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    Vista scenic beauty estimation model: An application of integrating neural net and geographic information system

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    Author
    Yuan, Yulan
    Issue Date
    1998
    Keywords
    Landscape Architecture.
    Environmental Sciences.
    Artificial Intelligence.
    Urban and Regional Planning.
    Advisor
    Gimblett, Randy H.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    There are some issues that have to be addressed for further understanding and improving scenic beauty management. First, the conventional model, preference rating based on fixed scene and direction, may not sufficiently reflect the reality of visual experience. Rather, visual and scenic preference is construed of a spatial experience. Second, the predictors are chosen based on measuring the composition of landscape features shown in the image. The measurement may not necessarily represent the contents of the physical environment. Third, judgements of scenic preference are complicated tasks. Simple linear regression analysis, with limited degree of freedom and some statistical constraints, may not represent the complexity of human judgments. An integrated model was developed by integrating the Scenic Beauty Estimation (SBE) model (Terry, 1976), the geographic information system (GIS) and, the artificial neural network (ANN). The results suggested the integrated model might be utilized as an automatic scenic preference mechanism for policy making. Implications for future research are also suggested.
    Type
    text
    Thesis-Reproduction (electronic)
    Degree Name
    M.L.A.
    Degree Level
    masters
    Degree Program
    Graduate College
    Renewable Natural Resources
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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