Graduate Thesis Or Dissertation

 

Coincident Nodes Multi-Edge Graph for Simultaneous Decision and Objective Space Multi-Dimensional Visualization Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/qv33s285v

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  • The importance of data visualization is becoming increasingly more substantial to the field of optimization and engineering design where a carefully designed visualization of the data on decision parameters (i.e Decision Space) and performance functions (i.e Objective Space) is critical to the success of the decision making process. One of the main goals of data visualization is to unveil the different patterns, trends and relationships that the data encapsulates. However, this aforementioned goal becomes challenging when visualizing multidimensional decision and objective space data both qualitatively and quantitatively. In fact, in order to discover the patterns and inter-variable relationships that the data encapsulates, a holistic visualization approach, where all the variables are simultaneously represented, is required. However, holistically mapping multidimensional data in a single 2D visual is a challenging task that could result in a cognitively overwhelming output. Consequently, we aim to reach a balance between the desired holistic view that facilitates pattern discovery, and a clear user friendly visualization. In this thesis, we present a novel holistic visualization model for pattern discov- ery in multidimensional decision and objective space data structures and demon- strate its usage in a watershed conservation plan context. We use a coincident nodes and multi-edge network map visualization to represent users' decisions in terms of watershed conservation plan practices and goals without losing the ge- ographical knowledge provided by a map. In reality the decision and objective space are highly related. This simultaneous combination of the geographical in- formation, decision space and objective space yields to an efficient identification of existing patterns that are further validated using a set of predefined statistical methods.
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