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Journal of Computations & Modelling, vol.8, no.4, 2018, 29-63
ISSN: 1792-7625 (print), 1792-8850 (online)
Scienpress Ltd, 2018
Need identification and sensitivity analysis of consumers
using Bayesian Network:
A case of Fuji Shopping Street Town
Daisuke Suzuki1, Akane Okubo
2, Tsuyosi Aburai
3 and Kazuhiro Takeyasu
4
Abstract
Shopping streets at local city in Japan became old and are generally declining. In this paper,
we handle the area rebirth and/or regional revitalization of shopping street. We focus on
Fuji city in Japan. Four big festivals are held at Fuji city. Many people visit these festivals
including residents in that area. Therefore a questionnaire investigation to the residents and
visitors is conducted during these periods in order to clarify residents and visitors’ needs
for the shopping street, and utilize them to the plan building of the area rebirth and/or
regional revitalization of shopping street. There is a big difference between Fuji Shopping
Street and Yoshiwara Shopping Street. Therefore we focus Fuji Shopping Street in this
paper. These are analyzed by using Bayesian Network. Sensitivity analysis is also
conducted. As there are so many items, we focus on “The image of the surrounding area at
this shopping street” and pick up former half and make sensitivity analysis in this paper.
The analysis utilizing Bayesian Network enabled us to visualize the causal relationship
among items. Furthermore, sensitivity analysis brought us estimating and predicting the
prospective visitors. Sensitivity analysis is performed by back propagation method. These
are utilized for constructing a much more effective and useful plan building. We have
obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting
records would be the future works to be investigated.
1 Fujisan Area Management Company
2 NIHON University Junior College, Japan
3 Tokushima University, Japan
4 College of Business Administration, Tokoha University, Japan
Article Info: Received : May 5, 2018. Revised: June 7, 2018.
Published online: October 1, 2018.
30 Need identification and sensitivity analysis of consumers using Bayesian Network
Mathematics Subject Classification : 62F15
Key Words: Fuji City, Area rebirth, Regional vitalization, festival, Bayesian Network,
Back Propagation
1 Introduction
Shopping streets at local city in Japan are generally declining. It is because most of them
were built in the so-called “High Growth Period (1954-1973)”. Therefore they became
old and area rebirth and/or regional revitalization are required everywhere.
There are many papers published concerning area rebirth or regional revitalization.
Inoue (2017) has pointed out the importance of tourism promotion. Ingu et al.(2017)
developed the project of shutter art to Wakkanai Chuo shopping street in Hokkaido, Japan.
Ohkubo (2017) has made a questionnaire research at Jigenji shopping street in
Kagoshima Prefecture, Japan and analyzed the current condition and future issues. For
about tourism, many papers are presented from many aspects as follows.
Yoshida et al. designed and conducted a visitor survey on the spot, which used a
questionnaire to investigate the activities of visitors to the Ueno district in Taito ward,
Tokyo. Doi et al. analyzed the image of the Izu Peninsula as a tourist destination in their
2003 study “Questionnaire Survey on the Izu Peninsula.” Kano conducted tourist
behavior studies in Atami city in 2008, 2009, 2014 and in other years.
In this paper, we handle the area rebirth and/or regional revitalization of shopping street.
We focus on Fuji city in Japan. Fuji city is located in Shizuoka Prefecture. Mt. Fuji is
very famous all around the world and we can see its beautiful scenery from Fuji city,
which is at the foot of Mt. Fuji. There are two big shopping street in Fuji city. One is
Yoshiwara shopping street and another one is Fuji shopping street. They became old and
building area rebirth and regional revitalization plan have started. Following
investigation was conducted by the joint research group (Fuji Chamber of Commerce &
Industry, Fujisan Area Management Company, Katsumata Maruyama Architects,
Kougakuin University and Tokoha University). The main project activities are as follows.
A. Investigation on the assets which are not in active use
B. Questionnaire Investigation to Entrepreneur
C. Questionnaire Investigation to the residents and visitors
After that, area rebirth and regional revitalization plan were built.
In this paper, we handle above stated C.
Four big festivals are held at Fuji city. Two big festivals are held at Yoshiwara shopping
Daisuke Suzuki et al. 31
street and two big festivals at Fuji shopping street.
At Yoshiwara Shopping Street, Yoshiwara Gion Festival is carried out during June and
Yoshiwara Shukuba (post-town) Festival is held during October. On the other hand,
Kinoene Summer Festival is conducted during August and Kinoene Autumn Festival is
performed during October at Fuji Shopping Street. Many people visit these festivals
including residents in that area.
Therefore questionnaire investigation of C is conducted during these periods.
Finally, we have obtained 982 sheets (Yoshiwara district: 448, Fuji district: 534).
Basic statistical analysis and Bayesian Network analysis are executed based on that.
In this paper, a questionnaire investigation is executed in order to clarify residents and
visitors’ needs for the shopping street, and utilize them to the plan building of the area
rebirth and/or regional revitalization of shopping street. There is a big difference between
Fuji Shopping Street and Yoshiwara Shopping Street. Therefore we focus Fuji Shopping
Street in this paper. These are analyzed by using Bayesian Network. Sensitivity analysis
is also conducted. As there are so many items, we focus on “The image of the
surrounding area at this shopping street” and pick up former half and make sensitivity
analysis in this paper. By that model, the causal relationship is sequentially chained by
the characteristics of visitors, the purpose of visiting and the image of the surrounding
area at this shopping street. The analysis utilizing Bayesian Network enabled us to
visualize the causal relationship among items. Furthermore, sensitivity analysis brought
us estimating and predicting the prospective visitors. Sensitivity analysis was conducted
by back propagation method.
Some interesting and instructive results are obtained.
The rest of the paper is organized as follows. Outline of questionnaire investigation is
stated in section 2. In section 3, Bayesian Network analysis is executed which is followed
by the sensitivity analysis in section 4. Remarks is stated in section 5.
2 Outline and the Basic Statistical Results of the Questionnaire
Research
2.1 Outline of the Questionnaire Research
A questionnaire investigation to the residents and visitors is conducted during these
periods in order to clarify residents and visitors’ needs for the shopping street and utilize
them to the plan building of the area rebirth and/or regional revitalization of shopping
street. The outline of questionnaire research is as follows. Questionnaire sheet is
attached in Appendix 1.
32 Need identification and sensitivity analysis of consumers using Bayesian Network
(1) Scope of
investigation
: Residents and visitors who have visited four big
festivals at Fuji city in Shizuoka Prefecture, Japan
(2) Period : Yoshiwara Gion Festival: June 11,12/2016
Yoshiwara Shukuba (post-town) Festival: October
9/2016
Kinoene Summer Festival: August 6,7/2016
Kinoene Autumn Festival: October 15,16/2016
(3) Method : Local site, Dispatch sheet, Self writing
(4) Collection : Number of distribution 1400
Number of collection 982(collection rate 70.1%)
Valid answer 982
2.2 Basic Statistical Results
Now, we show the main summary results by single variable.
2.2.1 Characteristics of answers
(1) Sex (Q7)
Male 43.3%, Female 56.7%
These are exhibited in Figure 1.
Figure 1: Sex (Q7)
(2) Age (Q8)
10th
20.6%, 20th
16.7%, 30th
25.3%, 40th
17.0%, 50th
10.1%, 60th
6.9%, More than 70
3.4%
These are exhibited in Figure 2.
43.3%
56.7%
0
50
100
150
200
250
300
350
Male Female
Daisuke Suzuki et al. 33
Figure 2: Age (Q8)
(3) Residence (Q9)
a. Fuji city 82.8%, b. Fujinomiya city 8.8%, c. Numazu city 2.1%, d. Mishima city 0.7%,
e. Shizuoka city 0.9%, F. Else (in Shizuoka Prefecture) 2.1%, g. Outside of Shizuoka
Prefecture 2.6%
These are exhibited in Figure 3.
Figure 3: Residence (Q9)
(4) How often do you come to this shopping street? (Q1)
Everyday 21.2%, More than 1 time a week 17.2%, More than 1 time a month 22.7%,
More than 1 time a year 26.8%, First time 3.0%, Not filled in 4.1%
3.4%
6.9%
10.1%
17.0%
25.3%
16.7%
20.6%
0 20 40 60 80 100 120 140 160
Morethan
60th
50th
40th
30th
20th
10th
82.8%
8.8%
2.1% 0.7% 0.9% 2.1% 2.6%
0
50
100
150
200
250
300
350
400
450
500
Fujicity Fujinomiyacity
Numazucity
Mishimacity
Shizuokacity
Else(inShizuokaPref.)
OutsideofShizuokaPref.
34 Need identification and sensitivity analysis of consumers using Bayesian Network
These are exhibited in Figure 4.
Figure 4: How often do you come to this shopping street? (Q1)
(5) What is the purpose of visiting here? (Q2)
Shopping 17.2%, Eating and drinking 13.6%, Business 7.4%, Celebration, event 34.1%,
Leisure, amusement 6.1%, miscellaneous 21.6%
These are exhibited in Figure 5.
Figure 5: What is the purpose of visiting here? (Q2)
(6) How do you feel about the image of the surrounding area at this shopping street?
(Q3)
Beautiful 51.2%, Ugly 48.8%, Of the united feeling there is 44.3%, Scattered 55.7%,
Daisuke Suzuki et al. 35
Varied 38.5%, Featureless 61.5%, New 37.1%, Historic 62.9%, Full of nature 37.1%,
Urban 62.9%,
Cheerful 44.1%, Gloomy 55.9%, Individualistic 42.0%, Conventional 58.0%, Friendly
57.8%,
Unfriendly 42.2%, Healed 53.3%, Stimulated 46.7%, Open 44.8%, exclusive 55.2%,
Want to reside 43.6%,
Do not want to reside 56.4%, Warm 55.1%, Aloof 44.9%, Fascinating 42.1%, Not
fascinating 57.9%,
Want to play 47.1%, Want to examine deliberately 52.9%, Lively 36.8%, Calm 63.2%,
Atmosphere of urban 28.0%, Atmosphere of rural area 72.0%
These are exhibited in Figure 6.
Figure 6: How do you feel about the image of the surrounding area at this shopping street? (Q3)
72.0%
63.2%
52.9%
57.9%
44.9%
56.4%
55.2%
46.7%
42.2%
58.0%
55.9%
62.9%
67.6%
61.5%
55.7%
48.8%
28.0%
36.8%
47.1%
42.1%
55.1%
43.6%
44.8%
53.3%
57.8%
42.0%
44.1%
37.1%
32.4%
38.5%
44.3%
51.2%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
AtmosphereofurbanAtmosphereofruralarea
LivelyCalm
WanttoplayWanttoexaminedeliberately
Fascina ngNotfascina ng
WarmAloof
WanttoresideDonotwanttoreside
Openexclusive
HealedS mulated
FriendlyUnfriendly
Individualis cConven onal
CheerfulGloomy
FullofnatureUrban
NewHistoric
VariedFeatureless
OftheunitedfeelingthereisSca ered
Beau fulUgly
36 Need identification and sensitivity analysis of consumers using Bayesian Network
(7) There are many old buildings at the age of nearly 50 years. Do you think we can
still use them? (Q4)
. Can use it 48.7%, Cannot use it 29.2%, Have no idea 22.1%
These are exhibited in Figure 7.
Figure 7: There are many old buildings at the age of nearly 50 years. Do you think we can still
use them? (Q4)
3 Bayesian Network Analysis
In constructing Bayesian Network, it is required to check the causal relationship
among groups of items.
Based on this, a model is built as is shown in Figure 8.
Figure 8: A Built Model
Canuseit48.7%
Cannotuseit29.2%
Havenoidea22.1%
Daisuke Suzuki et al. 37
We used BAYONET software (http://www.msi.co.jp/BAYONET/). When plural nodes
exist in the same group, it occurs that causal relationship is hard to set a priori. In that
case, BAYONET system set the sequence automatically utilizing AIC standard. Node and
parameter of Figure 8 are exhibited in Table 1.
Table 1: Node and Parameter
Node Parameter
1 2 3 4 5 6 7 8 9 10
Gender Male Femal
e
Age 10th 20th 30th 40th
50th 60th Mor
e
than
70
The
purpose of
visiting
Shop
ping
Eating
and
drinkin
g
Busines
s
Cele
brati
on 、
even
t
Leis
ure,
amu
sem
ent
misc
ellan
eous
The image
of the
surroundin
g area at
this
shopping
street
Beaut
iful Ugly
Of the
united
feeling
there is
Scat
tere
d
Vari
ed
Feat
urele
ss
New
Hist
oric
Full
of
natu
re
Urba
n
Node Parameter
11 12 13 14 15 16 17 18 19 20
The image
of the
surroundin
g area at
this
shopping
street
Cheer
ful
Gloom
y
Individu
alistic
Con
vent
iona
l
Frie
ndly
Unfr
iendl
y Heal
ed
Stim
ulate
d
Ope
n
Excl
usiv
e
38 Need identification and sensitivity analysis of consumers using Bayesian Network
Node Parameter
21 22 23 24 25 26 27 28 29 30
The image
of the
surroundin
g area at
this
shopping
street
Want
to
reside
Do not
want
to
reside
Warm Alo
of
Fasc
inati
ng
Not
fasci
natin
g
Wan
t to
play
Wan
t to
exa
mine
delib
erate
ly
Live
ly
Cal
m
Node Parameter
31 32
The image
of the
surroundin
g area at
this
shopping
street
Atmo
spher
e of
urban
Atmos
phere
of
rural
area
In the next section, sensitivity analysis is achieved by back propagation method. Back
propagation method is conducted in the following method (Figure 9).
xxxXPr
u U
XU
i
iuuUxPx
j
j
Y
XY xx
jk
XYXY xxxkj
x ik ik
kXUXU uUxPxuki
Figure 9: Back propagation method (Takeyasu et al., 2010)
jY
1Y
X x x
uXU
From X
To X
From X
To X
xYX
uUX
xXY
Daisuke Suzuki et al. 39
4 Sensitivity Analysis
Now, posterior probability is calculated by setting evidence as, for example, 1.0.
Comparing Prior probability and Posterior probability, we can seek the change and
confirm the preference or image of the surrounding area at this shopping street. We set
evidence to all parameters. Therefore the analysis volume becomes too large. In this
paper, we focus on “The image of the surrounding area at this shopping street” and pick
up former half and make sensitivity analysis. We prepare another paper for the rest of
them.
As stated above, we set evidence for each parameter, and the calculated posterior
probability is exhibited in Appendix 2. The value of “Posterior probability – Prior
probability” (we call this “Difference of probability” hereafter) is exhibited in Appendix
3. The sensitivity analysis is executed by mainly using this table.
Here, we classify each item by the strength of the difference of probability.
・Strong (++,--): Select major parameter of which absolute value of difference of
probability is more than 0.05
・Medium (+,-): Select major parameter of which absolute value of difference of
probability is more than 0.01
・Weak: Else
In selecting items, negative value does not necessarily have distinct meaning, therefore
we mainly pick up positive value in the case meaning is not clear.
Now we examine each for Strong and Medium case.
4.1 Sensitively Analysis for “The image of the surrounding area at this shopping street”
(1) Setting evidence to “Beautiful”
After setting evidence to “Beautiful”, the result is exhibited in Table 2.
Table 2: Setting evidence to “Beautiful” case
Eating and drinking -
Business -
Celebration、event -
Scattered -
Full of nature +
Cheerful +
Individualistic +
Friendly +
Open +
Exclusive -
Warm +
40 Need identification and sensitivity analysis of consumers using Bayesian Network
Aloof -
Fascinating +
Want to play +
Lively +
Male -
Female +
Age: 10th ++
Age: 20th +
Age: 30th +
Age: 40th -
Age: 50th -
Age: 60th -
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Beautiful” had come under the image of the surrounding area at this shopping
street as “Full of nature”, “Cheerful”, “Individualistic”, “Friendly”, “Open”, “Warm”,
“Fascinating”, “Want to play” or “Lively” of an age of “10th
”,”20th
“ or “30th” in which
the gender is “Female”.
(Strong part is indicated by bold font.)
(2) Setting evidence to “Ugly”
After setting evidence to “Ugly”, the result is exhibited in Table 3.
Table 3: Setting evidence to “Ugly” case
Open -
Want to play -
Age: 10th -
Age: 20th --
Age: 30th -
Age: 50th ++
Age: 60th --
Age: More than 70 +
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Ugly” had come by an age of “50th“ or ” More than 70 “.
Daisuke Suzuki et al. 41
(3) Setting evidence to “Of the united feeling there is”
After setting evidence to “Of the united feeling there is”, the result is exhibited in Table
4.
Table 4: Setting evidence to “Of the united feeling there is” case
Eating and drinking -
Business -
Celebration、event -
Cheerful +
Individualistic +
Conventional -
Friendly +
Unfriendly -
Open +
Exclusive -
Want to reside +
Warm +
Aloof -
Fascinating +
Want to play +
Lively +
Age: 10th ++
Age: 30th +
Age: 40th -
Age: 50th +
Age: 60th --
Age: More than 70 ++
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Of the united feeling there is” had come under the image of the surrounding
area at this shopping street as “Cheerful”, “Individualistic”, “Friendly”,, “Open”, “Want
to reside”, “Warm”, “Fascinating”, “Want to play” or “Lively” of an age of “10th”,”30th“,
“50th” or “More than 70”.
(4) Setting evidence to “Scattered”
After setting evidence to “Scattered”, the result is exhibited in Table 5.
42 Need identification and sensitivity analysis of consumers using Bayesian Network
Table 5: Setting evidence to “Scattered” case
Eating and drinking +
Business +
Celebration、event +
Beautiful -
Ugly +
Varied -
Featureless +
Full of nature -
Cheerful -
Gloomy +
Individualistic -
Conventional +
Friendly -
Unfriendly +
Healed -
Stimulated +
Open -
Exclusive +
Want to reside -
Do not want to reside +
Warm -
Aloof +
Fascinating -
Not fascinating +
Want to play -
Lively -
Calm +
Age: 10th --
Age: 20th --
Age: 40th ++
Age: 50th ++
Age: 60th ++
Age: More than 70 +
Daisuke Suzuki et al. 43
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Scattered” had come with the purpose of visiting for “Eating and drinking”,
“Business” or “Celebration、event” under the image of the surrounding area at this
shopping street as “Ugly”, “Featureless”, “Gloomy”, “Conventional”, “Unfriendly”,
“Stimulated”, “Exclusive”, “Do not want to reside”, “Aloof”, “Not fascinating” or “Calm”
of an age of “40th”,”50th“, “60th” or “More than 70”.
(5) Setting evidence to “Varied”
After setting evidence to “Varied”, the result is exhibited in Table 6.
Table 6: Setting evidence to “Varied” case
Individualistic +
Age: 10th ++
Age: 20th -
Age: 40th -
Age: 50th +
Age: 60th --
Age: More than 70 --
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Varied” had come under the image of the surrounding area at this shopping
street as “Individualistic” of an age of “10th” or “50th”.
(6) Setting Evidence to “Featureless”
After setting evidence to “Featureless”, the result is exhibited in Table 7.
Table 7: Setting evidence to “Featureless” case
Leisure, amusement +
Scattered +
Cheerful -
Unfriendly +
Healed -
Stimulated +
Open -
Exclusive +
Fascinating -
44 Need identification and sensitivity analysis of consumers using Bayesian Network
Want to play -
Lively -
Age: 10th -
Age: 20th --
Age: 40th +
Age: 50th ++
Age: 60th ++
Age: More than 70 +
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Featureless” had come with the purpose of visiting for “Leisure, amusement”
under the image of the surrounding area at this shopping street as “Scattered”,
“Unfriendly”, “Stimulated” or “Exclusive” of an age of “40th”,”50th“, “60th” or “More
than 70”.
(7) Setting Evidence to “New”
After setting evidence to “New”, the result is exhibited in Table 8.
Table 8: Setting evidence to “New” case
Male -
Female +
Age: 10th -
Age: 20th +
Age: 40th +
Age: 50th -
Age: 60th +
We can observe that “Those who have an image of the surrounding area at this shopping
street as “New” had come by an age of ”20th“, “40th” or “60th” in which the gender is
“Female”.
(8) Setting evidence to “Historic”
After setting evidence to “Historic”, the result is exhibited in Table 9.
Daisuke Suzuki et al. 45
Table 9: Setting evidence to “Historic” case
Male -
Female +
Age: 20th -
Age: 30th +
Age: 40th -
Age: 50th +
Age: 60th --
Age: More than 70 --
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Historic” had come by an age of “30th” or “50th” in which the gender is
“Female”.
(9) Setting evidence to “Full of nature”
After setting evidence to “Full of nature”, the result is exhibited in Table 10.
Table 10: Setting evidence to “Full of nature” case
Eating and drinking -
Business -
Celebration、event -
Leisure, amusement +
Beautiful +
Cheerful +
Individualistic +
Conventional -
Friendly +
Open +
Exclusive -
Warm +
Fascinating +
Want to play +
Lively +
Male -
Female +
Age: 10th ++
46 Need identification and sensitivity analysis of consumers using Bayesian Network
Age: 20th +
Age: 40th --
Age: 50th -
Age: 60th -
Age: More than 70 ++
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Full of nature” had come with the purpose of visiting for “Leisure, amusement”
under the image of the surrounding area at this shopping street as “Beautiful”, “Cheerful”,
“Individualistic”, “Friendly”, “Open”, “Warm”, “Fascinating”, “Want to play” or “Lively”
of an age of “10th”, ”20th“ or “More than 70” in which the gender is “Female”.
(10) Setting evidence to “Urban”
After setting evidence to “Urban”, the result is exhibited in Table 11.
Table 11: Setting evidence to “Urban” case
Age: 10th +
Age: 20th -
Age: 30th -
Age: 40th -
Age: 50th +
Age: 60th ++
Age: More than 70 -
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Urban” had come by an age of “10th”, “50th” or ”60th“.
(11) Setting evidence to “Cheerful”
After setting evidence to “Cheerful”, the result is exhibited in Table 12.
Table 12: Setting evidence to “Cheerful” case
Celebration、event -
Leisure, amusement -
Of the united feeling there is +
Scattered -
Varied +
Daisuke Suzuki et al. 47
Individualistic +
Conventional -
Friendly +
Unfriendly -
Healed +
Open +
Exclusive -
Want to reside +
Warm +
Aloof -
Fascinating +
Not fascinating -
Want to play +
Lively +
Male -
Female +
Age: 10th ++
Age: 20th ++
Age: 30th +
Age: 40th +
Age: 50th --
Age: 60th --
Age: More than 70 --
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Cheerful” had come under the image of the surrounding area at this shopping
street as “Of the united feeling there is”, “Varied”, “Individualistic”, “Friendly”,
“Healed”, “Want to reside”, “Warm”, “Fascinating”, “Want to play” or “Lively” of an age
of “10th” or ”20th“, “30th” or “40th” in which the gender is “Female”.
(12) Setting evidence to “Gloomy”
After setting evidence to “Gloomy”, the result is exhibited in Table 13.
48 Need identification and sensitivity analysis of consumers using Bayesian Network
Table 13: Setting evidence to “Gloomy” case
Eating and drinking +
Business +
Celebration、event +
Beautiful -
Of the united feeling there is -
Scattered +
Varied -
Individualistic -
Conventional +
Friendly -
Unfriendly +
Healed -
Stimulated +
Open -
Exclusive +
Warm -
Aloof +
Fascinating -
Not fascinating +
Want to play -
Lively -
Male +
Female -
Age: 10th --
Age: 20th -
Age: 30th -
Age: 40th +
Age: 50th ++
Age: 60th ++
Age: More than 70 ++
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Gloomy” had come with the purpose of visiting for“Eating and drinking”,
“Business” or “Celebration、event” under the image of the surrounding area at this
shopping street as “Scattered”, “Conventional”, “Unfriendly”, “Stimulated”, “Exclusive”,
Daisuke Suzuki et al. 49
“Aloof” or “Not fascinating” of an age of “40th”, “50th”, ”60th“ or “More than 70” in
which the gender is “Male.
(13) Setting evidence to “Individualistic”
After setting evidence to “Individualistic”, the result is exhibited in Table 14.
Table 14: Setting evidence to “Individualistic” case
Eating and drinking -
Business -
Celebration、event -
Of the united feeling there is +
Scattered -
Varied +
New -
Full of nature +
Cheerful +
Friendly +
Unfriendly -
Healed +
Open +
Exclusive -
Want to reside +
Warm +
Aloof -
Fascinating +
Want to play +
Lively +
Male -
Female +
Age: 10th ++
Age: 20th -
Age: 30th --
Age: 40th --
Age: 50th +
Age: 60th --
Age: More than 70 --
50 Need identification and sensitivity analysis of consumers using Bayesian Network
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Individualistic” had come under the image of the surrounding area at this
shopping street as “Of the united feeling there is”, “Varied”, “Full of nature”, “Cheerful”,
“Friendly”, “Healed”, “Open”, “Want to reside”, “Warm”, “Fascinating”, “Want to play”
or “Lively” of an age of “10th” or “50th” in which the gender is “Female”.
(14) Setting evidence to “Conventional”
After setting evidence to “Conventional”, the result is exhibited in Table 15.
Table 15: Setting evidence to “Conventional” case
Eating and drinking +
Business +
Celebration、event +
Beautiful -
Of the united feeling there is -
Scattered +
Varied -
Full of nature -
Cheerful -
Gloomy +
Friendly -
Unfriendly +
Healed -
Stimulated +
Open -
Exclusive +
Want to reside -
Warm -
Aloof +
Fascinating -
Not fascinating +
Want to play -
Lively -
Male +
Female -
Daisuke Suzuki et al. 51
Age: 10th --
Age: 20th +
Age: 30th ++
Age: 40th +
Age: 50th ++
Age: 60th ++
Age: More than 70 +
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Conventional” had come with the purpose of visiting for“Eating and drinking”,
“Business” or “Celebration、event” under the image of the surrounding area at this
shopping street as “Scattered”, “Gloomy”, “Unfriendly”, “Stimulated”, “Exclusive”,
“Aloof” or “Not fascinating” of an age of “20th”, “30th”, “40th”, “50th”, ”60th“ or
“More than 70” in which the gender is “Male”.
(15) Setting evidence to “Friendly”
After setting evidence to “Friendly”, the result is exhibited in Table 16.
Table 16: Setting evidence to “Friendly” case
Eating and drinking -
Business -
Celebration、event -
Beautiful +
Of the united feeling there is +
Scattered -
Varied +
New -
Full of nature +
Cheerful +
Gloomy -
Individualistic +
Conventional -
Healed +
Stimulated -
Open +
Exclusive -
52 Need identification and sensitivity analysis of consumers using Bayesian Network
Want to reside +
Do not want to reside -
Warm +
Aloof -
Fascinating +
Not fascinating -
Want to play +
Lively +
Calm -
Male -
Female +
Age: 10th ++
Age: 20th -
Age: 30th -
Age: 40th --
Age: 50th -
Age: 60th --
Age: More than 70 --
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Friendly” had come under the image of the surrounding area at this shopping
street as “Beautiful”, “Of the united feeling there is”, “Varied”, “Full of nature”,
“Cheerful”, “Individualistic”, “Healed”, “Open”, “Want to reside”, “Warm”,
“Fascinating”, “Want to play” or “Lively” of an age of “10th” in which the gender is
“Female”.
(16) Setting evidence to “Unfriendly”
After setting evidence to “Unfriendly”, the result is exhibited in Table 17.
Table 17: Setting evidence to “Unfriendly” case
Leisure, amusement +
Of the united feeling there is -
Scattered +
Varied -
Cheerful -
Individualistic -
Daisuke Suzuki et al. 53
Conventional +
Healed -
Stimulated +
Open -
Exclusive +
Warm -
Aloof +
Fascinating -
Want to play -
Lively -
Age: 10th --
Age: 20th -
Age: 30th -
Age: 40th +
Age: 50th ++
Age: 60th ++
Age: More than 70 ++
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Unfriendly” had come with the purpose of visiting for “Leisure, amusement”
under the image of the surrounding area at this shopping street as “Scattered”,
“Conventional”, “Stimulated”, “Exclusive” or “Aloof” of an age of “40th”,
“50th”, ”60th“ or “More than 70”.
5 Remarks
The Results for Bayesian Network Analysis are as follows.
In the Bayesian Network Analysis, model was built under the examination of the causal
relationship among items. Sensitively Analysis was conducted after that. The main result
of sensitively analysis is as follows.
We can observe that “Those who have an image of the surrounding area at this
shopping street as “Beautiful” had come under the image of the surrounding area at this
shopping street as “Full of nature”, “Cheerful”, “Individualistic”, “Friendly”, “Open”,
“Warm”, “Fascinating”, “Want to play” or “Lively” of an age of “10th
”,”20th
“ or “30th”
in which the gender is “Female”.
54 Need identification and sensitivity analysis of consumers using Bayesian Network
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Of the united feeling there is” had come under the image of the surrounding
area at this shopping street as “Cheerful”, “Individualistic”, “Friendly”,, “Open”, “Want
to reside”, “Warm”, “Fascinating”, “Want to play” or “Lively” of an age of “10th”,”30th“,
“50th” or “More than 70”.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Scattered” had come with the purpose of visiting for “Eating and drinking”,
“Business” or “Celebration、event” under the image of the surrounding area at this
shopping street as “Ugly”, “Featureless”, “Gloomy”, “Conventional”, “Unfriendly”,
“Stimulated”, “Exclusive”, “Do not want to reside”, “Aloof”, “Not fascinating” or “Calm”
of an age of “40th”,”50th“, “60th” or “More than 70”.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Full of nature” had come with the purpose of visiting for “Leisure, amusement”
under the image of the surrounding area at this shopping street as “Beautiful”, “Cheerful”,
“Individualistic”, “Friendly”, “Open”, “Warm”, “Fascinating”, “Want to play” or “Lively”
of an age of “10th”, ”20th“ or “More than 70” in which the gender is “Female”.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Cheerful” had come under the image of the surrounding area at this shopping
street as “Of the united feeling there is”, “Varied”, “Individualistic”, “Friendly”,
“Healed”, “Want to reside”, “Warm”, “Fascinating”, “Want to play” or “Lively” of an age
of “10th” or ”20th“, “30th” or “40th” in which the gender is “Female”.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Gloomy” had come with the purpose of visiting for“Eating and drinking”,
“Business” or “Celebration、event” under the image of the surrounding area at this
shopping street as “Scattered”, “Conventional”, “Unfriendly”, “Stimulated”, “Exclusive”,
“Aloof” or “Not fascinating” of an age of “40th”, “50th”, ”60th“ or “More than 70” in
which the gender is “Male.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Friendly” had come under the image of the surrounding area at this shopping
street as “Beautiful”, “Of the united feeling there is”, “Varied”, “Full of nature”,
“Cheerful”, “Individualistic”, “Healed”, “Open”, “Want to reside”, “Warm”,
Daisuke Suzuki et al. 55
“Fascinating”, “Want to play” or “Lively” of an age of “10th” in which the gender is
“Female”.
6 Conclusion
Shopping streets at local city in Japan became old and are generally declining. In this paper,
we handle the area rebirth and/or regional revitalization of shopping street. We focus on
Fuji city in Japan. Four big festivals are held at Fuji city. There is a big difference between
Fuji Shopping Street and Yoshiwara Shopping Street. Therefore we focus Fuji Shopping
Street in this paper. Many people visit these festivals including residents in that area.
Therefore a questionnaire investigation to the residents and visitors is conducted during
these periods in order to clarify residents and visitors’ needs for the shopping street, and
utilize them to the plan building of the area rebirth and/or regional revitalization of
shopping street. These are analyzed by using Bayesian Network. Sensitivity analysis is
also conducted. As there are so many items, we focus on “The image of the surrounding
area at this shopping street” and pick up former half and make sensitivity analysis in this
paper. By that model, the causal relationship is sequentially chained by the characteristics
of visitors, the purpose of visiting and the image of the surrounding area at this shopping
street.
The Results for Bayesian Network Analysis are as follows.
In the Bayesian Network Analysis, model was built under the examination of the causal
relationship among items. Sensitively Analysis was conducted after that. The main result
of sensitively analysis is as follows.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Scattered” had come with the purpose of visiting for “Eating and drinking”,
“Business” or “Celebration、event” under the image of the surrounding area at this
shopping street as “Ugly”, “Featureless”, “Gloomy”, “Conventional”, “Unfriendly”,
“Stimulated”, “Exclusive”, “Do not want to reside”, “Aloof”, “Not fascinating” or “Calm”
of an age of “40th”,”50th“, “60th” or “More than 70”.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Full of nature” had come with the purpose of visiting for “Leisure, amusement”
under the image of the surrounding area at this shopping street as “Beautiful”, “Cheerful”,
“Individualistic”, “Friendly”, “Open”, “Warm”, “Fascinating”, “Want to play” or “Lively”
of an age of “10th”, ”20th“ or “More than 70” in which the gender is “Female”.
56 Need identification and sensitivity analysis of consumers using Bayesian Network
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Cheerful” had come under the image of the surrounding area at this shopping
street as “Of the united feeling there is”, “Varied”, “Individualistic”, “Friendly”,
“Healed”, “Want to reside”, “Warm”, “Fascinating”, “Want to play” or “Lively” of an age
of “10th” or ”20th“, “30th” or “40th” in which the gender is “Female”.
We can observe that “Those who have an image of the surrounding area at this shopping
street as “Friendly” had come under the image of the surrounding area at this shopping
street as “Beautiful”, “Of the united feeling there is”, “Varied”, “Full of nature”,
“Cheerful”, “Individualistic”, “Healed”, “Open”, “Want to reside”, “Warm”,
“Fascinating”, “Want to play” or “Lively” of an age of “10th” in which the gender is
“Female”.
The analysis utilizing Bayesian Network enabled us to visualize the causal relationship
among items. Furthermore, sensitivity analysis brought us estimating and predicting the
prospective visitors. Sensitivity analysis was achieved by back propagation method.
These are utilized for constructing a much more effective and useful plan building.
Although it has a limitation that it is restricted in the number of research, we could obtain
the fruitful results. To confirm the findings by utilizing the new consecutive visiting
records would be the future works to be investigated.
ACKNOWLEDGEMENTS
The authors are grateful to all those who supported us for answering the questionnaire
investigation.
References
[1] Inoue, Akiko(2017) “Changes in Local Communities Brought by Municipal
Mergers : From the Viewpoint of Tourism Promotion as the Main Industry”, Bulletin
of the Faculty of Regional Development Studies, Otemon Gakuin University, Vol.2,
pp.1-32.
[2] Ingu, Shuzo / Uemura, Miki / Uchida, Yuka / Omiya, Misa / Miura, Taiki / Hironori,
Hironori(2017)”A study on the application of geothermal power generation to local
Daisuke Suzuki et al. 57
revitalization in Obama Town, Unzen City: in consideration of futurability in
Obama”, Environmental Science Research, Nagasaki University, 20(1), pp.51-63.
[3] Kotani, Akihiro (2017), The implementation report of the Machi-lab shutter art
project, Bulletin of Wakkanai Hokusei Gakuen University,(17),207-218.
[4] Ohkubo, Yukio (2017), Current status and problems in Jigenji-dori shopping area:
from a consumer questionnaire, Bulletin of Local Research, Kagoshima
International University, Vol.44 no.2 p.1 -15.
[5] Shioya Hideo, Overview and application of tourism statistics: Analysis using
statistical survey on overnight travels, Journal of Economic Structures 17(1-2),
16-29, 2009 Pan Pacific Association of Input-Output Studies.
[6] Japan Tourism Agency (2015) “Research study on economic impacts of tourism in
Japan 2013, p3
[7] Yoshida, Ituki (2009) “Consideration on the Characteristic of Visitors' Activity and
the Research Method for Tourist Visitors in Urban Areas”
[8] Doi, Hideji(2009) “Evaluation of policies to build tourist destinations and statistical
analysis” Nippon Hyoron Sha.
[9] https://www.jnto.go.jp/eng/location/rtg/pdf/pg-410.pdf#search='Izupeninsula'
[10] http://www.kawazu-onsen.com/eng/
[11] Atami city (2015) “2014 Survey of Tourist Behavior”
[12] Kano, Michiko (2011) Characteristic analysis of Atami tourists: Reconsideration
based on data add and modify Shizuoka Economic Research. 16 (2), p. 61-78,
Shizuoka University
58 Need identification and sensitivity analysis of consumers using Bayesian Network
APPENDIX 1
Questionnaire Sheet about the Image Around the Shopping Street
1. How often do you come to this shopping street?
a. Everyday b. ( ) times a week c. ( ) times a month d. ( ) times a year
e. miscellaneous ( )
2. What is the purpose of visiting here? (Plural answers allowed)
a. shopping b. eating and drinking c. business d. celebration、event e. leisure,
amusement
f. miscellaneous ( )
3. How do you feel about the image of the surrounding area at this shopping street?
Select the position
Beautiful ・ ・ ・ ・ ・ Ugly
Of the united
feeling there is
・ ・ ・ ・ ・ Scattered
Varied ・ ・ ・ ・ ・ Featureless
New ・ ・ ・ ・ ・ Historic
Full of nature ・ ・ ・ ・ ・ Urban
Cheerful ・ ・ ・ ・ ・ Gloomy
Individualistic ・ ・ ・ ・ ・ Conventional
Friendly ・ ・ ・ ・ ・ Unfriendly
Healed ・ ・ ・ ・ ・ Stimulated
Open ・ ・ ・ ・ ・ exclusive
Want to reside ・ ・ ・ ・ ・ Do not want to
reside
Warm ・ ・ ・ ・ ・ Aloof
Fascinating ・ ・ ・ ・ ・ Not fascinating
Want to play ・ ・ ・ ・ ・ Want to
examine
deliberately
Lively ・ ・ ・ ・ ・ Calm
Atmosphere of
urban
・ ・ ・ ・ ・ Atmosphere of
rural area
Daisuke Suzuki et al. 59
4. There are many old building at the age of nearly 50 years. Do you think we can still
use them?
a. Can use it b. Cannot use it C. Have no idea
5. Is there any functions or facilities that will be useful?
6. Comments
7. Sex
a. Male b. Female
8. Age
a.10th b.20th c.30th d.40th e.50th f.6th g. More than70
9. Residence
a. Fuji City b. Fujinomiya City c. Numazu City d. Mishima City e. Shizuoka
City f. Miscellaneous in Shizuoka Prefecture
g. Outside of Shizuoka Prefecture[ ]
60 Need identification and sensitivity analysis of consumers using Bayesian Network
APPENDIX 2
Calculated posterior probability
Shoppin
gE
ating a
nd
drin
kin
gB
usin
ess
Celeb
ration、
event
Leisu
re,
amusem
ent
Beau
tiful
Ugly
Of th
e u
nited
feeling th
ere is
Scattered
Varied
Featu
reless
New
Histo
ricF
ull o
f natu
reU
rban
Cheerfu
lG
loom
yIn
div
idualistic
Conventio
nal
Frien
dly
Unfrien
dly
Shoppin
g0
.215
10
.211
0.2
08
0.2
11
0.2
33
0.2
16
0.2
11
0.2
12
0.2
14
0.2
09
0.2
15
0.2
22
0.2
16
0.2
17
0.2
11
0.2
14
0.2
15
0.2
08
0.2
14
0.2
10
0.2
22
Eatin
g a
nd d
rinkin
g0
.174
0.1
72
10
.197
0.1
91
0.1
55
0.1
67
0.1
78
0.1
63
0.1
83
0.1
66
0.1
75
0.1
81
0.1
73
0.1
66
0.1
69
0.1
69
0.1
80
0.1
57
0.1
87
0.1
63
0.1
78
Busin
ess0
.103
0.1
01
0.1
17
10
.113
0.0
90
0.0
99
0.1
05
0.0
96
0.1
06
0.0
98
0.1
02
0.1
03
0.1
02
0.0
99
0.0
99
0.0
99
0.1
06
0.0
92
0.1
12
0.0
97
0.1
04
Celeb
ration、
even
t0
.396
0.3
92
0.4
33
0.4
35
10
.374
0.3
80
0.4
07
0.3
74
0.4
16
0.3
81
0.4
01
0.4
04
0.3
94
0.3
80
0.3
90
0.3
72
0.4
10
0.3
64
0.4
21
0.3
73
0.4
11
Leisu
re, am
usem
ent
0.0
89
0.0
98
0.0
80
0.0
79
0.0
84
10
.091
0.0
86
0.0
88
0.0
90
0.0
87
0.0
91
0.0
92
0.0
90
0.0
92
0.0
90
0.0
85
0.0
89
0.0
88
0.0
87
0.0
88
0.0
96
Beau
tiful
0.3
39
0.3
42
0.3
24
0.3
28
0.3
26
0.3
46
10
0.3
47
0.3
28
0.3
47
0.3
36
0.3
36
0.3
41
0.3
49
0.3
39
0.3
52
0.3
31
0.3
52
0.3
30
0.3
50
0.3
31
Ugly
0.2
92
0.2
87
0.2
99
0.2
99
0.3
00
0.2
85
0.0
00
10
.288
0.3
01
0.2
93
0.2
94
0.2
88
0.2
93
0.2
90
0.2
95
0.2
84
0.2
96
0.2
94
0.2
96
0.2
90
0.2
98
Of th
e united
feeling
there is
0.2
55
0.2
51
0.2
39
0.2
41
0.2
40
0.2
53
0.2
60
0.2
51
10
0.2
64
0.2
50
0.2
48
0.2
55
0.2
60
0.2
57
0.2
68
0.2
47
0.2
73
0.2
41
0.2
67
0.2
43
Scattered
0.3
81
0.3
81
0.3
99
0.3
92
0.4
00
0.3
90
0.3
68
0.3
92
01
0.3
68
0.3
92
0.3
89
0.3
80
0.3
69
0.3
86
0.3
54
0.3
95
0.3
61
0.3
98
0.3
61
0.4
06
Varied
0.1
75
0.1
71
0.1
67
0.1
67
0.1
68
0.1
71
0.1
79
0.1
76
0.1
81
0.1
69
10
0.1
70
0.1
76
0.1
80
0.1
77
0.1
82
0.1
70
0.1
87
0.1
69
0.1
83
0.1
68
Featu
reless
0.4
90
0.4
91
0.4
91
0.4
87
0.4
96
0.5
03
0.4
84
0.4
94
0.4
81
0.5
04
01
0.4
93
0.4
89
0.4
85
0.4
96
0.4
73
0.4
97
0.4
82
0.4
96
0.4
80
0.5
06
New
0.1
24
0.1
28
0.1
29
0.1
24
0.1
27
0.1
28
0.1
23
0.1
22
0.1
20
0.1
27
0.1
20
0.1
25
10
0.1
22
0.1
22
0.1
23
0.1
25
0.1
17
0.1
26
0.1
19
0.1
27
Histo
ric0
.561
0.5
65
0.5
57
0.5
56
0.5
59
0.5
70
0.5
64
0.5
63
0.5
62
0.5
60
0.5
64
0.5
61
01
0.5
64
0.5
60
0.5
61
0.5
60
0.5
64
0.5
59
0.5
63
0.5
62
Full o
f natu
re0
.370
0.3
74
0.3
50
0.3
58
0.3
55
0.3
81
0.3
80
0.3
67
0.3
79
0.3
59
0.3
79
0.3
67
0.3
64
0.3
72
10
0.3
82
0.3
62
0.3
86
0.3
60
0.3
82
0.3
64
Urb
an
0.2
31
0.2
28
0.2
25
0.2
23
0.2
28
0.2
35
0.2
31
0.2
33
0.2
33
0.2
35
0.2
33
0.2
34
0.2
27
0.2
31
01
0.2
28
0.2
33
0.2
39
0.2
29
0.2
33
0.2
35
Cheerfu
l0
.259
0.2
59
0.2
51
0.2
49
0.2
44
0.2
49
0.2
68
0.2
52
0.2
73
0.2
41
0.2
69
0.2
50
0.2
56
0.2
59
0.2
67
0.2
55
10
0.2
77
0.2
44
0.2
73
0.2
39
Glo
om
y0
.432
0.4
34
0.4
44
0.4
45
0.4
47
0.4
35
0.4
21
0.4
38
0.4
19
0.4
48
0.4
20
0.4
39
0.4
35
0.4
31
0.4
22
0.4
34
01
0.4
13
0.4
45
0.4
16
0.4
51
Indiv
idualistic
0.2
38
0.2
32
0.2
14
0.2
13
0.2
18
0.2
37
0.2
47
0.2
40
0.2
55
0.2
26
0.2
54
0.2
35
0.2
26
0.2
39
0.2
48
0.2
46
0.2
54
0.2
28
10
0.2
57
0.2
26
Conventio
nal
0.4
38
0.4
40
0.4
71
0.4
79
0.4
66
0.4
32
0.4
26
0.4
45
0.4
16
0.4
58
0.4
22
0.4
44
0.4
47
0.4
37
0.4
26
0.4
34
0.4
14
0.4
53
01
0.4
16
0.4
57
Frien
dly
0.4
43
0.4
34
0.4
13
0.4
16
0.4
17
0.4
35
0.4
56
0.4
39
0.4
65
0.4
21
0.4
64
0.4
35
0.4
27
0.4
45
0.4
57
0.4
47
0.4
68
0.4
27
0.4
79
0.4
20
10
Unfrien
dly
0.2
36
0.2
45
0.2
42
0.2
42
0.2
46
0.2
57
0.2
30
0.2
41
0.2
25
0.2
52
0.2
26
0.2
44
0.2
42
0.2
36
0.2
32
0.2
40
0.2
18
0.2
46
0.2
23
0.2
46
01
Healed
0.2
85
0.2
79
0.2
79
0.2
82
0.2
75
0.2
67
0.2
91
0.2
83
0.2
95
0.2
71
0.2
95
0.2
78
0.2
77
0.2
85
0.2
91
0.2
83
0.3
01
0.2
76
0.3
00
0.2
75
0.2
97
0.2
68
Stim
ulated
0.1
80
0.1
87
0.1
82
0.1
85
0.1
83
0.1
93
0.1
78
0.1
83
0.1
76
0.1
88
0.1
74
0.1
84
0.1
82
0.1
79
0.1
80
0.1
84
0.1
74
0.1
85
0.1
78
0.1
84
0.1
74
0.1
92
Open
0.2
57
0.2
54
0.2
36
0.2
39
0.2
37
0.2
56
0.2
66
0.2
47
0.2
73
0.2
38
0.2
68
0.2
50
0.2
48
0.2
56
0.2
65
0.2
57
0.2
76
0.2
45
0.2
76
0.2
40
0.2
73
0.2
37
Exclu
sive
0.3
93
0.4
07
0.4
13
0.4
04
0.4
11
0.4
07
0.3
81
0.4
01
0.3
77
0.4
12
0.3
76
0.4
00
0.4
06
0.3
93
0.3
83
0.3
91
0.3
73
0.4
07
0.3
70
0.4
08
0.3
72
0.4
18
Wan
t to resid
e0
.241
0.2
43
0.2
30
0.2
31
0.2
30
0.2
46
0.2
46
0.2
39
0.2
50
0.2
33
0.2
47
0.2
39
0.2
37
0.2
41
0.2
47
0.2
44
0.2
52
0.2
36
0.2
56
0.2
32
0.2
50
0.2
35
Do n
ot w
ant to
resid
e0
.395
0.3
97
0.3
96
0.3
92
0.4
00
0.4
06
0.3
88
0.3
95
0.3
90
0.4
05
0.3
87
0.4
01
0.3
98
0.3
94
0.3
89
0.3
99
0.3
82
0.4
03
0.3
88
0.3
99
0.3
86
0.4
07
Warm
0.3
98
0.3
93
0.3
75
0.3
70
0.3
75
0.3
95
0.4
09
0.3
93
0.4
16
0.3
81
0.4
13
0.3
92
0.3
91
0.3
98
0.4
09
0.4
02
0.4
22
0.3
83
0.4
27
0.3
77
0.4
18
0.3
78
Alo
of
0.2
52
0.2
54
0.2
64
0.2
69
0.2
65
0.2
51
0.2
44
0.2
59
0.2
43
0.2
63
0.2
44
0.2
56
0.2
53
0.2
52
0.2
45
0.2
52
0.2
37
0.2
62
0.2
40
0.2
63
0.2
41
0.2
65
Fascin
ating
0.2
23
0.2
22
0.2
05
0.2
10
0.2
08
0.2
23
0.2
32
0.2
17
0.2
37
0.2
07
0.2
33
0.2
17
0.2
16
0.2
24
0.2
32
0.2
23
0.2
40
0.2
14
0.2
42
0.2
10
0.2
38
0.2
08
Not fascin
atin
g0
.423
0.4
24
0.4
35
0.4
30
0.4
36
0.4
28
0.4
15
0.4
29
0.4
13
0.4
38
0.4
15
0.4
29
0.4
27
0.4
22
0.4
16
0.4
25
0.4
06
0.4
32
0.4
10
0.4
33
0.4
11
0.4
38
Wan
t to p
lay0
.218
0.2
17
0.2
02
0.1
98
0.2
00
0.2
16
0.2
28
0.2
07
0.2
33
0.1
98
0.2
29
0.2
10
0.2
15
0.2
18
0.2
26
0.2
14
0.2
42
0.2
04
0.2
35
0.2
01
0.2
34
0.1
96
Wan
t to ex
am
ine
delib
erately0
.312
0.3
21
0.3
14
0.3
12
0.3
13
0.3
30
0.3
10
0.3
10
0.3
10
0.3
18
0.3
06
0.3
16
0.3
17
0.3
11
0.3
12
0.3
15
0.3
07
0.3
16
0.3
08
0.3
14
0.3
06
0.3
23
Liv
ely
0.1
81
0.1
78
0.1
75
0.1
76
0.1
73
0.1
74
0.1
86
0.1
79
0.1
88
0.1
72
0.1
88
0.1
77
0.1
77
0.1
81
0.1
86
0.1
80
0.1
93
0.1
75
0.1
91
0.1
74
0.1
90
0.1
70
Calm
0.5
20
0.5
30
0.5
28
0.5
27
0.5
28
0.5
38
0.5
14
0.5
21
0.5
11
0.5
33
0.5
08
0.5
26
0.5
27
0.5
19
0.5
15
0.5
23
0.5
07
0.5
30
0.5
08
0.5
29
0.5
07
0.5
39
Atm
osp
here o
f urb
an
0.0
97
0.0
95
0.0
99
0.0
97
0.0
99
0.0
90
0.0
95
0.0
99
0.0
97
0.0
97
0.0
97
0.0
96
0.0
97
0.0
97
0.0
95
0.0
96
0.0
97
0.0
97
0.0
97
0.0
97
0.0
97
0.0
95
Atm
osp
here o
f rura
l
area0
.629
0.6
30
0.6
33
0.6
26
0.6
35
0.6
43
0.6
23
0.6
31
0.6
22
0.6
41
0.6
23
0.6
35
0.6
33
0.6
28
0.6
24
0.6
33
0.6
15
0.6
36
0.6
21
0.6
35
0.6
21
0.6
42
Male
0.4
33
0.3
64
0.4
85
0.5
56
0.4
92
0.2
85
0.3
90
0.4
44
0.4
25
0.4
42
0.4
16
0.4
31
0.3
80
0.4
19
0.3
92
0.4
36
0.3
84
0.4
76
0.3
93
0.4
77
0.4
13
0.4
21
Fem
ale0
.567
0.6
36
0.5
15
0.4
44
0.5
08
0.7
15
0.6
10
0.5
56
0.5
75
0.5
58
0.5
84
0.5
69
0.6
20
0.5
81
0.6
08
0.5
64
0.6
16
0.5
24
0.6
07
0.5
23
0.5
87
0.5
79
10th
0.2
05
0.1
72
0.0
82
0.0
88
0.1
11
0.1
97
0.2
44
0.1
95
0.2
92
0.1
37
0.2
73
0.1
85
0.1
41
0.2
08
0.2
48
0.2
36
0.2
78
0.1
63
0.3
43
0.1
17
0.2
95
0.1
35
20th
0.1
66
0.2
03
0.2
19
0.2
56
0.1
69
0.1
24
0.1
77
0.1
36
0.1
68
0.1
33
0.1
48
0.1
42
0.1
89
0.1
59
0.1
76
0.1
37
0.2
25
0.1
53
0.1
38
0.1
78
0.1
58
0.1
41
30th
0.2
51
0.2
29
0.2
63
0.2
86
0.2
77
0.2
61
0.2
63
0.2
40
0.2
16
0.2
47
0.2
58
0.2
48
0.2
53
0.2
59
0.2
53
0.2
20
0.2
16
0.2
42
0.1
91
0.2
81
0.2
45
0.2
34
40th
0.1
70
0.1
68
0.2
25
0.1
39
0.2
03
0.1
43
0.1
30
0.1
74
0.1
59
0.1
98
0.1
40
0.1
79
0.2
12
0.1
65
0.1
19
0.1
58
0.1
56
0.1
86
0.1
33
0.1
75
0.1
35
0.1
78
50th
0.1
02
0.0
81
0.1
40
0.1
46
0.1
36
0.0
58
0.0
89
0.1
60
0.0
79
0.1
46
0.1
08
0.1
15
0.0
89
0.1
04
0.0
97
0.1
19
0.0
76
0.1
18
0.1
16
0.1
27
0.0
91
0.1
35
60th
0.0
70
0.0
79
0.0
51
0.0
53
0.0
66
0.1
33
0.0
63
0.0
56
0.0
56
0.1
03
0.0
48
0.0
93
0.0
80
0.0
64
0.0
66
0.0
99
0.0
32
0.0
91
0.0
52
0.0
82
0.0
49
0.1
16
More th
an70
0.0
35
0.0
69
0.0
19
0.0
32
0.0
37
0.0
86
0.0
34
0.0
39
0.0
30
0.0
37
0.0
25
0.0
38
0.0
35
0.0
41
0.0
41
0.0
30
0.0
17
0.0
47
0.0
25
0.0
39
0.0
26
0.0
61
Gen
der
Age
The p
urp
ose o
f
visitin
g
The im
age o
f
the su
rrou
nd
ing
area at this
shoppin
g street
The p
urp
ose o
f visitin
gnam
e_fu
jistate
Prio
rT
he im
age o
f the su
rrou
nd
ing a
rea at this sh
oppin
g street
Daisuke Suzuki et al. 61
Healed
Stim
ulated
Open
Exclu
sive
Wan
t to resid
eD
o n
ot w
ant to
reside
Warm
Alo
of
Fascin
ating
Not fascin
atin
gW
ant to
play
Wan
t to
exam
ine
delib
erately
Liv
elyC
almA
tmosp
here o
f
urb
an
Atm
osp
here o
f
rura
l areaM
aleF
emale
10th
20th
30th
40th
50th
60th
More th
an70
0.2
10
0.2
22
0.2
12
0.2
22
0.2
16
0.2
16
0.2
12
0.2
16
0.2
14
0.2
14
0.2
13
0.2
20
0.2
11
0.2
19
0.2
11
0.2
15
0.1
81
0.2
40
0.1
80
0.2
62
0.1
96
0.2
12
0.1
70
0.2
40
0.4
19
0.1
71
0.1
75
0.1
60
0.1
83
0.1
65
0.1
74
0.1
64
0.1
83
0.1
60
0.1
79
0.1
62
0.1
74
0.1
69
0.1
76
0.1
79
0.1
75
0.1
95
0.1
58
0.0
70
0.2
29
0.1
82
0.2
30
0.2
40
0.1
27
0.0
95
0.1
02
0.1
05
0.0
96
0.1
06
0.0
97
0.1
01
0.0
96
0.1
09
0.0
96
0.1
05
0.0
93
0.1
02
0.1
01
0.1
03
0.1
03
0.1
02
0.1
32
0.0
80
0.0
44
0.1
58
0.1
17
0.0
84
0.1
48
0.0
77
0.0
95
0.3
83
0.4
01
0.3
66
0.4
14
0.3
77
0.4
00
0.3
73
0.4
17
0.3
67
0.4
08
0.3
62
0.3
96
0.3
79
0.4
01
0.4
04
0.3
99
0.4
49
0.3
55
0.2
14
0.4
02
0.4
37
0.4
73
0.5
31
0.3
71
0.4
19
0.0
83
0.0
93
0.0
88
0.0
92
0.0
90
0.0
91
0.0
89
0.0
88
0.0
90
0.0
89
0.0
89
0.0
92
0.0
85
0.0
91
0.0
84
0.0
90
0.0
58
0.1
12
0.0
85
0.0
66
0.0
92
0.0
75
0.0
50
0.1
68
0.2
16
0.3
47
0.3
35
0.3
52
0.3
29
0.3
47
0.3
34
0.3
49
0.3
28
0.3
53
0.3
33
0.3
56
0.3
37
0.3
49
0.3
35
0.3
35
0.3
36
0.3
06
0.3
65
0.4
03
0.3
62
0.3
54
0.2
59
0.2
97
0.3
05
0.3
33
0.2
90
0.2
96
0.2
80
0.2
98
0.2
89
0.2
92
0.2
88
0.3
00
0.2
84
0.2
96
0.2
78
0.2
90
0.2
88
0.2
93
0.2
98
0.2
93
0.3
00
0.2
86
0.2
78
0.2
38
0.2
79
0.2
98
0.4
60
0.2
33
0.3
23
0.2
64
0.2
50
0.2
71
0.2
44
0.2
64
0.2
52
0.2
66
0.2
45
0.2
70
0.2
48
0.2
71
0.2
53
0.2
65
0.2
50
0.2
54
0.2
52
0.2
50
0.2
58
0.3
63
0.2
58
0.2
19
0.2
38
0.1
97
0.2
04
0.2
20
0.3
62
0.3
97
0.3
52
0.4
00
0.3
68
0.3
90
0.3
64
0.3
98
0.3
53
0.3
94
0.3
45
0.3
87
0.3
61
0.3
90
0.3
82
0.3
88
0.3
88
0.3
75
0.2
53
0.3
05
0.3
74
0.4
43
0.5
46
0.5
56
0.3
99
0.1
82
0.1
70
0.1
83
0.1
68
0.1
79
0.1
72
0.1
82
0.1
70
0.1
83
0.1
72
0.1
84
0.1
71
0.1
82
0.1
71
0.1
76
0.1
73
0.1
69
0.1
80
0.2
33
0.1
56
0.1
80
0.1
44
0.1
86
0.1
20
0.1
24
0.4
78
0.5
00
0.4
76
0.4
99
0.4
84
0.4
97
0.4
82
0.4
97
0.4
76
0.4
97
0.4
70
0.4
95
0.4
78
0.4
96
0.4
87
0.4
95
0.4
88
0.4
91
0.4
41
0.4
19
0.4
82
0.5
16
0.5
52
0.6
51
0.5
33
0.1
20
0.1
25
0.1
20
0.1
28
0.1
22
0.1
25
0.1
22
0.1
25
0.1
20
0.1
25
0.1
23
0.1
26
0.1
21
0.1
26
0.1
24
0.1
25
0.1
09
0.1
35
0.0
85
0.1
41
0.1
25
0.1
54
0.1
09
0.1
42
0.1
24
0.5
62
0.5
60
0.5
61
0.5
62
0.5
62
0.5
60
0.5
62
0.5
61
0.5
64
0.5
61
0.5
63
0.5
60
0.5
61
0.5
60
0.5
63
0.5
61
0.5
43
0.5
75
0.5
69
0.5
37
0.5
78
0.5
46
0.5
74
0.5
09
0.6
56
0.3
78
0.3
70
0.3
83
0.3
60
0.3
79
0.3
64
0.3
80
0.3
61
0.3
84
0.3
63
0.3
85
0.3
69
0.3
80
0.3
67
0.3
64
0.3
67
0.3
35
0.3
97
0.4
47
0.3
90
0.3
72
0.2
60
0.3
54
0.3
49
0.4
30
0.2
30
0.2
37
0.2
32
0.2
30
0.2
34
0.2
34
0.2
34
0.2
31
0.2
31
0.2
32
0.2
27
0.2
33
0.2
30
0.2
33
0.2
28
0.2
33
0.2
33
0.2
30
0.2
66
0.1
90
0.2
02
0.2
15
0.2
72
0.3
27
0.1
99
0.2
74
0.2
50
0.2
78
0.2
46
0.2
70
0.2
50
0.2
75
0.2
44
0.2
78
0.2
49
0.2
87
0.2
55
0.2
75
0.2
52
0.2
60
0.2
53
0.2
29
0.2
81
0.3
50
0.3
50
0.2
22
0.2
37
0.1
94
0.1
16
0.1
24
0.4
18
0.4
45
0.4
13
0.4
47
0.4
23
0.4
41
0.4
15
0.4
48
0.4
13
0.4
41
0.4
02
0.4
37
0.4
16
0.4
40
0.4
31
0.4
37
0.4
74
0.3
99
0.3
42
0.3
98
0.4
16
0.4
73
0.5
00
0.5
56
0.5
81
0.2
51
0.2
36
0.2
57
0.2
25
0.2
52
0.2
34
0.2
56
0.2
26
0.2
57
0.2
31
0.2
56
0.2
35
0.2
51
0.2
33
0.2
39
0.2
35
0.2
17
0.2
55
0.3
99
0.1
98
0.1
81
0.1
87
0.2
72
0.1
78
0.1
72
0.4
24
0.4
48
0.4
10
0.4
56
0.4
21
0.4
43
0.4
15
0.4
57
0.4
11
0.4
49
0.4
03
0.4
41
0.4
22
0.4
46
0.4
39
0.4
42
0.4
83
0.4
04
0.2
51
0.4
69
0.4
90
0.4
52
0.5
49
0.5
13
0.4
85
0.4
62
0.4
29
0.4
72
0.4
20
0.4
59
0.4
33
0.4
65
0.4
23
0.4
71
0.4
30
0.4
76
0.4
34
0.4
64
0.4
32
0.4
42
0.4
37
0.4
23
0.4
58
0.6
37
0.4
21
0.4
32
0.3
52
0.3
94
0.3
09
0.3
33
0.2
22
0.2
52
0.2
18
0.2
51
0.2
30
0.2
44
0.2
25
0.2
49
0.2
21
0.2
44
0.2
13
0.2
44
0.2
22
0.2
45
0.2
33
0.2
41
0.2
30
0.2
41
0.1
55
0.2
00
0.2
20
0.2
48
0.3
14
0.3
89
0.4
09
10
0.3
00
0.2
72
0.2
92
0.2
77
0.2
95
0.2
75
0.2
99
0.2
78
0.3
01
0.2
79
0.2
99
0.2
78
0.2
87
0.2
80
0.2
88
0.2
83
0.3
65
0.3
34
0.2
80
0.2
37
0.2
75
0.1
45
0.1
62
01
0.1
72
0.1
89
0.1
81
0.1
84
0.1
76
0.1
88
0.1
73
0.1
84
0.1
67
0.1
87
0.1
74
0.1
86
0.1
75
0.1
82
0.1
77
0.1
82
0.1
54
0.2
03
0.1
21
0.1
65
0.2
48
0.2
95
0.2
85
0.2
70
0.2
45
10
0.2
68
0.2
51
0.2
72
0.2
40
0.2
79
0.2
46
0.2
84
0.2
53
0.2
72
0.2
49
0.2
53
0.2
52
0.2
52
0.2
60
0.3
88
0.2
84
0.2
46
0.2
07
0.1
20
0.2
11
0.1
62
0.3
75
0.4
12
01
0.3
84
0.4
03
0.3
75
0.4
12
0.3
70
0.4
04
0.3
62
0.4
03
0.3
73
0.4
04
0.3
96
0.3
98
0.3
87
0.3
97
0.2
51
0.3
92
0.3
45
0.4
94
0.4
89
0.4
65
0.6
56
0.2
47
0.2
42
0.2
52
0.2
35
10
0.2
50
0.2
35
0.2
52
0.2
37
0.2
53
0.2
42
0.2
48
0.2
40
0.2
39
0.2
39
0.2
26
0.2
53
0.3
15
0.2
61
0.1
95
0.2
17
0.2
17
0.2
33
0.2
47
0.3
83
0.4
04
0.3
87
0.4
05
01
0.3
88
0.4
03
0.3
86
0.4
01
0.3
81
0.4
01
0.3
83
0.4
01
0.3
93
0.3
99
0.4
09
0.3
83
0.3
67
0.3
43
0.3
57
0.4
63
0.3
80
0.5
38
0.4
95
0.4
13
0.3
88
0.4
22
0.3
80
0.4
12
0.3
91
10
0.4
21
0.3
87
0.4
30
0.3
94
0.4
15
0.3
90
0.3
97
0.3
94
0.3
52
0.4
33
0.5
49
0.4
02
0.3
63
0.3
63
0.3
43
0.3
31
0.2
10
0.2
43
0.2
62
0.2
36
0.2
65
0.2
45
0.2
57
01
0.2
38
0.2
59
0.2
29
0.2
55
0.2
41
0.2
57
0.2
54
0.2
55
0.2
85
0.2
27
0.1
83
0.2
37
0.2
34
0.2
79
0.3
40
0.2
80
0.4
09
0.2
34
0.2
14
0.2
43
0.2
10
0.2
33
0.2
18
0.2
37
0.2
12
10
0.2
48
0.2
19
0.2
36
0.2
17
0.2
21
0.2
19
0.2
10
0.2
33
0.3
32
0.2
38
0.2
16
0.1
76
0.1
31
0.1
45
0.2
20
0.4
12
0.4
32
0.4
06
0.4
35
0.4
15
0.4
30
0.4
11
0.4
35
01
0.4
00
0.4
27
0.4
11
0.4
29
0.4
25
0.4
27
0.4
41
0.4
10
0.3
48
0.3
81
0.4
16
0.4
72
0.5
03
0.5
05
0.4
85
0.2
31
0.2
01
0.2
41
0.2
01
0.2
28
0.2
10
0.2
35
0.1
99
0.2
40
0.2
07
10
0.2
34
0.2
09
0.2
18
0.2
13
0.1
80
0.2
46
0.3
22
0.2
47
0.2
32
0.2
07
0.0
71
0.0
95
0.0
86
0.3
06
0.3
25
0.3
07
0.3
20
0.3
13
0.3
17
0.3
09
0.3
15
0.3
07
0.3
15
01
0.3
07
0.3
19
0.3
08
0.3
15
0.3
01
0.3
21
0.2
83
0.3
43
0.2
61
0.3
29
0.3
09
0.4
36
0.3
88
0.1
90
0.1
75
0.1
92
0.1
72
0.1
86
0.1
76
0.1
89
0.1
73
0.1
91
0.1
76
0.1
95
0.1
78
10
0.1
81
0.1
78
0.1
75
0.1
86
0.2
34
0.2
15
0.1
81
0.1
44
0.1
57
0.1
16
0.0
86
0.5
07
0.5
39
0.5
05
0.5
35
0.5
17
0.5
28
0.5
10
0.5
31
0.5
06
0.5
27
0.5
00
0.5
30
01
0.5
15
0.5
24
0.5
16
0.5
22
0.4
44
0.5
35
0.4
63
0.5
56
0.5
60
0.6
95
0.6
56
0.0
97
0.0
94
0.0
95
0.0
98
0.0
96
0.0
96
0.0
97
0.0
97
0.0
96
0.0
97
0.0
97
0.0
95
0.0
96
0.0
96
10
0.0
98
0.0
96
0.0
94
0.0
86
0.0
97
0.1
24
0.1
12
0.0
47
0.0
86
0.6
18
0.6
37
0.6
18
0.6
38
0.6
24
0.6
36
0.6
22
0.6
36
0.6
18
0.6
35
0.6
15
0.6
34
0.6
18
0.6
34
01
0.6
29
0.6
29
0.5
85
0.5
66
0.6
20
0.6
79
0.6
57
0.7
64
0.6
56
0.4
38
0.4
27
0.4
26
0.4
27
0.4
05
0.4
49
0.3
83
0.4
90
0.4
08
0.4
51
0.3
59
0.4
17
0.4
19
0.4
30
0.4
39
0.4
33
10
0.4
33
0.4
33
0.4
33
0.4
33
0.4
33
0.4
33
0.4
33
0.5
62
0.5
73
0.5
74
0.5
73
0.5
95
0.5
51
0.6
17
0.5
10
0.5
92
0.5
49
0.6
41
0.5
83
0.5
81
0.5
70
0.5
61
0.5
67
01
0.5
67
0.5
67
0.5
67
0.5
67
0.5
67
0.5
67
0.5
67
0.2
63
0.1
75
0.3
10
0.1
31
0.2
69
0.1
91
0.2
83
0.1
49
0.3
05
0.1
69
0.3
04
0.1
86
0.2
66
0.1
75
0.2
00
0.1
91
0.2
05
0.2
05
10
00
00
0
0.1
95
0.1
88
0.1
84
0.1
66
0.1
80
0.1
45
0.1
68
0.1
56
0.1
78
0.1
50
0.1
89
0.1
83
0.1
97
0.1
71
0.1
47
0.1
50
0.1
66
0.1
66
01
00
00
0
0.2
47
0.1
70
0.2
41
0.2
21
0.2
03
0.2
28
0.2
29
0.2
34
0.2
43
0.2
47
0.2
68
0.2
10
0.2
52
0.2
24
0.2
51
0.2
48
0.2
51
0.2
51
00
10
00
0
0.1
41
0.1
56
0.1
37
0.2
14
0.1
53
0.1
99
0.1
55
0.1
89
0.1
34
0.1
90
0.1
62
0.1
79
0.1
35
0.1
82
0.2
19
0.1
84
0.1
70
0.1
70
00
01
00
0
0.0
98
0.1
40
0.0
48
0.1
26
0.0
92
0.0
98
0.0
88
0.1
37
0.0
60
0.1
21
0.0
33
0.1
01
0.0
88
0.1
10
0.1
17
0.1
06
0.1
02
0.1
02
00
00
10
0
0.0
36
0.1
15
0.0
58
0.0
83
0.0
68
0.0
96
0.0
58
0.0
78
0.0
46
0.0
84
0.0
31
0.0
98
0.0
45
0.0
94
0.0
34
0.0
85
0.0
70
0.0
70
00
00
01
0
0.0
20
0.0
56
0.0
22
0.0
59
0.0
36
0.0
44
0.0
19
0.0
57
0.0
35
0.0
40
0.0
14
0.0
44
0.0
17
0.0
44
0.0
31
0.0
37
0.0
35
0.0
35
00
00
00
1
Gen
der
Age
The im
age o
f the su
rroundin
g a
rea at this sh
oppin
g street
62 Need identification and sensitivity analysis of consumers using Bayesian Network
APPENDIX 3
Difference of probability
Shoppin
gE
ating a
nd
drin
kin
gB
usin
ess
Celeb
ration、
event
Leisu
re,
amusem
ent
Beau
tiful
Ugly
Of th
e u
nited
feeling th
ere is
Scattered
Varied
Featu
reless
New
Histo
ricF
ull o
f natu
reU
rban
Cheerfu
lG
loom
yIn
div
idualistic
Conventio
nal
Frien
dly
Unfrien
dly
Shoppin
g0.2
15
1-0
.004
-0.0
06
-0.0
03
0.0
19
0.0
02
-0.0
04
-0.0
02
-0.0
01
-0.0
05
0.0
00
0.0
08
0.0
01
0.0
02
-0.0
03
0.0
00
0.0
00
-0.0
07
-0.0
01
-0.0
04
0.0
07
Eatin
g a
nd d
rinkin
g0.1
74
-0.0
02
10.0
23
0.0
17
-0.0
20
-0.0
07
0.0
04
-0.0
11
0.0
09
-0.0
08
0.0
01
0.0
07
-0.0
01
-0.0
08
-0.0
05
-0.0
05
0.0
06
-0.0
17
0.0
13
-0.0
11
0.0
04
Busin
ess0.1
03
-0.0
01
0.0
15
10.0
10
-0.0
12
-0.0
03
0.0
02
-0.0
06
0.0
03
-0.0
04
0.0
00
0.0
01
-0.0
01
-0.0
03
-0.0
03
-0.0
04
0.0
04
-0.0
10
0.0
09
-0.0
06
0.0
02
Celeb
ration、
even
t0.3
96
-0.0
04
0.0
38
0.0
40
1-0
.022
-0.0
15
0.0
11
-0.0
22
0.0
20
-0.0
15
0.0
05
0.0
08
-0.0
01
-0.0
16
-0.0
06
-0.0
23
0.0
14
-0.0
32
0.0
25
-0.0
22
0.0
16
Leisu
re, am
usem
ent
0.0
89
0.0
09
-0.0
09
-0.0
10
-0.0
04
10.0
02
-0.0
03
-0.0
01
0.0
01
-0.0
02
0.0
02
0.0
03
0.0
01
0.0
03
0.0
01
-0.0
03
0.0
00
-0.0
01
-0.0
02
-0.0
01
0.0
07
Beau
tiful
0.3
39
0.0
03
-0.0
15
-0.0
12
-0.0
13
0.0
06
10
0.0
08
-0.0
11
0.0
08
-0.0
04
-0.0
04
0.0
01
0.0
10
0.0
00
0.0
12
-0.0
08
0.0
13
-0.0
09
0.0
11
-0.0
08
Ugly
0.2
92
-0.0
05
0.0
07
0.0
07
0.0
08
-0.0
06
01
-0.0
04
0.0
09
0.0
01
0.0
03
-0.0
03
0.0
01
-0.0
02
0.0
03
-0.0
08
0.0
04
0.0
02
0.0
04
-0.0
02
0.0
06
Of th
e united
feeling
there is
0.2
55
-0.0
03
-0.0
16
-0.0
14
-0.0
14
-0.0
02
0.0
06
-0.0
04
10
0.0
09
-0.0
04
-0.0
07
0.0
00
0.0
06
0.0
02
0.0
13
-0.0
08
0.0
18
-0.0
13
0.0
13
-0.0
12
Scattered
0.3
81
0.0
00
0.0
18
0.0
11
0.0
19
0.0
09
-0.0
12
0.0
11
01
-0.0
13
0.0
11
0.0
08
-0.0
01
-0.0
11
0.0
06
-0.0
27
0.0
14
-0.0
20
0.0
17
-0.0
19
0.0
26
Varied
0.1
75
-0.0
05
-0.0
08
-0.0
08
-0.0
07
-0.0
04
0.0
04
0.0
01
0.0
06
-0.0
06
10
-0.0
06
0.0
01
0.0
04
0.0
01
0.0
07
-0.0
05
0.0
11
-0.0
07
0.0
08
-0.0
07
Featu
reless
0.4
90
0.0
01
0.0
02
-0.0
03
0.0
06
0.0
14
-0.0
06
0.0
04
-0.0
08
0.0
14
01
0.0
04
0.0
00
-0.0
05
0.0
06
-0.0
17
0.0
08
-0.0
08
0.0
07
-0.0
09
0.0
16
New
0.1
24
0.0
04
0.0
05
0.0
00
0.0
03
0.0
04
-0.0
01
-0.0
01
-0.0
03
0.0
03
-0.0
04
0.0
01
10
-0.0
02
-0.0
02
-0.0
01
0.0
01
-0.0
07
0.0
03
-0.0
04
0.0
03
Histo
ric0.5
61
0.0
03
-0.0
04
-0.0
05
-0.0
02
0.0
09
0.0
02
0.0
02
0.0
00
-0.0
01
0.0
02
0.0
00
01
0.0
03
-0.0
01
0.0
00
-0.0
02
0.0
02
-0.0
02
0.0
02
0.0
01
Full o
f natu
re0.3
70
0.0
04
-0.0
20
-0.0
12
-0.0
15
0.0
11
0.0
10
-0.0
03
0.0
09
-0.0
11
0.0
09
-0.0
03
-0.0
06
0.0
02
10
0.0
12
-0.0
08
0.0
16
-0.0
10
0.0
12
-0.0
06
Urb
an
0.2
31
-0.0
03
-0.0
07
-0.0
08
-0.0
03
0.0
04
0.0
00
0.0
02
0.0
02
0.0
04
0.0
02
0.0
03
-0.0
04
-0.0
01
01
-0.0
03
0.0
01
0.0
08
-0.0
02
0.0
02
0.0
04
Cheerfu
l0.2
59
0.0
00
-0.0
08
-0.0
09
-0.0
15
-0.0
10
0.0
09
-0.0
06
0.0
14
-0.0
18
0.0
10
-0.0
09
-0.0
03
0.0
00
0.0
08
-0.0
03
10
0.0
18
-0.0
14
0.0
15
-0.0
20
Glo
om
y0.4
32
0.0
02
0.0
13
0.0
14
0.0
15
0.0
04
-0.0
11
0.0
06
-0.0
12
0.0
16
-0.0
12
0.0
07
0.0
03
-0.0
01
-0.0
09
0.0
03
01
-0.0
18
0.0
14
-0.0
16
0.0
19
Indiv
idualistic
0.2
38
-0.0
07
-0.0
25
-0.0
25
-0.0
20
-0.0
01
0.0
08
0.0
02
0.0
17
-0.0
12
0.0
15
-0.0
04
-0.0
12
0.0
01
0.0
10
0.0
07
0.0
16
-0.0
10
10
0.0
19
-0.0
12
Conventio
nal
0.4
38
0.0
01
0.0
33
0.0
40
0.0
28
-0.0
06
-0.0
12
0.0
07
-0.0
23
0.0
20
-0.0
16
0.0
06
0.0
08
-0.0
01
-0.0
12
-0.0
04
-0.0
24
0.0
14
01
-0.0
23
0.0
19
Frien
dly
0.4
43
-0.0
09
-0.0
30
-0.0
27
-0.0
26
-0.0
08
0.0
14
-0.0
03
0.0
22
-0.0
22
0.0
21
-0.0
08
-0.0
16
0.0
02
0.0
14
0.0
04
0.0
25
-0.0
16
0.0
36
-0.0
23
10
Unfrien
dly
0.2
36
0.0
08
0.0
06
0.0
05
0.0
09
0.0
21
-0.0
06
0.0
05
-0.0
11
0.0
16
-0.0
10
0.0
08
0.0
06
0.0
00
-0.0
04
0.0
04
-0.0
18
0.0
10
-0.0
13
0.0
10
01
Healed
0.2
85
-0.0
06
-0.0
06
-0.0
03
-0.0
10
-0.0
18
0.0
06
-0.0
02
0.0
10
-0.0
14
0.0
10
-0.0
07
-0.0
08
0.0
00
0.0
06
-0.0
02
0.0
17
-0.0
09
0.0
15
-0.0
10
0.0
12
-0.0
17
Stim
ulated
0.1
80
0.0
07
0.0
02
0.0
05
0.0
03
0.0
13
-0.0
02
0.0
03
-0.0
04
0.0
08
-0.0
05
0.0
04
0.0
02
-0.0
01
0.0
00
0.0
05
-0.0
06
0.0
06
-0.0
02
0.0
04
-0.0
06
0.0
12
Open
0.2
57
-0.0
03
-0.0
21
-0.0
17
-0.0
19
-0.0
01
0.0
10
-0.0
10
0.0
17
-0.0
19
0.0
11
-0.0
07
-0.0
08
0.0
00
0.0
09
0.0
01
0.0
20
-0.0
11
0.0
20
-0.0
17
0.0
17
-0.0
19
Exclu
sive
0.3
93
0.0
14
0.0
20
0.0
11
0.0
18
0.0
14
-0.0
11
0.0
08
-0.0
16
0.0
20
-0.0
17
0.0
08
0.0
13
0.0
00
-0.0
10
-0.0
01
-0.0
20
0.0
14
-0.0
23
0.0
15
-0.0
21
0.0
25
Wan
t to resid
e0.2
41
0.0
02
-0.0
11
-0.0
10
-0.0
11
0.0
05
0.0
05
-0.0
02
0.0
09
-0.0
08
0.0
06
-0.0
02
-0.0
04
0.0
00
0.0
06
0.0
03
0.0
11
-0.0
05
0.0
15
-0.0
09
0.0
09
-0.0
06
Do n
ot w
ant to
resid
e0.3
95
0.0
02
0.0
02
-0.0
03
0.0
05
0.0
11
-0.0
06
0.0
00
-0.0
04
0.0
10
-0.0
08
0.0
06
0.0
03
-0.0
01
-0.0
06
0.0
05
-0.0
13
0.0
09
-0.0
06
0.0
05
-0.0
08
0.0
13
Warm
0.3
98
-0.0
05
-0.0
23
-0.0
28
-0.0
23
-0.0
03
0.0
12
-0.0
05
0.0
18
-0.0
17
0.0
15
-0.0
06
-0.0
07
0.0
01
0.0
11
0.0
04
0.0
25
-0.0
14
0.0
30
-0.0
21
0.0
20
-0.0
19
Alo
of
0.2
52
0.0
02
0.0
12
0.0
17
0.0
13
-0.0
01
-0.0
08
0.0
07
-0.0
09
0.0
12
-0.0
08
0.0
04
0.0
02
0.0
00
-0.0
07
0.0
00
-0.0
15
0.0
10
-0.0
12
0.0
11
-0.0
11
0.0
13
Fascin
ating
0.2
23
-0.0
01
-0.0
18
-0.0
13
-0.0
16
0.0
00
0.0
09
-0.0
06
0.0
14
-0.0
16
0.0
10
-0.0
06
-0.0
07
0.0
01
0.0
09
0.0
00
0.0
17
-0.0
09
0.0
18
-0.0
14
0.0
15
-0.0
15
Not fascin
atin
g0.4
23
0.0
01
0.0
12
0.0
07
0.0
13
0.0
05
-0.0
08
0.0
06
-0.0
11
0.0
14
-0.0
08
0.0
06
0.0
04
-0.0
01
-0.0
07
0.0
02
-0.0
17
0.0
09
-0.0
13
0.0
10
-0.0
12
0.0
15
Wan
t to p
lay0.2
18
-0.0
01
-0.0
16
-0.0
20
-0.0
18
-0.0
01
0.0
11
-0.0
10
0.0
15
-0.0
20
0.0
11
-0.0
08
-0.0
03
0.0
01
0.0
09
-0.0
04
0.0
24
-0.0
14
0.0
18
-0.0
17
0.0
16
-0.0
22
Wan
t to ex
am
ine
delib
erately0.3
12
0.0
09
0.0
02
0.0
00
0.0
00
0.0
18
-0.0
02
-0.0
02
-0.0
03
0.0
05
-0.0
07
0.0
03
0.0
05
-0.0
01
-0.0
01
0.0
03
-0.0
05
0.0
04
-0.0
04
0.0
02
-0.0
06
0.0
10
Liv
ely
0.1
81
-0.0
03
-0.0
06
-0.0
05
-0.0
08
-0.0
08
0.0
05
-0.0
03
0.0
07
-0.0
09
0.0
07
-0.0
04
-0.0
04
0.0
00
0.0
05
-0.0
01
0.0
12
-0.0
07
0.0
09
-0.0
07
0.0
08
-0.0
11
Calm
0.5
20
0.0
10
0.0
09
0.0
07
0.0
08
0.0
18
-0.0
06
0.0
01
-0.0
09
0.0
14
-0.0
12
0.0
07
0.0
08
-0.0
01
-0.0
04
0.0
04
-0.0
13
0.0
10
-0.0
12
0.0
09
-0.0
13
0.0
19
Atm
osp
here o
f urb
an
0.0
97
-0.0
02
0.0
03
0.0
00
0.0
02
-0.0
06
-0.0
01
0.0
02
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
-0.0
02
-0.0
01
0.0
01
0.0
00
0.0
00
0.0
00
0.0
00
-0.0
01
Atm
osp
here o
f rura
l
area0.6
29
0.0
01
0.0
04
-0.0
03
0.0
06
0.0
14
-0.0
05
0.0
03
-0.0
07
0.0
12
-0.0
06
0.0
06
0.0
04
-0.0
01
-0.0
05
0.0
04
-0.0
14
0.0
07
-0.0
08
0.0
06
-0.0
08
0.0
13
Male
0.4
33
-0.0
68
0.0
52
0.1
23
0.0
59
-0.1
48
-0.0
43
0.0
11
-0.0
08
0.0
09
-0.0
16
-0.0
02
-0.0
52
-0.0
14
-0.0
41
0.0
04
-0.0
49
0.0
43
-0.0
40
0.0
44
-0.0
20
-0.0
12
Fem
ale0.5
67
0.0
68
-0.0
52
-0.1
23
-0.0
59
0.1
48
0.0
43
-0.0
11
0.0
08
-0.0
09
0.0
16
0.0
02
0.0
52
0.0
14
0.0
41
-0.0
04
0.0
49
-0.0
43
0.0
40
-0.0
44
0.0
20
0.0
12
10th
0.2
05
-0.0
33
-0.1
23
-0.1
17
-0.0
94
-0.0
09
0.0
38
-0.0
10
0.0
87
-0.0
69
0.0
68
-0.0
20
-0.0
64
0.0
03
0.0
43
0.0
30
0.0
73
-0.0
43
0.1
38
-0.0
88
0.0
90
-0.0
71
20th
0.1
66
0.0
37
0.0
53
0.0
90
0.0
02
-0.0
43
0.0
11
-0.0
30
0.0
02
-0.0
33
-0.0
18
-0.0
24
0.0
23
-0.0
07
0.0
09
-0.0
29
0.0
59
-0.0
13
-0.0
28
0.0
12
-0.0
08
-0.0
25
30th
0.2
51
-0.0
22
0.0
12
0.0
34
0.0
26
0.0
09
0.0
11
-0.0
11
-0.0
36
-0.0
04
0.0
07
-0.0
04
0.0
02
0.0
07
0.0
01
-0.0
31
-0.0
36
-0.0
09
-0.0
60
0.0
30
-0.0
06
-0.0
17
40th
0.1
70
-0.0
02
0.0
55
-0.0
31
0.0
33
-0.0
27
-0.0
40
0.0
04
-0.0
11
0.0
28
-0.0
30
0.0
09
0.0
42
-0.0
05
-0.0
51
-0.0
12
-0.0
14
0.0
16
-0.0
37
0.0
05
-0.0
35
0.0
08
50th
0.1
02
-0.0
21
0.0
39
0.0
45
0.0
35
-0.0
44
-0.0
13
0.0
58
-0.0
23
0.0
44
0.0
06
0.0
13
-0.0
13
0.0
02
-0.0
04
0.0
18
-0.0
25
0.0
16
0.0
14
0.0
26
-0.0
11
0.0
34
60th
0.0
70
0.0
08
-0.0
19
-0.0
18
-0.0
04
0.0
62
-0.0
07
-0.0
14
-0.0
14
0.0
32
-0.0
22
0.0
23
0.0
10
-0.0
07
-0.0
04
0.0
29
-0.0
39
0.0
20
-0.0
18
0.0
12
-0.0
21
0.0
45
More th
an70
0.0
35
0.0
33
-0.0
16
-0.0
03
0.0
02
0.0
50
-0.0
01
0.0
04
-0.0
05
0.0
02
-0.0
10
0.0
03
0.0
00
0.0
06
0.0
06
-0.0
05
-0.0
18
0.0
12
-0.0
10
0.0
04
-0.0
09
0.0
26
nam
e_fu
jistate
Prio
rT
he im
age o
f the su
rroundin
g a
rea at this sh
oppin
g street
Gen
der
Age
The p
urp
ose o
f
visitin
g
The im
age o
f
the su
rroundin
g
area at this
shoppin
g street
The p
urp
ose o
f visitin
g
Daisuke Suzuki et al. 63
Healed
Stim
ulated
Open
Exclu
sive
Wan
t to resid
eD
o n
ot w
ant to
reside
Warm
Alo
of
Fascin
ating
Not fascin
atin
gW
ant to
play
Wan
t to
exam
ine
delib
erately
Liv
elyC
almA
tmosp
here o
f
urb
an
Atm
osp
here o
f
rura
l areaM
aleF
emale
10th
20th
30th
40th
50th
60th
More th
an70
-0.0
05
0.0
08
-0.0
03
0.0
07
0.0
01
0.0
01
-0.0
03
0.0
01
-0.0
01
0.0
00
-0.0
02
0.0
06
-0.0
04
0.0
04
-0.0
04
0.0
00
-0.0
34
0.0
26
-0.0
35
0.0
48
-0.0
19
-0.0
03
-0.0
44
0.0
25
0.2
04
-0.0
03
0.0
01
-0.0
14
0.0
09
-0.0
09
0.0
00
-0.0
10
0.0
09
-0.0
14
0.0
05
-0.0
12
0.0
00
-0.0
05
0.0
02
0.0
05
0.0
01
0.0
21
-0.0
16
-0.1
04
0.0
55
0.0
08
0.0
56
0.0
66
-0.0
47
-0.0
79
0.0
00
0.0
02
-0.0
07
0.0
03
-0.0
05
-0.0
01
-0.0
07
0.0
07
-0.0
07
0.0
02
-0.0
09
0.0
00
-0.0
02
0.0
01
0.0
00
-0.0
01
0.0
29
-0.0
22
-0.0
59
0.0
55
0.0
14
-0.0
19
0.0
45
-0.0
26
-0.0
08
-0.0
12
0.0
05
-0.0
30
0.0
19
-0.0
19
0.0
04
-0.0
23
0.0
21
-0.0
29
0.0
12
-0.0
34
0.0
00
-0.0
16
0.0
06
0.0
08
0.0
04
0.0
54
-0.0
41
-0.1
82
0.0
06
0.0
41
0.0
78
0.1
35
-0.0
25
0.0
23
-0.0
05
0.0
05
-0.0
01
0.0
03
0.0
01
0.0
02
0.0
00
-0.0
01
0.0
01
0.0
00
0.0
00
0.0
04
-0.0
04
0.0
03
-0.0
04
0.0
01
-0.0
30
0.0
23
-0.0
04
-0.0
23
0.0
03
-0.0
14
-0.0
38
0.0
79
0.1
27
0.0
08
-0.0
04
0.0
13
-0.0
10
0.0
07
-0.0
06
0.0
10
-0.0
11
0.0
13
-0.0
07
0.0
16
-0.0
02
0.0
10
-0.0
04
-0.0
04
-0.0
03
-0.0
34
0.0
26
0.0
64
0.0
22
0.0
15
-0.0
80
-0.0
42
-0.0
34
-0.0
06
-0.0
01
0.0
05
-0.0
11
0.0
06
-0.0
03
0.0
00
-0.0
04
0.0
09
-0.0
08
0.0
04
-0.0
14
-0.0
02
-0.0
04
0.0
01
0.0
06
0.0
01
0.0
08
-0.0
06
-0.0
14
-0.0
53
-0.0
13
0.0
07
0.1
68
-0.0
59
0.0
31
0.0
09
-0.0
05
0.0
17
-0.0
10
0.0
10
-0.0
03
0.0
12
-0.0
10
0.0
15
-0.0
06
0.0
17
-0.0
02
0.0
10
-0.0
04
0.0
00
-0.0
03
-0.0
05
0.0
04
0.1
08
0.0
03
-0.0
36
-0.0
17
-0.0
58
-0.0
51
-0.0
35
-0.0
18
0.0
16
-0.0
28
0.0
19
-0.0
12
0.0
10
-0.0
17
0.0
17
-0.0
27
0.0
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0
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00
00
10
0
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45
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0.0
28
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25
0.0
24
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36
0.0
15
0.0
00
0.0
00
00
00
01
0
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15
0.0
21
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13
0.0
24
0.0
01
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09
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17
0.0
22
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01
0.0
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21
0.0
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0.0
09
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04
0.0
02
0.0
00
0.0
00
00
00
00
1
Gen
der
Age
The im
age o
f the su
rroundin
g a
rea at this sh
oppin
g street
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