Selecting Optimal Residential Locations Using Fuzzy GIS Modeling

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Description

Integrating decision analytical techniques in geographic information systems (GIS) can help remove the two primary obstacles in spatial decision making: inaccessibility to required geographic data and difficulties in synthesizing various criteria. I developed a GIS model to assist people seeking optimal residential locations. Fuzzy set theory was used to codify criteria for each factor used in evaluating residential locations, and weighted linear combination (WLC) was employed to simulate users' preferences in decision making. Three examples were used to demonstrate the applications in the study area. The results from the examples were analyzed. The model and the ArcGIS Extension can be ... continued below

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Tang, Zongpei December 2006.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 206 times . More information about this thesis can be viewed below.

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  • Tang, Zongpei

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Integrating decision analytical techniques in geographic information systems (GIS) can help remove the two primary obstacles in spatial decision making: inaccessibility to required geographic data and difficulties in synthesizing various criteria. I developed a GIS model to assist people seeking optimal residential locations. Fuzzy set theory was used to codify criteria for each factor used in evaluating residential locations, and weighted linear combination (WLC) was employed to simulate users' preferences in decision making. Three examples were used to demonstrate the applications in the study area. The results from the examples were analyzed. The model and the ArcGIS Extension can be used in other geographic areas for residential location selection, or in other applications of spatial decision making.

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  • December 2006

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  • May 5, 2008, 3:10 p.m.

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  • Dec. 12, 2013, 3:10 p.m.

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Tang, Zongpei. Selecting Optimal Residential Locations Using Fuzzy GIS Modeling, thesis, December 2006; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc5396/: accessed February 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .