Non-Photorealistic Rendering in Visual Analytics
Abstract: Hand-drawn images have been used to inspire and inform humans for centuries. From cave paintings to Leonardo da Vinci's scientific abstractions to modern medical illustrations, art has been used to convey knowledge and information. Recently, the visualization community has begun exploring the principles used in such hand-drawn imagery, developing artistically inspired focus+context techniques and data abstractions and illustrations. Such work has gained ground as data has become larger and more complex as researchers focus on the development of illustration inspired visualization techniques for abstracting data to prevent visual overload while conveying complex structures in an easily understandable form. This lecture will explore the space of non-photo realistic rendering (NPR) and present new techniques in geographical visualization inspired by NPR imagery. Multivariate visual aesthetics and typography inspired cartogram algorithms will be described followed by a discussion on potential future directions of NPR inspired visual analytics.
Bio: Ross Maciejewski is currently an Assistant Professor at Arizona State University in the School of Computing, Informatics & Decision Systems Engineering. Prior to this, he served as a Visiting Assistant Professor at Purdue University and worked at the Department of Homeland Security Center of Excellence for Command Control and Interoperability in the Visual Analytics for Command, Control, and Interoperability Environments (VACCINE) group. His interests lie in the realms of data exploration and visual analysis, focusing on geographic visualization, non-photorealistic rendering, predictive analytics, modelling and simulation. For more information, visit his website at http://rmaciejewski.faculty.asu.edu or contact him at firstname.lastname@example.org.