Regular Paper

Lost again on the way? :Measuring Human
Map-Reading Ability
Kaori KOBAYASHI♥ Ryong LEE♦
Kazutoshi SUMIYA♠
Recently, various navigation systems have found a placein many aspects of our daily life. However, it is difficult forpeople to effectively use such a system because the ability
to understand the maps generated by and the guidance
given by the system differs from person to person. We
think that the fact that the system gives the same
guidance to everyone is a problem for some people.
Therefore, it is necessary for us to investigate a person’s
ability to understand maps and the related guidance, thatis, the human map-reading ability. Moreover, we consider
that it is important that a navigation system analyzes the
map-reading ability of its user and highlights the user’s
weaknesses in this aspect. In this paper, we havedeveloped a system to simulate the way people find aparticular place in real space. First, we performed outdoor
experiments for studying how pedestrians find their way
to a certain place by using the proposed system. Next, we
proposed three major indicators of pedestrian behavior
and finally measured the human map-reading ability
using the proposed system. Consequently, we think that
our approach will open up a new vista for personal
guidance services.

1. Introduction
Recently, many online map services and navigational
systems have become pervasive as pedestrians or drivers
are easily guided by these smart systems. However, there
are still some people who are struggling with routefinding in spite of using navigation devices. We often
complain about the routes that the navigational systems
present on the increasingly popular positioning systems.
However, before criticizing the limitations of the systems,
we need to consider the human ability to recognize thereal world and to find routes. That is, a primitive question
arises: why do people get so lost and how does the ability
to find the correct route differ among people?
♥

Student Member. School of Human Science andEnvironment, University of Hyogo.
nc06s078@stshse.u-hyogo.ac.jp♦
Regular Member. School of Human Science andEnvironment, University of Hyogo.
leeryong@shse.u-hyogo.ac.jp♠
Regular Member. School of Human Science andEnvironment, University of Hyogo.
sumiya@shse.u-hyogo.ac.jp

DBSJ Journal, Vol.8, No.4
March 2010
The understanding of human beings’ various capabilities
has been applied as a universal theme in various fields
from psychology to user-interface engineering. In thispaper, we are challenged to reason the human
map-reading ability in a new fashion with a methodology
currently available. In this paper, we refer to the term
“map-reading ability” as a rather generalized ability,
including the capability to find ways well using maps. In
general, we believe that the map-reading ability differsfrom person to person. While the complete understandingof such a capability requires many interdisciplinary
studies, we can easily imagine that someone who has lived
in a suburban area with small buildings for her lifetime
would have a tendency to lose her way in a big city thathas sophisticated structures [9, 10]. It can be understoodthat her geo-space recognition has not been developed tofind ways for such sophisticated places as the canyon ofcity buildings [3, 4]. Ellard [1, 2] wrote in his book that it
is important that we better understand how the humanmind works, rather than look for technological solutions toall of our problems. He insists that there needs to be agreater connection to psychology for architects and urban
planners to better design our physical spaces forfacilitating human way finding. We think that his opinion
is important. We consider that not only the architect but
also the developer of a navigation system should consider
the human map-reading ability. Then, the navigation
system will be able to provide the most suitable guidance
for a user. Therefore, we attempt to measure the human
map-reading ability. To achieve our purpose of providing asimplified map to users, we think that the measurementof each person’s map-reading ability is very essential.
However,
unlikeother
information
searches,
recommendations [7], or user adaptive systems [6, 8], such
an ability cannot be easily represented even by the people
themselves. That is, while a person, who lacks a sense of
direction, can know that she often finds herself struggling
to find the right way to a place, it is difficult to explain
what the problem is in detail. Therefore, we think that it
is important for a navigation system to allow for thedifferences in the human map-reading ability and provide
support in both a real space and a virtual space. This
invisible and ambiguous capability should be measured in
an objective and a quantifiable way. Therefore, ourhypothesis is that we can model a user’s ability forreading and understanding maps by analyzing the actionof the user on the spot on the basis of a course andmeasure this ability with a computer-based simulation.
This approach can estimate users’ capability with aminimal effort and cost by emulating real-world walking
or driving conditions. We repeated the experiment, exceptthat subjects walked a real-world route, and we compared
the results to those of the computer-based simulation.
The rest of this paper is organized as follows. Section 2
describes our approach to estimating the human
map-reading ability in a systematic method. Section 3
explains the preliminary experiment. Section 4
demonstrates the designed measurements. Section 5shows the simulation results of the developed system.
Section 6 concludes the paper and discusses future work.

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2. Map-Reading Ability and Simulation
System

2.1 System for Measuring Human Map-Reading
Ability
The term map-reading ability refers to the ability ofpeople to read and understand a map. This ability differsfrom person to person. Therefore, it is inappropriate thatonline map services and navigation systems show thesame map for all users. For instance, in the case of a user
who cannot find landmarks, these systems have to show a
map in which landmarks are emphasized. In the case of auser who is weak at interpreting directions at crossings,
the system should show a route that avoids crossings. In
order to program navigation systems to do these things,
we consider that we need to measure the human
map-reading ability. Moreover, it is important that thesesystems themselves measure the human map-reading
ability. Therefore, we have developed a way-finding
simulation system for measuring the human map-reading
ability.

measurements. We can analyze the behaviors of a user byusing the user’s operation history. First, we analyzed the
behavior of the user in a real space to make it possible tomeasure his/her map-reading ability more precisely. We
will discuss the experiment next.

3. Preliminary Experiment
In order to find out the major reasons why people
become confused when walking on an unfamiliar route,we performed an outdoor route finding experiment. Before
the experiment, we conducted a survey to know eachparticipant’s route finding ability with a well-organizedquestionnaire. In this section, we will describe thequestionnaire survey and the experiment and show thestrong relationship between the two tests. As participantsin the experiments, six students who were randomly
selected from our department joined the following tests
voluntarily.
Table 1: Details of Real-Navi Setting
Investigation day
Map
Object route#Crossing#Corner
Participant
Route information

Online Map

Street-View

History of User Operation

Figure 1: Way-Finding Simulation System

2.2 Way-Finding Simulation System bBased on
Street View
In order to simulate the outdoor navigation and evaluate
the user’s map-reading ability, we have developed asimulation system with two types of maps, as shown in
Figure 1. In the interface, the left side shows a 2D map [5],whereas the right side shows a 3D street view of a Google
map. From both these maps, a user faces a situation
similar to that in the experiment that we performed in an
outdoor field; that is, the left map corresponds to a paper
map given to the user, and the right view gives the user
the simulated real-world scenes. The user can operate theleft map in a manner similar to browsing a paper map. On
the other hand, the right view can simulate the user
walking virtually and show the real scenes. In addition, toconfirm the surroundings of a position, the user can see
other sides by panning or tilting the view left/right or
upward/downward. Of course, this simulation systemcannot perfectly simulate a real-world situation. The
human behaviors possible in outdoor environments are
explicitly different from the ones possible in the system.
However, this system provides a practical solution tomeasure such a sophisticated ability with less effort andtime. By observing the user’s navigation of a route givenin this system, we can measure the user’s map-reading
ability as a numerical value and clarify, in detail, thereasons for a lack of sense of direction through the2009,10,1
Paper map
R1(Route 1): 1, Shinzaike-honcyo, Himeji,
Hyogo～Yashiro-tokojicyo, Himeji, Hyogo
R2(Route 2): Yashiro-tokojicyo, Himeji, Hyogo～Nishiyashirocyo, Himeji, Hyogo
R1: 15，R2: 11
R1: 7，R2: 8
University student 6(Man 3，Woman 3)(Age 21～23)
R1: Total distance： 780m，Travel time： 9min
R2: Total distance： 600m，Travel time： 7min

3.1 Questionnaire
Before performing the experiment, each participant wastested for his/her geo-spatial sensing ability with awell-organized questionnaire, as shown in Table 2, which
was devised by Takeuchi [11, 12] to judge a person’s senseof direction. We let them answer the questionnaire,
because we wanted to recognize the subjective knowledgeof each participant. The questionnaire was composed of 20
questions. Each question had to be given a score from 1 to
5; therefore, the total score was in the range of 20 to 100.
However, this result does not necessarily accurately
estimate the ability of each participant.

3.2 Observation of Way-Finders’ Behaviors
In this preliminary experiment, we performed an actualroute finding experiment in outdoor fields to build a set ofmeasurements to represent the participants’ map-reading
ability. For the outdoor experiment called Real-Navi, we
prepared two routes, as shown in Figure 2. The first one(Figure 2(a)) was a residential district; therefore, there
were very few landmarks on this route. Furthermore, atthe spot indicated by the red circle in Figure 2(a), thename of the shop was changed. The other path is shown in
Figure 2(b). At the spot indicated by the red circle in
Figure 2(b), there is a very small road, such as a backstreet, and it is very difficult for pedestrians to find thisroad. The two paths were unfamiliar to all theparticipants, and their visual representations were notgiven in advance. The other experimental settings are
given in Table 1. We observed seven factors—“Total Time,”“Missing Route,” “Right and Left Confirmation,” “Map
Confirmation,” “Stop,” “Return,” and “Rotating a Map.”
We express the “total time” in minutes and the otherfactors with numbers.

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Table 2: Result of Questionnaire Survey
Sense of Direction Questionnaire-Short Form (SDQ-S) A B C D E F
1 I lose the directions north, south, east, and west if I go
1 1 1 2 5 3
to a new place.
※2 I do not make mistakes in identifying directions, even if
1 1 1 4 5 4I am in an unkown place.
3 I can understand an indication of a direction, if it is
2 2 2 4 5 4
specified by "Left or Right" but not if it is specified by"North, South, East or West".
4 It is difficult to understand if the train is traveling5 2 1 4 5 5north, south, east or west.
5 I do not have confidence in the direction I am walking
1 2 3 3 3 4
if I do not know the place and I become uneasy.
Idonotunderstandwhich
direction
the
room
faces
6
1 2 1 1 4 3
when in a hotel room.
7 It is considerably difficult to go to a new place, even if
1 3 4 5 5 4I examined a map beforehand.
※8 I can find the position where I am located on a map
3 2 3 4 4 2
immediately.
※9 I can easily and vividly remember the image of the map 5 1 2 4 5 2
in my head.
10
3 3 5 5 3 4I cannot memorize a mark at landmarks.
11

3

2

5

5

5

4

12 Even if a place is visited many times, I cannot
remenber landmark in the area.
13 I cannot distinguish and remember differences inscenery.
14 When I drive to a destination with a lot of turns, I often
forget the way I went to get there.
15
I often forget where I previously turned.

5

3

5

5

5

3

3

2

5

4

4

4

2

1

2

4

5

3

2

2

2

4

5

4

16

3

3

5

4

4

4

I cannot easily find object to be landmarks.

I do not confirm landmarks at turning points.

17 Although I was told the way to go, I could not follow
4 3 4 5 5 3
the way correctly.
18 In a residential area, if there was a row of similer
1 2 2 4 3 3houses, I could not figure out the destination.
※19 I can immediately distinguish between roads of similar
1 2 3 4 3 2
appearance.
20 Whenever I walk with other people toward a2 1 3 5 3 4destination, I do not worry about whether the route isTotal
49 40 59 80 86 69
Direction score (1-9)
20 16 18 31 41 31
Space score (10-16)
21 16 29 31 31 26
Score: Yes(=1)～No(=5), but for question marked with (※) score: No(=1)～Yes(=5)

3announced to the monitors whether he/she could reach thedestination as a confirmation.
We paid attention to the relation of a participant’s
behavior to the difference in the questionnaire score. We
have tabulated the behavior of the participants in Tables
3 and 4. Then, the behaviors from the questionnaire
survey were considered, and a correlation was obtainedwith respect to the three factors of “Missing Route,”“Right and Left Confirmation,” and “Map Confirmation.”
Therefore, we think that these three factors are importantand focus only on them.
We could determine significant differences between thenovices and the experts in our experiment. As a result ofTables 3 and 4, we could distinguish the participants into
two groups: the novices and the experts. In the novice
group, the number of these factors was definitely higher
compared to the other group. However, the second factor“Missing Route” was also important, while the number of
its occurrences was not helpful in separating theparticipants into two groups. In the case of the secondroute, all the participants made a mistake at the fifth
cross point. At this location, most participants wentstraight toward the west; although they had to turn left at
the point, they soon became aware of their mistake, cameback to point 5, and were able to find the correct route.
Table 3: User Behavior Patterns in Route 1
A

B

C

D

E

F

Questionnaire Score

49

40

59

80

86

69

Total Time（minutes）

10

13

15

7

7

11

#Missing Route

0

0

1

0

0

0

#Right and Left Confirmation

55

58

32

26

21

49

#Map Confirmation

80

91

88

39

32

89

#Stop

3

0

2

2

2

2

#Return

0

0

1

0

0

0

#Rotating a Map

0

6

7

5

0

0

5
4
2

6
6
Start

3

1

4

End

7

7

5

End

1
2

(a) Route 1 in Real-NaviStart(b)Route 2 in Real-Navi

Figure 2: Routes Designated for Outdoor Experiment
In the field experiment, we performed the test according
to the following procedure:
Experimental Procedure in Real-Navi:
Step 1. Each participant was positioned at the starting
point and given only a paper map without any otherguidance. There were two monitoring people. One was acamera man who recorded all the scenes with a video
camera along the way. Another logger registered all the
behaviors of the participant and noted his/hercharacteristic activities. The monitors did not give any
guidance or intermit the navigation, but only observed the
behavior of the participants during the short trip until theparticipants reached the destination.
Step 2. Each participant and two monitors walked thepath toward the destination. At every crossing point, thelogger registered the details of all the behaviors: for
instance, what the participant gazed at intently, which
way he/she faced, or whether he/she raised the map to
re-confirm the way. In addition, if the participant lost
his/her way, data such as how far he/she is from the rightpath was registered.
Step 3. Finally, near the destination, each participant

Table 4: User Behavior Patterns in Route 2
A

B

C

D

E

F

Questionnaire Score

49

40

59

80

86

69

Total Time（minutes）

8

11

8

10

8

9

#Missing Route

0

0

1

0

0

0

#Right and Left Confirmation

47

49

22

45

20

39

#Map Confirmation

84

87

60

50

21

77

#Stop

2

1

2

3

2

3

#Return

1

1

0

1

1

1

#Rotating a Map

0

7

8

8

0

0

However, participant C did not come back to point 5;
instead, participant C followed another route but could
still reach the destination. In other words, theparticipant’s traversal routes were a little different fromthe given route. In addition, the expert group (D and E)
did not confirm the direction or review the map asfrequently as compared to the novice group (A, B, C, andF) on an average. For instance, the expert group (D and E)
confirmed only an important point that a store name waschanged; further, only when there was a main landmark,they referred to the maps well. Because they limited theirreferences to a specific point on the map to confirm
whether to go to the right or the left, they seemed tounderstand the information that was necessary forreaching their destination. On the other hand, because
the novice group (A, B, C, and F) referred to the map andconfirmed the right and left directions many times, they

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4. Design of Measurement
In the preliminary experiments, we found themissing-way patterns as follows:
Frequent referring to a map: People who lacked asense of geo-spatial region often needed to confirm thecontent of the map.
Re-confirming the direction at crossing points: People
with a good sense of direction quickly determined
their direction on the way.
Recovering the mistake and moving to the right path:
People who lacked a sense of direction could notrecover their misses by going back to the previous
points on the way.
The three factors that we observed in the real-world
experiment were the same most occurring ones as thoseobserved in our simulation in the case of users who could
not reach their given destinations. In particular, theseerrors described how we could help users in finding their
way on the basis of their shortcomings. Furthermore, eachpattern could be measured in our system by previewing
the route search, where each person reviewed thesearched route as we did in our simulation. That is, byoperating in the 2D map and 3D street views, we could
speculate each person’s ability in selecting the right way
at a crossing point, maintaining their destination
direction, and judging the most important regions to befocused upon. To measure these specific abilities, wedeveloped the following measurable indicators:• SoM (Sense of Space Memorizing): How many times does
each participant need to refer to a map?
• SoD (Sense of Direction): How precise and quickly does
the participant detect the direction to follow?
• SoP (Sense of Positioning): If the participant made a
mistake, could he/she recover from the problem soon?
At the current stage, we deal with each measurement
equally since one measurement does not seem to be morecritical than another. The methodologies to measure eachindicator are depicted in Figure 3. First, for judging where
to focus on a map, a user generally needs to check
important regions such as the intersections and thedetailed area around the destination itself so as to not
miss the final stage. For this, every corner along a routeand the destination area were previously determined.
Like the expression of SoM, the ratio of the actuallyviewed regions focused on to the prepared list wasrepresented as a normalized value. Second, the maximum
angle of route direction and user’s eye direction were alsoacquired and normalized by calculating the SoD formula.
Here, route_vectori is the ith line-segment vector, andeye_vectori is the widest vector at the ith position. Whenthe angle is large, we believe that the user’s attention is at
a low level; the user did not gaze at a specific landmark or
toward the goal, and instead looked at other less related
objects. In addition, the user, while looking away from theroute, could easily lose the direction toward thedestination or correct paths. Therefore, it could bevalidated that the sense of direction would be weak when
the user’s eye angle is larger than the threshold. Lastly,by using the expression of SoP, we could obtain andnormalize the differential area. These indicators havebeen measured with the following formulae:
SoM =

SoD =

count (user_selected ∩ candidate)1•
count (candidate)(1+ # reconfirm)route _ vectori × eye _ vectoriroute _ vectori eye _ vectori

SoP =

1
1 + ∫ user_path − ∫ route•

length ( route )length (user_path )Finally, each result, given as a normalized value between
0.0 and 1.0, was summed up and divided by 3.0 in order toobtain the total map-reading ability.
Map-Reading Ability = (SoM + SoD + SoP)/3.0
6th max vector

SoM

SoD

SoP
5th max vector

3rd max vector

4th max vector

2nd max vector

cross points
max expansion area

route vector

route of systemeye vector

route of user’s operation

1st max vector

Figure 3: Indicators of Map-Reading Ability
With these measures, we reexamined the results of
Real-Navi, scored them, and compared the results withthose of the first questionnaire survey. As a result, wefound that these two results had a strong relation, as
shown in Figure 4. Therefore, we can say that ourmeasurements can reflect the human map-reading ability
with a considerable accuracy.
Normalized Scores in Questionnaire

could not decide their own landmark.

DBSJ Journal, Vol.8, No.4
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1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

C
A

F

D E
y = 1.7294x - 0.2372

B

0.4

0.5
0.6
Normalized Scores in Real-Navi Exp.

0.7

Figure 4: Correlation of Questionnaire Survey andReal-Navi

5. Measuring in Simulated Way-Finding
In this section, we describe a simulation system toevaluate the human map-reading ability on the basis of
the measurements designed from the preliminary
experiments. With this system, a user can navigate agiven route and know his/her self-ability with anestimated value.

5.1 Experiment
For the computer-based simulation experiment called
Virtual-Navi, we prepared two routes, as shown on the left
map of Figure 5. The first one was very simple and easy.
However, there are few landmarks on the route.
Furthermore, in the spot indicated by the red circle in
Figure 5(a), the street view had a malfunction. In this spot,
the screen only showed the wall of a house. Therefore, the日本データベース学会論文誌 Vol.8, No.4
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DBSJ Journal, Vol.8, No.4
March 20102009,10,5
R3(Route 3): Nitto elementary school～A
Object routekindergarten attached to the Nihonbashi elementary
R4(Route 4): Nishiumeda～Higobashi#CrossingR3: 11，R4: 19#Corner
R3: 5，R4: 4
University student 6
Participant(Man 3，Woman 3)(Age 21～23)
Visual studio 2008 C#
System information 2 screen (Left： Online Map，Right： Street-View)
Record history（operation name，cordinate，angle）
Map operation
Moving, Centering, Zooming in, Zooming out
Street-View operation Moving, Centering, Zooming in, Zooming out, Pan, Tilt
Route information R3: Total distance： 600m，Travel time： 8min
R4: Total distance： 2km，Travel time： 30min

0.8
0.7

Normalized Scores in Virtual-Navi Exp.

Investigation day

the field. Therefore, it can be said that our measurements
are well organized and appropriate to estimate the human
map-reading ability with high precision.

E

0.6

D

0.5
0.4

B

0.3

C
F

0.2

y = 1.9861x - 0.6055

A

0.1
0
0.4

0.5
0.6
Normalized Scores in Real-Navi Exp.

0.7

Figure 6: Correlation of Virtual-Navi and Real Navi
1.2
SoM Score of Virtual-Navi

participants lost their sense of direction at this point. The
other path, shown in Figure 5(b), had very similar scenery
for a long portion of the route. In addition, it was difficult
for the participants to look around the surroundings
because there were high buildings. Of course, these twopaths were unfamiliar to all the participants, and theirvisual representations were not given to the participantsin advance. The other experimental settings are listed inTable 5.
Table 5: Detail of Virtual-Navi Setting

D

1

E

0.8
0.6
0.4

C

F

0.2

A

B

0
0

0.2
0.4
0.6
SoM Score of Real-Navi

0.8(b) Route 2 in Virtual-Navi

Figure 5: Routes Designated for Computer- bBased
Simulation Experiment
Experimental Procedure in Virtual-Navi
Step 1. We allowed the participants to practice for thestreet-view operation. The participants had not previously
used the street-view display. By allowing them to practice,
we reduced the gap between the simulation andreal-world navigation.
Step 2. We gave a route on the map and let theparticipants operate street view to go toward thedestination. We fixed a camera so that the screen of thesystem entered and recorded all operations.
Step 3. Finally, near the destination, the participantsannounced to the monitors whether they could reach thedestination as a confirmation. After the experiment, we
also performed a verbal investigation to ask about where
the most confusing point was, etc.

5.2 Experimental Result
Participants D and E had high scores, and the otherparticipants had low scores. This resembles thequestionnaire survey results, except in the case of F who
had a low Virtual-Navi score yet a high questionnairescore. The correlation of the questionnaire survey andReal-Navi is low in the case of participant F, as shown
Figure 4.
However, we can see a high correlation between
Real-Navi and the computer-based simulation by using
our measurements (Figure 6). In other words, we canmeasure the map-reading ability of a person by acomputer-based simulation even if we do not go out into

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

E
D

F
C

A
B

0

0.2

0.4

0.6

0.8

1

SoD Score of Real-Navi(b) Sense of Direction
1.2
SoP Score of Virtual-Navi(a) Route 1 in Virtual-Navi

SoD Score of Virtual-Navi(a) Sense of Space Memorizing

E

1
0.8

D

0.6

C

B

0.4

F

0.2

A

0
0.8

0.85

0.9

0.95

1

1.05

SoP Score of Real-Navi(c) Sense of Positioning
Figure 7: Correlation of Virtual-Navi and Real Navi
(Three Measures)
Furthermore, we expressed a correlation according toeach indicator of the map-reading ability, as shown in
Figures 7(a)–(c). With respect to SoM, the correlation of
Real-Navi and Virtual-Navi is very strong (Figure 7(a)).
The novice group (A, B, C, and F) tend to always expand
the online maps, such as SoM in SoD, and it is thoughtthat SoM lacks as for them for these factors (Figure 7(b)).
In addition, the correlation is very strong with respect to
SoD. However, it follows that participant F resembled
participant D, although their groups were different. We

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understood that the level of their SoD was about the same.
Therefore we found that it cannot be said that all ability
was high even if map-reading ability was high. It wasproved by this result. On the other hand, the result of SoP
was not very good (Figure 7(c)). From this, we understoodthat a review and the improvement of the indicator werenecessary.

6. Conclusion
In this paper, we developed a system to estimate thehuman map-reading ability on the basis of themeasurement of three measures. In order to design themeasurements and validate their correctness, we
conducted a questionnaire survey and outdoor
experiments and found significant reasons why theparticipants lost their ways. We modeled such reasons
with measurements for testing the participants’ sense of
space memory, sense of direction, and sense of positioning.
Although these factors were often thought to be the major
reason of losing one’s way, we could find the reasons fromour experiments and formalize them as measurable
indicators. Based on these measures, we computed thecorrelation between the questionnaire survey and theoutdoor experiment and showed a strong relation tovalidate our measurements. We also performed anotherexperiment to estimate the same ability in our systemwith the same measurements. As a result, we found thatthe simulation could estimate the human map-reading
ability almost similarly to the outdoor experiments orquestionnaires. This means that with a little effort fromusers, the system can reveal their map-reading ability
with high precision. Furthermore, the proposed system isconsiderably better than the questionnaire survey toanalyze, in detail, the participant’s ability to find his/herway. Therefore, we think that we can develop apersonalized navigation system. For instance, navigation
system can show route guide based on tendency of user byhistory of user’s behavior. In a future work, we will
investigate other measures that we could not clarify inthis experiment and increase the emulating degree of thesimulation in our system. In particular, with this type ofability measuring system, we expect that current
navigation systems can understand the map-reading
ability of their users and provide considerably better
adaptive services depending on each user’s ability.

[Acknowledgment]This research was supported in part by a Grant-in-Aid for
Scientific Research (B)(2) 20300039 from the Ministry of
Education, Culture, Sports, Science and Technology of Japan.

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Kaori KOBAYASHI
She is a student of School of Human Science andEnvironment, University of Hyogo. Her research interests
include geographic information system. She is a student
member of Information Processing Society of Japan andthe Database Society of Japan.

Ryong LEE

He is an Associate Professor at School of Human Scienceand Environment, University of Hyogo. His researchinterests include geographic information systems, social
network analysis and web information retrieval systems.
He is a member of ACM and Institute of Electronics,
Information and Communication Engineers. He received aPh.D. and a M.S. from Graduate School of Social
Informatics, Kyoto University in 2003 and worked as asenior researcher at Samsung Advanced Institute ofTechnology until June, 2008.

Kazutoshi SUMIYA
He is a Professor at School of Human Science andEnvironment, University of Hyogo. He received the Ph.D.
degree in engineering in 1998 from the Kobe University
Graduate School of Science and Technology. His researchinterests include multimedia database and databroadcasting. He is a member of IEEE Computer Society,
ACM, the Institute of Image Information and Television
Engineers, Information Processing Society of Japan andthe Database Society of Japan and the Institute ofElectronics, Information and Communication Engineers.

日本データベース学会論文誌 Vol.8, No.4
2010 年 3 月