| Project/Area Number |
10490021
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
広領域
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| Research Institution | Tokyo Metropolitan University |
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Principal Investigator |
HAGIHARA Kiyoko Tokyo Metropolitan University, Graduate School of Urban Science, Professor, 都市科学研究科, 教授 (00198649)
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| Co-Investigator(Kenkyū-buntansha) |
ZHANG Shaoping Meijo University, Faculty of Urban Science, Associate Professor, 都市情報学部, 助教授 (90278333)
HAGIHARA Yoshimi Kyoto University, Disaster Prevention Research Institute, Professor, 防災研究所, 教授 (00268567)
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| Project Period (FY) |
1998 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥5,800,000 (Direct Cost: ¥5,800,000)
Fiscal Year 2000: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1999: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1998: ¥3,400,000 (Direct Cost: ¥3,400,000)
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| Keywords | 非集計行動モデル / 経済学的水環境評価 / 認識データ / 潜在変数 / 顕示選好(RP)データ / 表明選好(SP)データ / RP・SP同時利用モデル / 表面選好データ / 利用価値 |
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| Research Abstract |
The purpose of the project is to propose a planning method of waterside with residents' participation by connecting the preferences of the residents and features of the plan with the aid of the discrete choice model. Environmental values are divided into use value and non-use value. In this study, only the use value is considered, for our research is focused on the urban waterside that is located very near our residence.
Firstly, structure of the disaggregate model is examined with the information from a questionnaire to the residents who live around the waterside in question. Then, the value of the waterside is derived by using a utility function which include some features of the waterside and other features about the residents, for example, the distance between the waterside and their house. As the features of the waterside, the cognizant data is used. In order to get these data we asked the residents to rank many kind of features of the waterside near their houses, for example, the quality of water, number of fishes, volume of green, and so on. These were expressed as five ranks from worse to excellent.
Secondly, the structure of residents' cognition is examined and latent variables are derived that play a role to combine the people' s ambiguous inspiration with concrete features of the waterside. By using the latent variables, we can more information for planning the waterside.
Thirdly, both the revealed preference data and stated preference data are jointly used to estimate the model. Then, more features that are statistically significant are able to be included in the model than the estimation that used only the revealed preference data. Thus, we could have more information on planning the urban waterside.
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