Environment and Planning B: Planning and Design 2005, volume 32, pages 777 ^ 785
DOI:10.1068/b31191
Influence of physical characteristics of routes on distance
cognition in virtual environments
Ebru Cubukcu
Department of City and Regional Planning, Dokuz Eylul University, Izmir, 35160, Turkey
Jack L Nasarô
City and Regional Planning, 231 Knowlton Hall, 275 W. Woodruff Avenue, The Ohio State
University, Columbus, OH 43210, USA; e-mail: [email protected]
Received 5 April 2004; in revised form 5 February 2005
Abstract. Discrepanices between perceived and actual distance may affect people's spatial behavior.
In a previous study Nasar, using self report of behavior, found that segmentation (measured through
the number of buildings) along the route affected choice of parking garage and path from the parking
garage to a destination. We recreated that same environment in a three-dimensional virtual environment and conducted a test to see whether the same factors emerged under these more controlled
conditions and to see whether spatial behavior in the virtual environment accurately reflected
behavior in the real environment. The results confirmed similar patterns of response in the virtual
and real environments. This supports the use of virtual reality as a tool for predicting behavior in the
real world and confirms increases in segmentation as related to increases in perceived distance.
Introduction
Research shows differences between actual distances and subjective estimates of
distance (Jansen-Osman and Berendt, 2002; Moore, 1979; Nasar, 1983; Sadalla and
Magel, 1980). Such distortions might affect spatial behavior. Characteristics of both
the individual and the route distort distance estimates and behavior (Canter and
Tagg, 1975; Cohen and Weatherford, 1980; Hanyu and Itsukushima, 1995; Lee, 1970;
Montello, 1997; Nasar, 1983; Nasar et al, 1985; Perüch et al, 1989; Sadalla and Magel,
1980; Sadalla and Staplin, 1980; Thorndyke, 1981). Among physical characteristics,
segmentation (features, such as turns and intersections, perceived as dividing a route),
or `information along the route' (the number of perceived features, such as buildings),
and the visibility of the destination have emerged as important variables affecting
subjective estimates of distance (Montello, 1997; Nasar, 1983; Nasar et al, 1985; Sadalla
and Staplin, 1980). As segmentation or amount of information along a route increase
and as a destination remains hidden, perceived distance tends to increase. Visibility of
destination may also relate to scaling (in which people estimate distances for a longer
segment as shorter than the sum of shorter segments that sum to the longer segment)
or indirectness of route (in which people estimate the straight-line distances between
two places as longer than routes with detours) (Montello, 1997).
One study used reports of naturalistic behavior in relation to two parking garages
and office destinations from the garages (Nasar, 1983). When the office was equidistant
from each garage and the respondent reported parking at one garage to minimize
the distance to the office, the route taken had less segmentation (fewer buildings)
than the shortest alternative route to the other garage. When the route taken to the
office was longer than the shortest alternative route, respondents chose the route which
put the destination in view earlier than did the alternative shortest route. (Though the
study did ask respondents why they chose the parking garage they did, and limited
ô Author to whom correspondence should be addressed.
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E Cubukcu, J L Nasar
the sample to those who said they parked at the parking garage closest to their
destination, it did not ask why they chose the route by which they walked to their office.)
This study replicated the Nasar (1983) study in a computer-simulated setting,
avirtual environment (VE). VE refers to computer-generated three-dimensional spatial
environments which can be visualized in real time. Some studies found that the
spatial information gained in VE transfers to real environments (Bliss et al, 1997;
Cromby et al, 1996; Wilson et al, 1996; 1997; Witmer et al, 1996). The study in the VE
expanded the Nasar (1983) study in four ways. First, it broadened the sample from a
small number of university professors to a more diverse sample of adults. Second,
rather than measuring route choice, it measured distance perception directly by asking
participants to tell which of two routes felt shorter. Montello (1991) criticized the use
of route choice as a measure of perceived distance in relation to visual cues, because
it includes alternative bases for distance judgments, such as cost or effort. Third, we
revised the calculation of visibility of destination. Both studies used a ratio to the full
walking distance. Nasar's (1983) study compared full walking distance with the length
of the street on which the destination came into sight, but this measure has a flaw.
Depending on the angle of approach and the physical features along the route, a
destination may not become visible when a person enters the destination street (other
features may hide it), or it may become visible before a person enters the destination
street (if, for example, a person takes a shortcut and sees the destination at an angle).
Thus, this study identified the first point at which the destination became visible along
the route and used the ratio of the distance between that point and the destination
to the full walking distance. Finally, as an additional measure of segmentation, we
added a measure of the number of turns along the route. Studies have found that
increases in the number of turns along a route increase the perceived distance both
in real and in the virtual environments (Jansen-Osman and Berendt, 2002; Sadalla and
Magel, 1980).
In the Nasar (1983) study and this VE replication of it, various physical factors of
the environment may have varied in a confounded way. For example, one key factorö
destination visiblity örelated to other factors ösuch as actual distance or number of
turnsöwhich may have influenced individuals' choices. Thus, the naturalistic study did
not establish causality. VE would allow one to unconfound these factors, but, as a first
step, in this study we sought first to see whether the VE produced a similar pattern of
results to those found in the natural conditions.
Methods
Description of the simulation
We simulated the setting of the Nasar (1983) study with a three-dimensional computermodeling program, GTK Radiant. A real-time three-dimensional environment-generator game engine, called QUAKE III ARENA, produced perspective views to simulate
ground-level walking-paced movement through the simulation. Users can control their
movement with a keyboard: left ^ right arrows provided left and right rotations; and
up ^ down arrows provided forward and backward translations. Research has found
that people can easily master this keyboard control of motion (Belingard and Perüch,
2000; Jansen-Osman and Berendt, 2002; Tlauka and Wilson, 1994; 1996; Wilson et al,
1996; 1997).
The site comprised a rectangular area on The Ohio State University (OSU) Campus.
It has about 237 acres including 7742 ft of paths and roads, combined, and 64 freestanding buildings. Real-world textures of the actual buildings were added to make the
simulation look realistic (figure 1); and ratings of the perceived realism of the simulation
indicated that participants experienced it as realistic. On a scale from 1 ˆ very realistic
Distance cognition in virtual environments
779
Figure 1. The simulated environments.
to 7 ˆ not realistic at all, they rated it as 2.17 [standard deviation (SD) ˆ 1.25]. Only 6%
of the respondents judged it as somewhat unrealistic, unrealistic, or not realistic at all.
The area had one `origin' at Brown Hall and two destinations, `destination A' at the
Northwest Parking Ramp and `destination B' at Arps Hall Parking Garage. It had
three alternative routes between the origin and destination A (figure 2), and four
alternative routes between the origin and destination B (figure 3, over).
Two judges (naive to the hypotheses) measured the physical features along the
routes (table 1, over). They calculated the walking distance on each route. They counted
the number of road and path intersections, the number of turns (both right-angle turns
and all types of turns), the number of visible free-standing buildings, and the number
of environmental changes along each route. They measured visibility of each destination as the ratio of the distance from the point where the judges agreed that the
destination first became visible to full walking distance.
Destination A
Origin
Figure 2. The routes between origin and destination A.
780
E Cubukcu, J L Nasar
Destination B
Origin
Figure 3. The routes between origin and destination B.
Table 1. Physical environmental characteristics across routes.
Route
Actual walking distance (ft)
Number of road intersections
Number of path intersections
Number of right-angle turns
Number of other turns
Number of free-standing buildings
Number of environmental changes
Visibility (%)
Participants
Route to destination A
Route to destination B
A1
A2
A3
B1
B2
B3
B4
1353
4
22
1
1
20
2
51
1353
5
18
3
3
22
0
29
1633
5
18
3
3
21
5
34
1277
3
21
3
3
20
6
30
1141
2
17
2
4
18
3
51
1277
3
2
1
1
17
1
23
1175
2
16
0
2
17
2
30
By e-mail, we invited the employees of two governmental agencies, The Ohio National
Guard and The Ohio Supercomputer Center (20 miles and 1 mile, respectively, from the
study area and parking garages), to participate in the study. The e-mail explained
the procedure, how long the study would take, and indicated that all answers would be
anonymous and confidential. Fifty-nine volunteers participated in the study. Interviews
took place at the agencies (outside the study area), so none of the participants parked
at either parking garage and walked to the destination. When asked about how well
they knew the OSU campus (on a seven-point scale, where 7 ˆ not at all) most
respondents (59.6% reported scores of 5 or higher, indicating that they felt they did
not know it at all, very well, or well. Thus, for the most part, their knowledge and
learning of the routes would have come from the VE. We dropped eight participants
from the sample because they did not find at least one destination from the origin in
the test phase; and we dropped an additional four participants who judged each
destination as equidistant from the origin. This left us with a sample of 47 participants
Distance cognition in virtual environments
781
(28 males, 19 females).(1) Their age ranged from 19 to 64 years, with an average of 41.0
(11.3). Three subjects chose not to report their age.
Procedure
The experiment had two phases: a learning and a testing phase. In the learning phase,
all participants started at the origin and freely explored the simulated environment for
up to five minutes to familiarize themselves with it. If participants saw the two
destinations and reported that they felt ready for the test before five minutes was up,
we gave the test. If after five minutes the participants did not see the two destinations or
reported that they did not feel ready for the test, we gave them an 90 additional seconds
to explore. To go into the test phase, the participant had to see the two destinations in
the learning phase. We recorded the exploration path and the destination they explored
first. Their judgment of closest destination did not differ in relation to the destination
they explored first [w 2 ˆ 0:84, degrees of freedom (df) ˆ 1, p > 0:05].
In the testing phase we asked participants to be as efficient as possible and find the
shortest route from the origin (Brown Hall) to each destination (parking garage). (This
reverses the field study, which examined routes taken from each parking garage to
various building destinations, including Brown Hall.) We did not impose a time limit
on the task. Participants were free to begin with either destination and the final
judgment of the closest destination did not differ by which destination they explored
first (w 2 ˆ 0:55, df ˆ 1, p > 0:05). After showing both routes, they picked the destination that felt closer to the origin. Then, we had them report their gender, and age and
judge the realism of the simulated environment. To control the effect of testing we
also asked them to rate their familiarity with the real setting and frequency of computer-game playing.(2) The participants who picked destinations A and B had similar
familiarity ratings (t ˆ 0:67, df ˆ 45, p > 0:05). The participants did not receive a
map of the environment before or during the experiment. The experimental session
lasted from eight to twelve minutes per participant.
Results
Overall, participants had a choice of three possible routes to destination A and four
possible routes to destination B. Figure 4 (over) shows the percentage of respondents who picked each route, and table 1 shows the walking distance for each route.
Participants tended to pick the shortest route, but their choices also reflected some
distortions. For destination A, most people picked route A1, which is roughly equidistant from the origin to route A2, both of which are shorter than route A3. The
fewest people picked route A3. These differences achieved statistical significant differences with bonferoni adjustments for multiple comparisons (A1 versus A2, w 2 ˆ 6:42,
df ˆ 1, p < 0:05; A1 versus A3, w 2 ˆ 25:40, df ˆ 1, p < 0:00; A2 versus A3,
w 2 ˆ 9:00, df ˆ 1, p < 0:05). For destination B, the highest percentage picked the
shortest route, B2, but at a statistically significant level in relation to only route B1
(w 2 ˆ 12:80, df ˆ 1, p < 0:00). The fewest people picked route B1, which differed
significantly from the shorter route B2 (w 2 ˆ 12:80, df ˆ 1, p < 0:00) and route B4
(1)
Analyses on the larger sample (N ˆ 51) which included those judging each destination as
equidistant, produced the same pattern of results found for the sample tested.
(2) Participants who picked destination A as closer had similar familiarity ratings (mean ˆ 4:9,
SD ˆ 2:1) to participants who picked destination B as closer (mean ˆ 4:4, SD ˆ 2:3), where
1 ˆ know the setting very well and 7 ˆ do not know the setting at all. For game-playing frequency
(1 ˆ all the time, to 7 ˆ not at all), participants who picked destination A as closer had similar
game-playing ratings (mean ˆ 4:9, SD ˆ 2:0) to participants who picked destination B as closer
(mean ˆ 4:4, SD ˆ 2:0).
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E Cubukcu, J L Nasar
70
40
66
38
60
Percentage
40
20
10
10
(a)
28
20
30
30
0
30
30
50
4
4
A1
A2
Shortest route
A3
0
(b)
B1
B2
B3
Shortest route
B4
Figure 4. Route taken from the origin to (a) destination A (n ˆ 47) and (b) destination B
(n ˆ 47).
(w 2 ˆ 8:07, df ˆ 1, p < 0:00). Routes B1 and B3 had the same distance from the
origin, but significantly more people picked route B3 (w 2 ˆ 9:00, df ˆ 1, p < 0:00);
and whereas route B3 is 102 ft (or about 8%) shorter than route B4, one more person
picked it than route B4. Although this did not achieve statistical significance
( p ˆ 0:85), it suggests that something about the two routes led people to misjudge
their relative distances.
Respondents also tended to gauge accurately which destination was closer. The
distance measures in table 1 show that from the origin, the shortest route to destination
B was closer than the shortest route to destination A by 212 ft; and the longest route to
destination B was 76 ft shorter than the shortest route to destination A. When asked
which destination felt closer, more participants (66%) correctly picked destination B as
closer than destination A (w 2 ˆ 4:79, df ˆ 1, p < 0:05).
Nasar (1983) examined three routes from the origin: the route taken (RT) to either
destination (judged as closest), the shortest route to the other destination (ROD), and
the shortest alternative route to the selected destination (ARSD). He compared all the
environmental factors along RT and ROD. He found that, when either the walkable or
crow-flies distance had no significant difference between the origin and either destination, RT had significantly fewer building sections along it than did ROD. No significant
effects emerged for the number of walk intersections, road intersections, combined
walk ^ road intersections, environmental changes, and visibility.
We followed the same analysis, but omitted crow-flies distance because having one
origin left no variation to that variable. We added two additional variables: number of
right-angle turns and number of turns of all types. Consistent with Nasar (1983) we
found no significant difference of actual distance between RT and ROD. Table 2 shows
the environmental features of each route. As expected, RT had less segmentation
(fewer building sections, fewer road intersections, and fewer right-angle turns) than
did ROD. A paired-sample t-test between RT and ROD revealed statistically significant differences for the number of building sections (t ˆ 3:23, df ˆ 46, p < 0:01), the
number of road intersections (t ˆ 2:68, df ˆ 46, p < 0:01), and number of right-angle
turns (t ˆ ÿ4:64, df ˆ 46, p < 0:01).
Nasar (1983) also compared visibility of the destination along RT and ARSD. He
found that RT puts the destination in view more rapidly than ARSD. For this analysis
he selected the instances where the lengths of RT and ARSD were equal. Considering
our small sample size and the setup of the experiment, we used a different analysis to
compare visibility along route taken and alternative routes. Recall that we asked each
Distance cognition in virtual environments
783
Table 2. Environmental characteristics between route taken and shortest alternative route to
other destination.
Variable
Actual distance
Environmental changes
Pedestrian intersections
Road intersections*
Building sections**
Right-angle turn**
Turns: all angles
RTa
RODb
mean
standard deviation
mean
standard deviation
1249
2.1
18.9
3.2
18.6
1.4
2.3
107
1.2
2.6
0.8
1.6
0.9
1.3
1281
1.7
19.0
3.7
20.0
2.0
2.7
101
1.0
1.4
0.5
1.4
0.0
1.0
* p < 0:05, ** p < 0:01.
a RT: the route taken from origin to either destination because it is perceived to be closest to
the destination.
b ROD: the shortest route from the origin to the other possible destination.
participant to find the shortest route from the origin to each destination before picking
the closest destination. For destination A, the lengths of route A1 and route A2 were
equal, and route A1 puts the destination in view quicker than route A2. For destination
B the lengths of route B1 and route B3 were equal and route B1 puts the destination in
view quicker than route B3. Thus, we compared the frequency of participants who
selected routes A1 and A2 when finding destination A, and those who selected routes
B1 and B3 when finding destination B. As expected, significantly more participants
selected route A1 (31) than route A2 (14) (w 2 ˆ 6:42, df ˆ 1, p < 0:05). Contrary to
expectations, significantly more participants selected route B3 (14) than route B1 (2)
(w 2 ˆ 9:00, df ˆ 1, p < 0:01). This unexpected result for destination B may relate to
the relatively small difference in visibility between routes B1 and B3 compared with the
high visibility difference between routes A1 and A2 (see table 1).
Discussion
The findings provided evidence that selected interurban spatial decisions depend both
on actual and on subjective estimates of distance; and that the subjective estimates
arise in part from characteristics of the route traversed. Respondents often picked the
shortest route to the destination, and they tended to gauge accurately which of two
destinations was closer. When two routes were equal and participants chose one more
often than the other, or when they chose a longer route over a shorter one, three
structural characteristics of the route related to the choice: the number of building
sections, road intersections, and right-angle turns. This agreed with Nasar's (1983)
findings for reported behavior in the real environment and it extends the findings to
a broader and more diverse population of adults and for route choice in the reverse
direction from Nasar (1983). It also supports the `information storage' and `segmentation' model; respondents judged routes with fewer noticeable features, building sections,
road intersections, or right-angle turns as shorter than equidistant or shorter routes
with more such features.
For visibility, the results partially supported Nasar's (1983) findings. Nasar (1983)
found that, in the choice between two routes, people tended to choose the one which
put the destination in view first. This study confirmed this result for instances where
visibility of two routes differed significantly (such as routes A1 and A2 between the
origin and destination A). For the instances where the visibility of two routes differed
slightly (such as routes B1 and B3 between the origin and destination B), visibility had
784
E Cubukcu, J L Nasar
the reverse effect on distance estimates, perhaps because of confounding with other
physical factors. Future research should test a wider range of visibility and the effect
of all physical environmental factors simultaneously. It should use VE or other
approaches to test the factors unconfounded from one another.
The results agree with findings that spatial information experienced through virtual
environments transfers to real environments (Bliss et al, 1997; Cromby et al, 1996b;
Wilson et al, 1996). In this case, for physical elements that affect perception of distance
and route choice, the findings in the VE echoed those in the real environment.
Although VE in comparison with navigation through real places has a restricted field
of view, viewing scale, possible room effects, a lack of proprioceptive feedback and
motor (physical) effort, research suggests that these effects have a relatively small
magnitude compared with the structure of the environment (Colle and Reid, 2000;
Waller et al, 2004). That said, VE may have stronger validity in some contexts than
in others.
We cannot assert with certainty that the learning of the area came wholly from the
VE experience, because some respondents reported familiarity with the area. However,
as most respondents came from more than a mile away, did not park in either parking
garage, walk the routes, and reported low familiarity with it, the learning for most of
our respondents probably did come primarily from the VE experience. Nevertheless,
subsequent work might conduct the VE test with respondents who have never experienced the area. It might also test other cultures or socioeconomic groups.
This study, like many before it, examines an environment with a relatively short
route or travel time. We do not know how the findings for segmentation and visibility
of destination would apply for significantly longer trips. Beyond that, research on
different kinds of trips, modes of travel, through different kinds of environments, and
to different kinds of locations can broaden our understanding of the ways in which
physical characteristics of the route may distort distance perception and route choice.
The repeated findings for the number of features or segmentation (compare Montello,
1997), however, suggest that it does produce systematic variation in perceived distance
and spatial behavior. Information about such systematic distortions in distance perception can be used in models of spatial behavior and in situating facilities to attract use.
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