Need identification and sensitivity analysis of consumers ... 8_4_2.pdf · Journal of Computations & Modelling, vol.8, no.4, 2018, 29-63 ISSN: 1792-7625 (print), 1792-8850 (online)

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.


(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%


43.3%

56.7%

0

50

100

150

200

250

300

350

Male Female


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%


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.



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%


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%,


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%


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


(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%


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%


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

http://www.msi.co.jp/BAYONET/


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


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 ＋


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 ＋


street as “Ugly” had come by an age of “50th“ or ” More than 70 “.


(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


Business －


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 ＋＋


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.


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 ＋＋




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 －－


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 －


Want to play －

Lively －

Age: 10th －

Age: 20th －－

Age: 40th ＋

Age: 50th ＋＋

Age: 60th ＋＋



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 ＋


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.


Table 9: Setting evidence to “Historic” case

Male －

Female ＋

Age: 20th －

Age: 30th ＋

Age: 40th －

Age: 50th ＋

Age: 60th －－



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


Business －



Beautiful ＋

Cheerful ＋

Individualistic ＋

Conventional －

Friendly ＋

Open ＋

Exclusive －

Warm ＋

Fascinating ＋

Want to play ＋

Lively ＋

Male －

Female ＋

Age: 10th ＋＋


Age: 20th ＋

Age: 40th －－

Age: 50th －

Age: 60th －



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 －


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


Leisure, amusement －

Of the united feeling there is ＋

Scattered －

Varied ＋


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 －－



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.


Table 13: Setting evidence to “Gloomy” case


Business ＋


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 ＋＋



street as “Gloomy” had come with the purpose of visiting for“Eating and drinking”,


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.

(13) Setting evidence to “Individualistic”

After setting evidence to “Individualistic”, the result is exhibited in Table 14.

Table 14: Setting evidence to “Individualistic” case


Business －



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 －－




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


Business ＋


Beautiful －


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 －


Age: 10th －－

Age: 20th ＋

Age: 30th ＋＋

Age: 40th ＋

Age: 50th ＋＋

Age: 60th ＋＋



street as “Conventional” had come with the purpose of visiting for“Eating and drinking”,


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


Business －


Beautiful ＋


Scattered －

Varied ＋

New －

Full of nature ＋

Cheerful ＋

Gloomy －

Individualistic ＋

Conventional －

Healed ＋

Stimulated －

Open ＋

Exclusive －


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 －－



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



Scattered ＋

Varied －

Cheerful －

Individualistic －


Conventional ＋

Healed －

Stimulated ＋

Open －

Exclusive ＋

Warm －

Aloof ＋

Fascinating －

Want to play －

Lively －

Age: 10th －－

Age: 20th －

Age: 30th －

Age: 40th ＋

Age: 50th ＋＋

Age: 60th ＋＋



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”.



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”.


















street as “Gloomy” had come with the purpose of visiting for“Eating and drinking”,


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.







“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.























“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


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

https://www.jnto.go.jp/eng/location/rtg/pdf/pg-410.pdf#search='Izupeninsula

http://www.kawazu-onsen.com/eng/


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


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［］


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

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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

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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

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ine

delib

erately0

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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

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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

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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


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

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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


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


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

13

-0.0

35

0.0

07

-0.0

20

0.0

10

0.0

01

0.0

07

0.0

08

-0.0

06

-0.1

27

-0.0

76

-0.0

07

0.0

62

0.1

65

0.1

76

0.0

18

0.0

06

-0.0

05

0.0

08

-0.0

07

0.0

04

-0.0

03

0.0

07

-0.0

06

0.0

08

-0.0

03

0.0

09

-0.0

04

0.0

06

-0.0

04

0.0

01

-0.0

02

-0.0

07

0.0

05

0.0

58

-0.0

19

0.0

05

-0.0

31

0.0

11

-0.0

55

-0.0

52

-0.0

12

0.0

11

-0.0

13

0.0

10

-0.0

05

0.0

07

-0.0

08

0.0

08

-0.0

13

0.0

07

-0.0

19

0.0

05

-0.0

12

0.0

06

-0.0

02

0.0

05

-0.0

02

0.0

02

-0.0

49

-0.0

70

-0.0

07

0.0

26

0.0

62

0.1

61

0.0

43

-0.0

03

0.0

01

-0.0

04

0.0

04

-0.0

02

0.0

01

-0.0

02

0.0

01

-0.0

04

0.0

01

-0.0

01

0.0

02

-0.0

03

0.0

02

0.0

00

0.0

01

-0.0

15

0.0

11

-0.0

39

0.0

17

0.0

01

0.0

31

-0.0

15

0.0

18

0.0

00

0.0

00

-0.0

02

0.0

00

0.0

00

0.0

00

-0.0

01

0.0

01

-0.0

01

0.0

02

-0.0

01

0.0

02

-0.0

02

0.0

00

-0.0

01

0.0

01

-0.0

01

-0.0

18

0.0

14

0.0

07

-0.0

24

0.0

17

-0.0

15

0.0

13

-0.0

52

0.0

95

0.0

08

0.0

00

0.0

13

-0.0

10

0.0

09

-0.0

06

0.0

10

-0.0

09

0.0

14

-0.0

07

0.0

15

-0.0

01

0.0

10

-0.0

03

-0.0

06

-0.0

03

-0.0

35

0.0

27

0.0

77

0.0

20

0.0

02

-0.1

10

-0.0

16

-0.0

21

0.0

60

-0.0

02

0.0

06

0.0

01

-0.0

01

0.0

03

0.0

03

0.0

02

0.0

00

0.0

00

0.0

01

-0.0

04

0.0

02

-0.0

01

0.0

02

-0.0

03

0.0

02

0.0

02

-0.0

01

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0.0

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01

00

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18

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1

Gen

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Age

The im

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Documents

Need identification and sensitivity analysis of consumers ... 8_4_2.pdf · Journal of Computations & Modelling, vol.8, no.4, 2018, 29-63 ISSN: 1792-7625 (print), 1792-8850 (online)