scholarly journals Bayesian Network Analysis for the Questionnaire Investigation on the Needs at Fuji Shopping Street Town

2018 ◽  
Vol 9 (2) ◽  
pp. 46
Author(s):  
Tsuyoshi Aburai ◽  
Akane Okubo ◽  
Daisuke Suzuki ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
The Future

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 (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 we focus Fuji Shopping Street Town in this paper. These are analyzed by using Bayesian Network. 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.

scholarly journals Bayesian Network Analysis for the Questionnaire Investigation on the Needs at Yoshiwara Shopping Street in Fuji City

2018 ◽  
Vol 9 (1) ◽  
pp. 211
Author(s):  
Kazuhiro Takeyasu ◽  
Tsuyosi Aburai ◽  
Akane Okubo ◽  
Daisuke Suzuki
Keyword(s):  
Sensitivity Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
The Future

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 (two for Fuji Shopping Street and two for Yoshiwara Shopping Street). 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 Yoshiwara Shopping Street in this paper. These are analyzed by using Bayesian Network. 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.

scholarly journals Bayesian Network Analysis for the Questionnaire Investigation on the Impression at Fuji City

2018 ◽  
Vol 9 (6) ◽  
pp. 1
Author(s):  
Daisuke Suzuki ◽  
Akane Okubo ◽  
Tsuyosi Aburai ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
The Future

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. In this paper, we mainly focus the impression the visitors feel and analyze them. These are analyzed by using Bayesian Network. 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. 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.

scholarly journals Questionnaire Investigation on Tourists’ Behavior and its Sensibility Analysis Utilizing Bayesian Network

2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Akane Okubo ◽  
Tsuyosi Aburai ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Local Government ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
Izu Peninsula ◽  
Regional Collaboration ◽  
Cherry Blossom ◽  
Sensibility Analysis ◽  
Tourism Service

Tourists from abroad are increasing rapidly in Japan. Kawazu town in Izu Peninsula is famous for its cherry trees. In the cherry blossom season, many tourists visit this town. In order to get much more visitors, tourists’ behavior should be investigated much further. The Kawazu Cherry Blossom Festival was carried out in February 2015. Our research investigation was performed during that period. In this paper, a questionnaire investigation is executed in order to clarify tourists’ behavior, and to seek the possibility of developing regional collaboration among local government, tourism related industry and visitors. In this research, we construct the model utilizing Bayesian Network and causal relationship is sequentially chained by the characteristics of travelers, an objective to visit Izu Peninsula in Japan and the main occasion to visit them. We analyzed them by sensitivity analysis and some useful results were obtained. Sensitivity analysis is performed by back propagation method. We have presented the paper concerning this. But the volume becomes too large, therefore we have split them and this paper shows the latter half of the investigation result by setting evidence to Bayesian Network items. These are utilized for constructing a much more effective and useful tourism service. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

scholarly journals How Does the Location of Urban Facilities Affect the Forecasted Population Change in the Osaka Metropolitan Fringe Area?

2020 ◽  
Vol 13 (1) ◽  
pp. 110
Author(s):  
Haruka Kato
Keyword(s):  
Network Analysis ◽  
Bayesian Network ◽  
Public Housing ◽  
Causal Relationship ◽  
Population Change ◽  
Residential Areas ◽  
New Town ◽  
Location Optimization ◽  
The Future ◽  
Future Population

This study aims to clarify the statistical causal relationship between the locations of urban facilities and forecasted population changes according to types of residential clusters in the Osaka Metropolitan Fringe areas. This paper’s background is the location optimization plan policy formulated by the Japanese MLIT (Ministry of Land, Infrastructure, Transport, and Tourism) in 2015. The methods combined urban ecological analysis, cohort analysis, and Bayesian network analysis. Using the Bayesian network analysis, the causal relationship between the forecasted population change ratio and the urban facility location was analyzed. The results suggest the location of urban facilities for each residential cluster that will prevent a rapid population decline in the future. Specifically, in the sprawl cluster, this study found that residential areas closer to medical facilities will sustain the future population, while in the old new-town cluster, this study found that residential areas closer to train stations will best sustain the future population. However, in the public housing cluster, residential areas more distant from regional resources will best sustain the future population. Therefore, it is worth considering different urban designs in the old new-town and public housing clusters, rather than the location optimization plan policy.

scholarly journals Questionnaire Investigation on the Needs at Fuji City and its Sensibility Analysis Utilizing Bayesian Network

2019 ◽  
Vol 11 (2) ◽  
pp. 125
Author(s):  
Tsuyoshi Aburai ◽  
Akane Okubo ◽  
Daisuke Suzuki ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Surrounding Area ◽  
Network Sensitivity ◽  
The Future ◽  
Sensibility Analysis

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. 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. 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.

scholarly journals Effects of Innovation Education and Corporate Needs -Analysis Using Bayesian Network

2020 ◽  
Vol 13 (12) ◽  
pp. 51
Author(s):  
Tsuyoshi Aburai ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Statistical Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Model ◽  
Odds Ratio ◽  
Skill Assessment ◽  
Bayesian Network Model ◽  
Innovation In Education ◽  
Basic Statistical Analysis

This paper offers a clarification of the skills required for innovation talent by comparing the effect of innovation in education at Tokushima University and the talent requirement of companies. The researchers performed the questionnaire investigation with the use of the 19 items of The Innovator’s DNA Skill Assessment. Both the basic statistical analysis and Bayesian Network analysis were conducted based on the resulting data. The sensitivity analysis was performed after building the Bayesian Network Model. The evidences are set to “skeptical thinking”, “taking risks”, and “creativity” in the item of mind. The calculation of the odds ratio reveals that enhancing the Observation skill and Skill to Plan and Design is effective in improving skeptical thinking and creativity.

How influential factors affect aviation networks: A Bayesian network analysis

2021 ◽  
Vol 91 ◽  
pp. 101995
Author(s):  
Yue Wang ◽  
Collin Wai Hung Wong ◽  
Tommy King-Yin Cheung ◽  
Edmund Yangming Wu
Keyword(s):  
Network Analysis ◽  
Bayesian Network ◽  
Influential Factors

Perception and Reality—Economic Inequality as a Driver of Populism?

2019 ◽  
Vol 41 (2) ◽  
pp. 337-358 ◽  
Author(s):  
Michael Hüther ◽  
Matthias Diermeier
Keyword(s):  
Economic Development ◽  
Income Distribution ◽  
Welfare State ◽  
Causal Relationship ◽  
Economic Inequality ◽  
Public Officials ◽  
Middle Classes ◽  
The Future ◽  
General Criticism ◽  
The Welfare State

Abstract Can the rise of populism be explained by the growing chasm between rich and poor? With regard to Germany, such a causal relationship must be rejected. Income distribution in Germany has been very stable since 2005, and people’s knowledge on actual inequality and economic development is limited: inequality and unemployment are massively overestimated. At the same time, a persistently isolationist and xenophobic group with diverse concerns and preferences has emerged within the middle classes of society that riggers support for populist parties. This mood is based on welfare chauvinism against immigration rather than on a general criticism of distribution. Since the immigration of recent years will inevitably affect the relevant indicators concerning distribution, an open, cautious but less heated approach is needed in the debate on the future of the welfare state. In order to address and take the local concerns of citizens seriously, an increased exchange with public officials on the ground is needed.

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

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