A Snapshot of Current Trends in Visualization
Visualization is the study of the transformation of data to visual representations. These visual elements are then used to gain insight into and from the data. In the 30 years since the landmark “Visualization in Scientific Computing” report in which the National Science Foundation Panel on Graphics, Image Processing, and Workstations outlined a vision for developing computer-generated visualization as a scientific field, the field has expanded to encompass three major subfields: scientific visualization, information visualization, and visual analytics. It also includes many domain-specific areas, such as geo-information visualization, biological data visualization, and software visualization.
This February 2017 Computing Now theme presents the highlights of last year’s IEEE VIS, a flagship multi-conference in the visualization field. The five featured articles represent the best visualization research from 2016. A related video from Kitware discusses challenges in scientific visualization software development. Together, they showcase novel algorithms, the latest visualization system additions and extensions, exciting advancements in visualization theory, and significant real-world applications.
Held in Baltimore in October, IEEE VIS 2016 consisted of the following conferences:
The week-long event leveraged a close partnership with IEEE Transactions on Visualization and Computer Graphics (TVCG) and IEEE Computer Graphics & Applications(CG&A); authors of 33 articles in these two publications presented their work, and 100 high-quality papers were accepted and published directly in TVCG.
The first three articles were published in TVCGand received the best paper awards during IEEE VIS 2016. The final two are inspirational articles from CG&A.
In “An Analysis of Machine- and Human-Analytics in Classification,” Gary K.L. Tam, Vivek Kothari, and Min Chen present an intriguing theoretic analysis of two case studies in which classification models developed with human soft knowledge performed better than models derived entirely from automated machine-learning. Using information-theoretic measurement, they estimate the quantities (in bits) of the human soft knowledge that were available using visual analytics in the model development processes. They conclude that developers should not ignore such soft knowledge, because it provides an abundance of extra information.
Extracting topological structures from data provides mathematical abstractions that enable advanced data analysis, exploration, and visualization. In “Jacobi Fiber Surfaces for Bivariate Reeb Space Computation,” Julien Tierny and Hamish Carr describe a novel algorithm for computing such a topological abstraction, called the Reeb space. Whereas traditional topology-based methods in visualization deal with data defined with univariate scalar functions, this algorithm enables a practical extension to bivariate datasets.
