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

Persistent Link:
http://hdl.handle.net/10150/278676
Title:
Vista scenic beauty estimation model: An application of integrating neural net and geographic information system
Author:
Yuan, Yulan
Issue Date:
1998
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)
Keywords:
Landscape Architecture.; Environmental Sciences.; Artificial Intelligence.; Urban and Regional Planning.
Degree Name:
M.L.A.
Degree Level:
masters
Degree Program:
Graduate College; Renewable Natural Resources
Degree Grantor:
University of Arizona
Advisor:
Gimblett, Randy H.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleVista scenic beauty estimation model: An application of integrating neural net and geographic information systemen_US
dc.creatorYuan, Yulanen_US
dc.contributor.authorYuan, Yulanen_US
dc.date.issued1998en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © 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.en_US
dc.description.abstractThere 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.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectLandscape Architecture.en_US
dc.subjectEnvironmental Sciences.en_US
dc.subjectArtificial Intelligence.en_US
dc.subjectUrban and Regional Planning.en_US
thesis.degree.nameM.L.A.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineRenewable Natural Resourcesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGimblett, Randy H.en_US
dc.identifier.proquest1391717en_US
dc.identifier.bibrecord.b38868271en_US
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