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A Comparison of Fuzzy Models in Similarity Assessment of Misregistered Area Class Maps

Abstract Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggrega... (more)
Created Date 2010
Contributor Brown, Scott Benjamin (Author) / Wentz, Elizabeth A. (Advisor) / Myint, Soe W. (Committee member) / Anderson, Sharolyn (Committee member) / Arizona State University (Publisher)
Subject Geography / Remote Sensing / Geographic Information Science and Systems / Land Use and Land Cover Change / Spatial Analysis & Modeling
Type Masters Thesis
Extent 53 pages
Language English
Reuse Permissions All Rights Reserved
Note M.A. Geography 2010
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis