Bayesian Network Analysis for the Questionnaire Investigation on the Needs at Fuji Shopping Street Town under the View Point of Service Engineering

Shopping streets at local city in Japan became old and are generally declining. In this paper, the area rebirth and/or regional revitalization of shopping street are handled. Fuji city in Japan is focused. Four big festivals are held at Fuji city (two for Fuji Shopping Street Town and two for Yoshiwara Shopping Street Town). 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 Town and Yoshiwara Shopping Street Town. Therefore Fuji Shopping Street Town is focused in this paper. These are analyzed by using Bayesian Network. These are analyzed by sensitivity analysis and odds ratio is calculated to the results of sensitivity analysis in order to obtain much clearer results. 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. Fruitful results are obtained. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated. Keywords—Fuji city; area rebirth; regional vitalization; Bayesian network; back propagation; service engineering

There are many papers published concerning area rebirth or regional revitalization. Author in [1] has pointed out the importance of tourism promotion. Author in [2] developed the project of shutter art to Wakkanai Chuo shopping street in Hokkaido, Japan. Author in [3] 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.
Author in [4] 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. Author in [5] analyzed the image of the Izu Peninsula as a tourist destination in their 2003 study "Questionnaire Survey on the Izu Peninsula." Author in [6] conducted tourist behavior studies in Atami city in 2008,2009,2014 and in other years.
In this paper, the area rebirth and/or regional revitalization of shopping street are handled. Fuji city in Japan is focused. Fuji city is located in Shizuoka Prefecture. Mt. Fuji is very famous all around the world and its beautiful scenery from Fuji city can be seen, which is at the foot of Mt. Fuji. There are two big shopping streets 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:  Investigation on the assets which are not in active use  Questionnaire Investigation to Entrepreneur  Questionnaire Investigation to the residents and visitors After that, area rebirth and regional revitalization plan were built.
In this paper, above stated C is handled.
Four big festivals are held at Fuji city. Two big festivals are held at Yoshiwara Shopping Street Town and two big festivals at Fuji Shopping Street Town.
At Yoshiwara Shopping Street Town, Yoshiwara Gion Festival is carried out during June and Yoshiwara Shukuba (post-town) Festival is held during October. On the other hand,  It is a probability model having a network structure.
Related items are connected with directional link. Therefore, understanding becomes easy by its visual chart.
The field of service marketing generally handles the shapeless.
Therefore it is often the case that it is hard to catch the influence to consumers.
Bayesian Network analysis enables to visualize the relationship and/or influence of shapeless products to consumers which is the field of service marketing.
These are also applied to service engineering.
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 Town and Yoshiwara Shopping Street Town. Therefore Fuji Shopping Street Town is focused in this paper. These are analyzed by using Bayesian Network. These are analyzed by sensitivity analysis and odds ratio is calculated to the results of sensitivity analysis in order to obtain much clearer results. 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. Conclusion is stated in Section 5.

A. 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

III. BAYESIAN NETWORK ANALYSIS
In constructing Bayesian Network, it is required to check the causal relationship among groups of items.
BAYONET software (http://www.msi.co.jp/BAYONET/) is used. 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 Fig. 8 are exhibited in Table I. In the next section, sensitivity analysis is achieved by back propagation method. Back propagation method is conducted in the following method (Fig. 9).

IV. SENSITIVITY ANALYSIS
Now, posterior probability is calculated by setting evidence as, for example, 1.0. Comparing Prior probability and Posterior probability, the change can be seen and the preference or image of the surrounding area at this shopping street can be confirmed. Evidence is set to all parameters. Therefore the analysis volume becomes too large. In this paper, nearly 1/3 of the total cases are picked up and analysis is executed. Nodes that are analyzed here are "Gender", "Age" and "The purpose of visiting". Another paper for the rest of them is prepared.
As stated above, evidence is set to each parameter, and the calculated posterior probability is exhibited in Appendix 2 which includes the calculation results of odds ratio.
Here, each item is classified by the strength of the odds ratio.
 Very Strong (+++): Select major parameter of which the odds ratio is more than 1.6  Strong (++): Select major parameter of which the odds ratio is more than 1.3  Medium (+): Select major parameter of which the odds ratio is more than 1.08 Now each of them is examined for Very Strong, Strong and Medium case.
A. Sensitivity Analysis for "The Purpose of Visiting" 1) Setting evidence to "Shopping": After setting evidence to "Shopping", the result is exhibited in Table II. Those who visit for "Shopping" had come with the purpose of visiting for "Leisure, amusement" of an age of "20 th ", "60 th " or "More than 70" in which the gender is "Female".
(Very Strong part is indicated by bold character and Strong is indicated by italic.) 2) Setting evidence to "Eating and drinking": After setting evidence to "Eating and drinking", the result is exhibited in Table III. Those who visit for "Eating and drinking" had come with the purpose of visiting for "Business", "Celebration、event" under the image of the surrounding area at this shopping street as "Scattered", "Conventional" or "Exclusive" of an age of "20th", "40th" or "50th" in which the gender is "Male".  3) Setting evidence to "Business": After setting evidence to "Business", the result is exhibited in Table IV. Those who visit for "Business" had come with the purpose of visiting for "Eating and drinking", "Celebration 、 event" under the image of the surrounding area at this shopping street as "Conventional" or "Aloof" of an age of "20th", "30 th " or "50th" in which the gender is "Male". 4) Setting evidence to "Celebration 、 event": After setting evidence to "Celebration 、 event", the result is exhibited in Table V. Those who visit for "Celebration、event" had come with the purpose of visiting for "Eating and drinking", "Business" under the image of the surrounding area at this shopping street as "Scattered", "Conventional" or "Exclusive" of an age of "30th", "40th" or "50th" in which the gender is "Male". Those who visit for "Leisure, amusement" had come with the purpose of visiting for "Shopping" under the image of the surrounding area at this shopping street as "Unfriendly" of an age of "60th" or "More than 70 "in which the gender is "Female".
B. Sensitivity Analysis for "Gender" 1) Setting Evidence to "Male": After setting evidence to "Male", the result is exhibited in Table VII. Those who are "Male" 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 "Gloomy", "Conventional" or "Aloof".
2) Setting Evidence to "Female": After setting evidence to "Female", the result is exhibited in Table VIII.
Those who are "Female" had come with the purpose of visiting for "Shopping", or "Leisure, amusement" under the image of the surrounding area at this shopping street as "Beautiful", "New", "Full of nature", "Cheerful", "Individualistic", "Warm" or "Want to play". 1) Setting evidence to "10th": After setting evidence to "10th", the result is exhibited in Table IX. Those who are at the age of "10th" 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", "Urban", "Cheerful", "Individualistic", "Friendly", www.ijacsa.thesai.org "Healed", "Open", "Want to reside", "Warm", "Fascinating", "Want to play" or "Lively".
2) Setting evidence to "20th": After setting evidence to "20th", the result is exhibited in Table X. Those who are at the age of "20th" had come with the purpose of visiting for "Shopping", "Eating and drinking" or "Business" under the image of the surrounding area at this shopping street as "Beautiful", "New", "Full of nature", "Cheerful", "Conventional", "Healed", "Stimulated", "Open", "Want to reside", " Fascinating", "Want to play", "Want to examine deliberately" or "Lively". 3) Setting evidence to "30th": After setting evidence to "30th", the result is exhibited in Table XI. Those who are at the age of "30th" had come with the purpose of visiting for "Business" or "Celebration 、 event" under the image of the surrounding area at this shopping street as "Conventional" or "Want to play". 4) Setting evidence to "40 th ": After setting evidence to "40 th ", the result is exhibited in Table XII. Those who are at the age of "40th" had come with the purpose of visiting for "Eating and drinking" or "Celebration 、 event" under the image of the surrounding area at this shopping street as "Scattered", "Featureless", "New", "Gloomy", "Exclusive", "Do not want to reside", "Aloof", "Not fascinating", "Calm", "Atmosphere of urban" or "Atmosphere of rural area".

V. CONCLUSION
Shopping streets at local city in Japan became old and are generally declining. In this paper, the area rebirth and/or regional revitalization of shopping street are handled. Fuji city in Japan is focused. Four big festivals are held at Fuji city (two for Fuji Shopping Street Town and two for Yoshiwara Shopping Street Town). Many people visit these festivals including residents in that area. There is a big difference between Fuji Shopping Street Town and Yoshiwara Shopping Street Town. Therefore Fuji Shopping Street Town is focused in this paper. 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. 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. This is really a quite new approach in this field and there is no related paper on this theme as far as searched.
In the Bayesian Network Analysis, model was built under the examination of the causal relationship among items. These are analyzed by sensitivity analysis and odds ratio is calculated to the results of sensitivity analysis in order to obtain much clearer results. The main result of sensitivity analysis is as follows. www.ijacsa.thesai.org Those who visit for "Business" had come with the purpose of visiting for "Eating and drinking", "Celebration、event" under the image of the surrounding area at this shopping street as "Conventional" or "Aloof" of an age of "20th", "30th" or "50th" in which the gender is "Male".
Those who are "Male" 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 "Gloomy", "Conventional" or "Aloof".
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 researches, the fruitful results could be obtained. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.