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Exploring Text Entities with Descriptive Non-photorealistic Rendering


Daniel Chang and Christopher Collins

We present a novel approach to text visualization called descriptive non-photorealistic rendering which exploits the inherent spatial and abstract dimensions in text documents to integrate 3D non-photorealistic rendering with information visualization.  The visualization encodes text data onto 3D models, emphasizing the relative significance of words in the text and the physical, real-world relationships between those words. Analytic exploration is supported through a collection of interactive widgets and direct multitouch interaction with the 3D models.  We applied our method to analyze a collection of vehicle complaint reports from the National Highway Traffic Safety Administration (NHTSA), and through a qualitative evaluation study, we demonstrate how our system can support tasks such as comparing the reliability of different makes and models, finding interesting facts, and revealing possible causal relations between car parts.


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