09
0.03
1.03
1.09
0.22
1.53
0.03
0.22
0.22
1
1
1
1
1
1
1
1
1
0.09
0.03
1.03
1.09
0.22
1.53
0.03
0.22
0.22
0.46
0.16
5.27*
5.56*
1.14
7.83*
0.14
1.10
1.15
0.08
0.03
1.06
0.60
1.38
1.66
0.07
0.16
0.20
1
1
1
1
1
1
1
1
1
0.08
0.03
1.06
0.60
1.38
1.66
0.07
0.16
0.20
0.40
0.16
5.33*
3.01
6.94*
8.32**
0.35
0.82
0.99
* p < 0.05, ** p < 0.01
145
APPENDIX C
THE STATISTICAL ANALYSES ON SPECIFIC
MEASURES (ERROR SCORES) OF NAVIGATION AND
SKETCHING TASK
146
Appendix C.1 The Specific Measures (Error Scores) of Navigation Task
Table C.1: General Linear Models on Speed for the first set of analyses.
Type III
Sum of df Mean Square
Squares
Gender
Age
Game Playing
Plan Layout
Level of Differentiation
Gender
Age
Game Playing
Plan Layout
F
24560.83
4843.79
6557.58
57686.52
12007.08
2893.33
1
1
1
1
1
2
24560.83 18.49**
4843.79 3.65ms
6557.58 4.94*
57686.52 43.42**
12007.08 9.04**
1446.66 1.09
16247.06
1049.63
5107.14
75448.40
2913.89
3893.26
1
1
1
1
1
2
16247.06 13.44**
1049.63 0.87
5107.14 4.22*
75448.40 62.39**
2913.89 2.41
1946.63 1.61
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
147
Table C.2: General Linear Models on Speed for the second set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
25155.83
4993.52
6538.79
58018.03
12125.04
819.50
15.68
1
1
1
1
1
1
1
25155.83 18.74 **
4993.52 3.72 ms
6538.79 4.87 *
58018.03 43.22 **
12125.04 9.03 **
819.50 0.61
15.68 0.01
17161.01
1110.89
5109.93
75144.27
2878.28
772.83
483.63
1
1
1
1
1
1
1
17161.01 13.98 **
1110.89 0.91
5109.93 4.16 *
75144.27 61.22 **
2878.28 2.34
772.83 0.63
483.63 0.39
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Table C.3 General Linear Models on Turn Error for the first set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
0.29
10.46
1.17
1.29
8.76
1.56
1
1
1
1
1
2
0.29
10.46
1.17
1.29
8.76
0.78
0.14
4.98*
0.56
0.61
4.17*
0.37
0.14
8.81
1.03
1.99
16.41
1.71
1
1
1
1
1
2
0.14
8.81
1.03
1.99
16.41
0.86
0.07
4.20*
0.49
0.95
7.83**
0.41
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
148
Table C.4: General Linear Model on Turn Error for the second set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
0.26
10.58
1.16
1.30
8.73
1.70
0.22
1
1
1
1
1
1
1
0.26
10.58
1.16
1.30
8.73
1.70
0.22
0.13
8.94
1.04
1.92
16.34
1.67
0.31
1
1
1
1
1
1
1
0.13
8.94
1.04
1.92
16.34
1.67
0.31
F
0.12
5.04 *
0.55
0.62
4.16 *
0.81
0.11
**
0.06
4.27 *
0.50
0.92
7.80 **
0.80
0.15
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Table C. 5: General Linear Model on Distance Error for the first set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
0.13
3.17
0.49
0.04
9.44
0.88
1
1
1
1
1
2
0.13 0.17
3.17 4.09*
0.49 0.63
0.04 0.05
9.44 12.19**
0.44 0.57
0.13
2.99
0.49
0.02
11.94
0.92
1
1
1
1
1
2
0.13 0.16
2.99 3.86ms
0.49 0.63
0.02 0.03
11.94 15.41**
0.46 0.60
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
149
Table C.6: General Linear Model on Distance Error for the second set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
0.12
3.19
0.49
0.04
9.43
0.74
0.27
1
1
1
1
1
1
1
0.12 0.15
3.19 4.12*
0.49 0.63
0.04 0.05
9.43 12.19**
0.74 0.95
0.27 0.35
0.11
3.01
0.49
0.02
11.92
0.75
0.29
1
1
1
1
1
1
1
0.11 0.15
3.01 3.89ms
0.49 0.63
0.02 0.03
11.92 15.40**
0.75 0.96
0.29 0.38
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
150
Appendix C.2 The Specific Measures (Error Scores) of Sketching Task
Table C.7: Binary Logistic Regression on MARKET Sign Location Error (at an intersection or on the
road) for the first set of analyses.
df
Gender
Age
Game Playing
Plan Layout
Moderate Versus High
Low versus High
Gender
Age
Game Playing
Plan Layout
Low versus High
B
S.E.
1 -0.01
1 0.01
1 0.06
1 0.54
1 0.59
2
1 0.85
1 0.76
0.44
0.04
0.12
0.43
0.39
1 -0.09
1 0.00
1 0.06
1 0.01
1 0.93
2
1 0.89
1 0.79
0.45
0.04
0.12
0.00
0.37
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
151
0.43
0.52
0.43
0.51
Wald
0.00
0.03
0.31
1.54
2.30
4.10
3.92*
2.15
0.04
0.00
0.23
2.52
6.30*
4.44
4.23*
2.37
Table C.8: Binary Logistic Regression on MARKET Sign Location Error (at an intersection or on the
road) for the second set of analyses.
df
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
B
S.E.
Wald
1 -0.02
1 0.01
1 0.07
1 0.54
1 0.58
1 0.76
1 0.05
0.43
0.04
0.12
0.43
0.39
0.37
0.36
0.00
0.06
0.35
1.57
2.26
4.26*
0.02
1 -0.09
1 0.01
1 0.06
1 0.01
1 0.91
1 0.76
1 0.08
0.44
0.04
0.12
0.00
0.37
0.37
0.36
0.04
0.01
0.27
2.28
6.17*
4.30*
0.05
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Table C.9: General Linear Model MARKET Sign Distance Error for the first set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
0.01
0.02
0.13
0.01
0.12
0.07
1
1
1
1
1
2
0.01
0.02
0.13
0.01
0.12
0.04
0.48
0.90
5.57*
0.45
4.84*
1.54
0.01
0.02
0.13
0.01
0.19
0.08
1
1
1
1
1
2
0.01
0.02
0.13
0.01
0.19
0.04
0.56
0.78
5.47*
0.52
8.09**
1.64
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
152
Table C.10: General Linear Model MARKET Sign Distance Error for the second set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
0.01
0.02
0.13
0.01
0.12
0.05
0.03
1
1
1
1
1
1
1
0.01
0.02
0.13
0.01
0.12
0.05
0.03
0.55
0.89
5.57*
0.45
4.85*
2.25
1.07
0.01
0.02
0.13
0.01
0.19
0.05
0.03
1
1
1
1
1
1
1
0.01
0.02
0.13
0.01
0.19
0.05
0.03
0.62
0.78
5.47*
0.50
8.07**
2.29
1.21
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Table C.11: General Linear Model on Route Turn Error for the first set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
0.02
78.39
34.95
8.93
58.14
37.53
1
1
1
1
1
2
0.09
65.05
32.75
15.29
109.17
39.33
1
1
1
1
1
2
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
153
0.02
78.39
34.95
8.93
58.14
18.77
F
0.00
8.90**
3.97*
1.01
6.60*
2.13
0.09 0.01
65.05 7.42**
32.75 3.74ms
15.29 1.75
109.17 12.46**
19.67 2.24
Table C.12: General Linear Model on Route Turn Error for the second set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
0.08
79.45
35.04
9.13
57.76
21.28
13.33
1
1
1
1
1
1
1
0.02
66.11
32.92
15.03
108.79
20.72
15.02
1
1
1
1
1
1
1
0.08
79.45
35.04
9.13
57.76
21.28
13.33
F
0.01
8.99**
3.97*
1.03
6.54*
2.41
1.51
0.02 0.00
66.11 7.52**
32.92 3.74ms
15.03 1.71
108.79 12.37**
20.72 2.36
15.02 1.71
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Table C.13: General Linear Model on Route Segment Error for the first set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
0.09
114.82
113.85
6.32
246.92
155.89
1
1
1
1
1
2
0.09
114.82
113.85
6.32
246.92
77.95
0.69
96.15
108.39
15.42
358.32
158.12
1
1
1
1
1
2
0.69 0.02
96.15 3.04ms
108.39 3.42ms
15.42 0.49
358.32 11.31**
79.06 2.50ms
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
154
0.00
3.62ms
3.59ms
0.20
7.78**
2.46ms
Table C.14: General Linear Model on Route Segment Error for the second set of analyses.
Type III
Squares
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
F
0.21
113.42
113.68
6.13
247.83
69.34
88.49
1
1
1
1
1
1
1
0.21
113.42
113.68
6.13
247.83
69.34
88.49
0.98
94.44
108.00
15.72
359.13
67.17
92.09
1
1
1
1
1
1
1
0.98 0.03
94.44 2.98ms
108.00 3.40ms
15.72 0.50
359.13 11.32**
67.17 2.12
92.09 2.90ms
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
155
0.01
3.57ms
3.58ms
0.19
7.80**
2.18
2.78ms
APPENDIX D
THE SUCCESS SCORES FOR VARIOUS TASKS
156
Appendix D.1 The Measures of Success Scores
Direction estimation task
Direction success: if the direction error lay within 20o from the actual angle it
was scored as “success.” Otherwise, it was scored as “failure.”
Navigation task
Success based on speed: If the participant completed the task over the
average speed of the sample it was scored as “success.” Otherwise, it was scored as
“failure.”
Success based on distance error: If the participant walked extra mile to
complete the test it was scored as “failure.” Otherwise, it was scored as “success.”
Success based on turn error: If the participant made an inefficient turn, it was
scored as “failure.” Otherwise it was scored as “success.”
Success based on the backtracking error: If the participant retracked a path it
was scored as “failure.” Otherwise it was scored as “success.”
An overall navigation success: Based on the above success scores, I calculated
an overall navigation success. If the participant walked over average speed, made no
distance and turn error, it was scored as “success37.”
37
Backtracking success score was dropped, because of the low variation.
157
Sketching task
Success based on map selection: Respondents received four maps from which
they were to choose the correct map. If the participant chose the correct map it was
scored as “success.” If not, it was scored as “failure.”
Success based on locating the MARKET sign exactly: If the participant drew
the MARKET sign at the correct location precisely, it was scored as “success.”
Otherwise it was scored as “failure.”
Success based on drawing the sequence of route turns: If the participants
drawings of turn sequence (eg. First left then up) is the same as sequence they
followed in the navigation test, it was scored as “success.” Otherwise it was scored as
“failure.”
Success based on drawing the route segments: This measure assess the
success based on exact accuracy. A participant’s drawing of route may be correct in
terms of sequence of turns, yet their drawing may not be correct if the turns were on
the wrong segments. If participant’s drawing of the road segments between START
and MARKET sign was exactly the same as the path they followed in the wayfinding
test, it was scored as “success.” Otherwise, it was scored as “failure.”
An overall sketching success: Based on the above success scores, I calculated
an overall sketching success. If the participant chose the correct map, locate the
MARKET sign exactly, and drew the route turns and segments correctly, it was
scored as “success.” Otherwise it was scored as “failure.”
158
Overall spatial awareness score based on direction estimation, navigation
and sketching tests
Using the responses to three tests, I calculated a composite measure, an
overall spatial awareness measure. The overall success score was the sum of the
success scores of direction estimation, navigation and sketching tests. If a person
completed all three tasks successfully it was scored as “3.” If a person did not
complete any task successfully it was scored as “0.”
159
Appendix D.2 The Analyses of Overall Spatial Awareness Success
Table D.1: In simple environments more participants were successful in more tasks than complex
ones.
Source
Plan Layout
Complex (n = 80)
Simple (n = 80)
Successful
in no task
45%
29%
Overall Success
Successful Successful
in one task
in two
tasks
31%
28%
Successful
in three
tasks
23%
30%
1%
14%
X2 (3, N = 160) = 12.24, p< .01
Table D.2: As differentiation increased from Low to Moderate to High, the percentage of respondents
successfully completed more tasks increased.
Source
Physical differentiation
Low - No
(n = 40)
Moderate – vertical or horizontal
(n = 80)
High – vertical and horizontal
(n = 40)
Successful
in no task
Overall Success
in one task
in two
tasks
Successful
in three
tasks
68%
20%
13%
0%
33%
29%
29%
10%
15%
40%
35%
10%
Too few participants succeeded in all three tasks to allow a statistical comparison
Table D.3: In environments with vertical differentiation more participants were successful in more
tasks than those without it.
Source
(Landmark Presence)
No Differentiation (n = 80)
With Differentiation (n = 80)
Object Landmark (n = 40)
Building Landmark (n = 40)
Successful
in no task
50%
24%
23%
25%
Overall Success
in one task
in two
tasks
28%
31%
38%
25%
23%
30%
28%
33%
No Vertical Differentiation versus With Differentiation: X2 (3, N = 160) = 20.52, p< .01
160
Successful
in three
tasks
0%
15%
13%
18%
Table D.4: In environments with horizontal differentiation more participants were successful in more
tasks than those in environments without it.
Source
(Road Hierarchy Presence)
With Differentiation (n = 80)
Road Width (n = 40)
Road Pavement (n = 40)
Successful
in no task
Overall Success
Successful
Successful
in one task in two tasks
50%
24%
25%
23%
21%
38%
43%
33%
19%
34%
30%
38%
Successful
in three
tasks
10%
5%
3%
8%
No Horizontal Differentiation versus With Differentiation X2 (3, N = 160) = 15.83, p< .01
Table D.5: Males showed a higher success rate than females.
Source
Gender
Female (n = 65)
Male (n = 95)
Successful
in no task
58%
22%
Overall Success
in one task
in two
tasks
31%
28%
11%
37%
Successful
in three
tasks
0%
13%
X2 (3, N = 160) =32.11, p< .01
Table D.6: More frequent game players showed higher success rate than less frequent game players.
Source
Game playing frequency
Never 1 (n = 23)
2 (n = 33)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
Successful
in no task
52%
52%
41%
53%
19%
7%
24%
Overall Success
in one task
in two
tasks
35%
21%
22%
27%
42%
29%
29%
13%
21%
22%
13%
32%
57%
35%
Too few “less frequent players” succeeded in all three tasks to allow statistical comparison.
161
Successful
in three
tasks
0%
6%
15%
7%
6%
7%
12%
Appendix D.3 The Analyses of Direction Success
Table D.7: In simple environments more participants were successful than in complex ones.
Source
Plan Layout
Complex (n = 80)
Simple (n = 80)
Successful Percentage
41%
59%
X2 (1, N = 160) = 4.9, p< .05
Table D.8: As differentiation increased from Low to Moderate to High, the percentage of successful
respondents increased.
Source
Low - No (n = 40)
Moderate – vertical or horizontal (n = 80)
High - vertical and horizontal (n = 40)
23%
56%
65%
X2 (2, N = 160) = 16.95, p< .01
Table D.9: In environments with vertical differentiation more participants were successful than in
environments without it.
Source
Vertical Differentiation (Landmark Presence)
With Differentiation, one of two kind (n = 80)
Object Landmark(n = 40)
38%
63%
70%
55%
No Vertical Differentiation versus With Differentiation: X2 (1, N = 160) = 10.00, p< .01
Table D.10: In environments with horizontal differentiation more participants were successful than in
environments without it and road pavement variation produced better success rates.
Source
Horizontal Differentiation (Road Hierarchy
Presence)
Road Width Variation (n = 40)
Road Pavement Variation (n = 40)
41%
59%
48%
70%
No Horizontal Differentiation versus With Differentiation: X2 (1, N = 160) = 4.9, p< .05
Road width variation versus Road pavement variation: X2 (1, N = 80) = 4.2, p< .05
162
Source
Gender
Female (n = 65)
Male (n = 95)
32%
62%
X2 (1, N = 160) =13.7, p< .01
Table D.12: More frequent game players showed higher success rates than less frequent game players.
Source
Never 1 (n = 23)
2 (n = 33)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
43%
45%
44%
33%
55%
71%
65%
Table D.13: Binary Logistic Regression on direction success for the first set of analyses.
Source (n = 160)
df
Gender
Age (year born)
Game Playing
Plan layout
Moderate versus High
Low versus High
Gender
Age (year born)
Game Playing
Plan layout
Low versus High
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
1
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
163
Direction Success Scores
B
SE WALD
-1.02
0.01
-0.03
0.33
-0.72
0.43
0.05
0.11
0.44
0.38
-0.26
-1.52
0.42
0.54
-1.00
0.01
-0.03
0.00
-0.91
0.44
0.05
0.11
0.00
0.37
-0.27
-1.56
0.42
0.54
5.55*
0.08
0.08
0.57
3.51ms
9.26**
0.38
7.97**
5.11*
0.06
0.09
0.52
6.04*
9.74**
0.41
8.44**
Table D.14: Binary Logistic Regression on direction success for the second set of analyses.
Source (n = 160)
df
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
164
Direction Success Scores
B
SE WALD
1
1
1
1
1
1
1
-1.04
0.02
-0.03
0.33
-0.71
-0.84
-0.63
0.43
0.05
0.11
0.43
0.38
0.36
0.36
5.85*
0.12
0.06
0.59
3.45ms
5.48*
3.16ms
1
1
1
1
1
1
1
-1.02
0.01
-0.03
0.00
-0.90
-0.85
-0.66
0.44
0.05
0.11
0.00
0.37
0.36
0.35
5.53*
0.10
0.06
0.46
6.01*
5.62*
3.46ms
Appendix D.4 The Analyses of Overall Navigation Success
Table D.15: In Simple environments more participants were successful than in complex ones.
Source (n = 160)
Plan Layout
Complex (n = 80)
Simple (n = 80)
33.8%
47.5%
Source
Low - No (n = 40)
High - vertical and horizontal (n = 40)
22.5%
42.5%
55.0%
X2 (2, N = 160) = 8.99, p< .05
Source (n = 160)
Object Landmark (n = 40)
Successful percentage
30.0%
51.3%
42.5%
60.0%
X2 (1, N = 160) = 7.49, p< .05
Source (n = 160)
Horizontal Differentiation (Road Hierarchy Presence)
165
35.0%
46.3%
45.0%
47.5%
Source
Gender
Female (n = 65)
Male (n = 95)
18.5%
55.8%
X2 (1, N = 160) =22.29, p< .01
Source
Never 1 (n = 23)
2 (n = 33)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
13.0%
24.2%
44.4%
33.3%
58.1%
64.3%
58.8%
X2 (6, N = 160) =20.91, p< .01
Table D.21: Binary Logistic Regression on overall navigation success for the first set of analyses.
Overall Navigation Success
B
SE WALD
df
Source (n = 160)
Gender
Age (year born)
Game Playing
Plan layout
Low versus High
Gender
Age (year born)
Game Playing
Plan layout
Low versus High
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
166
1
1
1
1
1
2
1
1
-1.29
0.10
0.08
1.74
-0.18
0.48
0.06
0.12
0.53
0.43
-0.35
-0.62
0.45
0.57
1
1
1
1
1
2
1
1
-1.14
0.08
0.08
0.02
-1.21
0.49
0.06
0.12
0.01
0.42
-0.38
-0.76
0.45
0.58
7.16**
2.73ms
0.49
10.79**
0.17
1.23
0.60
1.16
5.42*
1.59
0.46
9.68**
8.23**
1.77
0.71
1.72
Table D.22: Binary Logistic Regression on overall navigation success for the second set of analyses.
Source (n = 160)
df
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
167
Overall Navigation Success
B
SE WALD
1
1
1
1
1
1
1
-1.26
0.10
0.08
1.75
-0.18
-0.49
-0.14
0.48
0.06
0.12
0.53
0.43
0.40
0.39
6.84**
2.76ms
0.47
10.82**
0.18
1.56
0.13
1
1
1
1
1
1
1
-1.13
0.08
0.08
0.02
-1.22
-0.51
-0.25
0.49
0.06
0.12
0.01
0.42
0.39
0.39
5.35*
1.62
0.45
9.60**
8.23**
1.69
0.41
Appendix D.5. The Analyses of Overall Sketching Success
Table D.23: In Simple environments more participants were successful than in complex ones.
Source
Plan Layout
Complex (n = 80)
Simple (n = 80)
5.0%
22.5%
X2 (1, N = 160) = 10.32, p< .01
Source
Low - No (n = 40)
High – vertical and horizontal (n = 40)
0.0%
17.5%
20.0%
X2 (2, N = 160) = 8.64, p< .05
Source
Object Landmark(n = 40)
5.0%
22.5%
17.5%
27.5%
X2 (1, N = 160) = 10.32, p< .01
Source
Horizontal Differentiation (Road Hierarchy Presence)
168
12.5%
15.0%
17.5%
12.5%
Source
Gender
Female (n = 65)
Male (n = 95)
1.5%
22.1%
X2 (1, N = 160) =13.77, p< .01
Source
Never 1 (n = 23)
2 (n = 33)
3 (n = 27)
4 (n = 15)
5 (n = 31)
6 (n = 14)
4.3%
12.1%
22.2%
6.7%
12.9%
28.6%
11.8%
Table D.29: Binary Logistic Regression on overall sketching success for the first set of analyses.
Source (n = 160)
df
Gender
Age (year born)
Game Playing
Plan layout
Low versus High
Gender
Age (year born)
Game Playing
Plan layout
Low versus High
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
1
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
169
Sketching Success Scores
B
SE WALD
-2.80
-0.02
-0.06
-0.15
-1.82
1.12
0.08
0.16
0.70
0.67
-0.16
-19.19
0.55
5777.38
-2.46
-0.05
-0.09
0.01
-1.96
1.13
0.08
0.16
0.01
0.67
-0.15
-19.08
0.55
5815.18
6.20*
0.06
0.15
0.05
7.36**
0.08
0.08
0.00
4.76*
0.35
0.30
0.65
8.53**
0.08
0.08
0.00
Table D.30: Binary Logistic Regression on overall sketching success for the second set of analyses.
Source (n = 160)
df
Gender
Age (year born)
Game Playing
Plan Layout
Gender
Age (year born)
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
170
Sketching Success Scores
B
SE WALD
1
1
1
1
1
1
1
-2.99
-0.01
-0.12
0.03
-1.89
-1.59
-0.11
1.12
0.07
0.16
0.70
0.67
0.65
0.55
7.10**
0.04
0.54
0.00
7.83**
6.08*
0.04
1
1
1
1
1
1
1
-2.79
-0.03
-0.14
0.01
-2.08
-1.55
-0.10
1.13
0.07
0.16
0.01
0.67
0.64
0.55
6.13*
0.23
0.78
0.69
9.72**
5.76*
0.04
Appendix D.6. The Statistical Analyses on Specific Measures (Success Scores) of
Navigation Task
Table D.31: Binary Logistic Regression on success based on speed for the first set of analyses.
Gender
Age
Game Playing
Plan Layout
Low versus High
Gender
Age
Game Playing
Familiarity (Exploration Speed)
Plan Layout
Low versus High
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
171
df
B
SE WALD
1
1
1
1
1
2
1
1
1.69
0.02
0.29
2.30
-1.00
0.47
0.05
0.14
0.58
0.48
0.08
0.24
0.51
0.60
1
1
1
1
1
2
1
1
1.49
-0.01
0.27
0.03
0.47
0.49
0.05
0.14
0.01
0.46
0.24
0.40
0.53
0.62
13.19**
0.11
4.46*
15.69**
4.33*
0.16
0.02
0.16
9.48**
0.07
3.77ms
21.21**
1.07
0.44
0.21
0.42
Table D.32: Binary Logistic Regression on success based on speed for the second set of analyses.
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
df
B
SE WALD
1
1
1
1
1
1
1
1.69
0.02
0.28
2.29
-1.00
0.08
0.15
0.47
0.05
0.14
0.58
0.48
0.43
0.42
13.09**
0.11
4.40*
15.64**
4.33*
0.03
0.13
1
1
1
1
1
1
1
1.53
-0.01
0.27
0.03
0.47
0.04
0.36
0.49
0.05
0.14
0.01
0.46
0.45
0.44
9.79**
0.07
3.76ms
21.27**
1.03
0.01
0.68
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Table D.33: Binary Logistic Regression on success based on turn error for the first set of analyses.
Gender
Age
Game Playing
Plan Layout
Low versus High
Gender
Age
Game Playing
Plan Layout
Low versus High
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
172
df
B
SE WALD
1
1
1
1
1
2
1
1
0.51
0.12
-0.05
-0.11
1.01
0.45
0.05
0.12
0.44
0.41
0.49
0.80
0.44
0.55
1
1
1
1
1
2
1
1
0.57
0.13
-0.04
0.00
0.91
0.46
0.05
0.12
0.00
0.38
0.50
0.83
0.44
0.54
1.27
5.59*
0.15
0.06
6.15*
2.27
1.23
2.15
1.54
5.96*
0.10
0.60
5.77*
2.47
1.30
2.36
Table D.34: Binary Logistic Regression on success based on turn error for the second set of analyses.
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
df
B
SE WALD
1
1
1
1
1
1
1
0.51
0.12
-0.05
-0.11
1.01
0.40
0.41
0.45
0.05
0.12
0.44
0.41
0.38
0.37
1.31
5.59*
0.15
0.06
6.14*
1.14
1.20
1
1
1
1
1
1
1
0.58
0.13
-0.04
0.00
0.91
0.43
0.41
0.46
0.05
0.12
0.00
0.38
0.38
0.37
1.59
5.97*
0.10
0.60
5.75*
1.28
1.25
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
Table D.35: Binary Logistic Regression on success based on distance error for the first set of analyses.
Gender
Age
Game Playing
Plan Layout
Low versus High
Gender
Age
Game Playing
Plan Layout
Low versus High
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
173
df
B
SE WALD
1
1
1
1
1
2
1
1
0.52
0.16
-0.04
0.05
1.36
0.45
0.05
0.12
0.45
0.42
0.59
0.70
0.45
0.54
1
1
1
1
1
2
1
1
0.55
0.16
-0.03
0.00
1.36
0.46
0.06
0.12
0.00
0.39
0.60
0.73
0.45
0.54
1.30
8.27**
0.09
0.01
10.58**
2.14
1.70
1.68
1.40
8.24**
0.06
0.04
12.11**
2.31
1.79
1.85
Table D.36: Binary Logistic Regression on success based on distance error for the second set of
analyses.
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
174
df
B
SE WALD
1
1
1
1
1
1
1
0.52
0.16
-0.03
0.04
1.35
0.50
0.22
0.45
0.05
0.12
0.44
0.42
0.38
0.37
1.33
8.34**
0.08
0.01
10.56**
1.71
0.35
1
1
1
1
1
1
1
0.55
0.16
-0.03
0.00
1.35
0.52
0.23
0.46
0.06
0.12
0.00
0.39
0.38
0.37
1.45
8.34**
0.06
0.06
12.03**
1.85
0.39
Appendix D.7. The Statistical Analyses on Specific Measures (Success Scores) of
Sketching Task
Table D.37: Binary Logistic Regression on success based on map selection for the first set of analyses.
Gender
Age
Game Playing
Plan Layout
Low versus High
Gender
Age
Game Playing
Plan Layout
Low versus High
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
175
df
B
SE WALD
1
1
1
1
1
2
1
1
0.86
-0.03
0.18
0.35
0.25
0.43
0.04
0.11
0.43
0.39
0.29
0.16
0.46
0.53
1
1
1
1
1
2
1
1
0.90
-0.02
0.19
0.00
0.41
0.44
0.04
0.11
0.00
0.36
0.34
0.22
0.46
0.53
4.01*
0.35
2.74ms
0.67
0.41
0.43
0.41
0.09
4.19*
0.23
2.95ms
0.05
1.28
0.55
0.55
0.17
Table D.38: Binary Logistic Regression on success based on map selection for the second set of
analyses.
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
176
df
B
SE WALD
1
1
1
1
1
1
1
0.85
-0.02
0.18
0.37
0.25
0.34
-0.20
0.43
0.04
0.11
0.43
0.39
0.36
0.36
3.86
0.30
2.72
0.74
0.43
0.88
0.32
1
1
1
1
1
1
1
0.90
-0.02
0.19
0.00
0.42
0.37
-0.17
0.44
0.04
0.11
0.00
0.36
0.36
0.35
4.13
0.18
2.96
0.04
1.36
1.04
0.22
*
ms
*
ms
Table D.39: Binary Logistic Regression on success based on locating MARKET sign exactly for the
first set of analyses.
Gender
Age
Game Playing
Plan Layout
Low versus High
Gender
Age
Game Playing
Plan Layout
Low versus High
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
177
df
B
SE WALD
1
1
1
1
1
2
1
1
-0.25
0.01
0.05
0.51
1.11
0.44
0.05
0.11
0.44
0.38
0.86
1.36
0.49
0.55
1
1
1
1
1
2
1
1
-0.37
0.00
0.05
0.01
1.48
0.46
0.05
0.11
0.00
0.39
0.92
1.42
0.49
0.56
0.32
0.05
0.23
1.39
8.26**
6.08*
3.11ms
6.07*
0.65
0.00
0.16
2.60
14.67**
6.51*
3.43ms
6.50*
Table D.40: Binary Logistic Regression on success based on locating MARKET sign exactly for the
second set of analyses.
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
178
df
B
SE WALD
1
1
1
1
1
1
1
-0.22
0.01
0.05
0.51
1.11
0.67
0.65
0.44
0.05
0.11
0.43
0.39
0.37
0.36
0.26
0.06
0.23
1.35
8.23**
3.29ms
3.30ms
1
1
1
1
1
1
1
-0.33
0.00
0.05
0.01
1.47
0.67
0.70
0.45
0.05
0.11
0.00
0.39
0.37
0.36
0.53
0.00
0.17
2.50
14.59**
3.26ms
3.75ms
Table D.41: Binary Logistic Regression on success based on drawing the sequence of route turns for
the first set of analyses.
Gender
Age
Game Playing
Plan Layout
Low versus High
Gender
Age
Game Playing
Plan Layout
Low versus High
df
B
1
1
1
1
1
2
1
1
0.51
0.10
0.04
0.33
0.94
0.44
0.05
0.12
0.43
0.39
0.82
1.02
0.44
0.53
0.47
0.10
0.04
0.00
1.14
0.44
0.05
0.12
0.00
0.38
0.83
1.05
0.44
0.52
1
1
1
1
1
2
1
1
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
179
SE WALD
1.35
4.18*
0.14
0.59
5.64*
4.59
3.46ms
3.78ms
1.15
3.65ms
0.11
0.77
9.12**
4.82ms
3.59ms
3.99*
Table D.42: Binary Logistic Regression on success based on drawing the sequence of route turns for
the second set of analyses.
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
180
df
B
SE WALD
1
1
1
1
1
1
1
0.53
0.10
0.05
0.33
0.93
0.50
0.54
0.43
0.05
0.12
0.43
0.39
0.37
0.36
1.49
4.15*
0.16
0.58
5.57*
1.84
2.26
1
1
1
1
1
1
1
0.50
0.10
0.04
0.00
1.12
0.50
0.56
0.44
0.05
0.12
0.00
0.37
0.37
0.36
1.30
3.64ms
0.13
0.75
9.01**
1.85
2.45
Table D.43: Binary Logistic Regression on success based on drawing the route segments for the first
set of analyses.
Gender
Age
Game Playing
Plan Layout
Low versus High
Gender
Age
Game Playing
Plan Layout
Low versus High
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
181
df
B
SE WALD
1
1
1
1
1
2
1
1
0.38
0.02
-0.04
0.05
1.86
0.47
0.05
0.12
0.45
0.43
0.39
1.20
0.50
0.57
1
1
1
1
1
2
1
1
0.32
0.01
-0.05
0.00
1.94
0.48
0.05
0.12
0.00
0.42
0.40
1.20
0.51
0.57
0.65
0.09
0.13
0.01
18.38**
5.12ms
0.61
4.46*
0.44
0.02
0.16
0.23
21.33**
5.11ms
0.64
4.47*
Table D.44: Binary Logistic Regression on success based on drawing the route segments for the
second set of analyses.
Gender
Age
Game Playing
Plan Layout
Gender
Age
Game Playing
Plan Layout
* p < 0.05, ** p < 0.01 and ms 0.05<p<0.10
182
df
B
SE WALD
1
1
1
1
1
1
1
0.33
0.01
-0.04
0.07
1.85
0.68
0.57
0.46
0.05
0.12
0.46
0.43
0.39
0.38
0.51
0.07
0.13
0.02
18.42**
3.00ms
2.21
1
1
1
1
1
1
1
0.27
0.01
-0.05
0.00
1.93
0.67
0.58
0.47
0.05
0.12
0.00
0.42
0.39
0.38
0.33
0.02
0.17
0.26
21.28**
2.93ms
2.28

- The Ohio State University

Transcription

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