Visualizing and Modeling Spatial Data Uncertainty

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dc.contributor.ORCID0000-0002-5446-1668 (Koo, H)
dc.contributor.advisorChun, Yongwan
dc.creatorKoo, Hyeongmo
dc.date.accessioned2019-06-15T00:23:47Z
dc.date.available2019-06-15T00:23:47Z
dc.date.created2018-05
dc.date.issued2018-05
dc.date.submittedMay 2018
dc.date.updated2019-06-15T00:25:57Z
dc.description.abstractThis dissertation extends the understanding of spatial data uncertainty, which inevitably exists in any process of Geographic Information Sciences involving measuring, representing, and modeling the world. This dissertation consists of three specific sub-topics in visualizing and modeling spatial data uncertainty. First, a framework for attribute uncertainty visualization is suggested based on bivariate mapping techniques, and this framework is implemented in a popular GIS environment. The framework and implementation support many visual variables that have been investigated in the literature. This research outcome can provide flexibility to enhance communication and visualization effectiveness for uncertainty visualization. The second sub-topic is a development of optimal map classification methods by simultaneously considering attribute estimates and their uncertainty. This study expands the discussion of constructing an optimal map classification result in which data uncertainty is incorporated in a map classification process. This method utilizes a shortest path problem in an acyclic network based on dissimilarity measures with various cost and objective functions. Finally, modeling positional uncertainty acquired through street geocoding is investigated to understand potential factors of the uncertainty and then to identify impacts of the uncertainty on spatial analysis results. This study accounts for spatial autocorrelation among geocoded points in a modeling process, which has been barely included in this type of modeling. This research has contributions to increasing explanation and to extending geocoding uncertainty modeling by suggesting additional covariates and considering spatial autocorrelation.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/6593
dc.language.isoen
dc.rights©2018 Hyeongmo Koo
dc.subjectUncertainty (Information theory)
dc.subjectVisualization
dc.subjectSpatial data infrastructures
dc.subjectUncertainty—Mathematical models
dc.subjectAutocorrelation (Statistics)
dc.titleVisualizing and Modeling Spatial Data Uncertainty
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentGeospatial Information Sciences
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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