On one hand, the meta-language for visualization is ever being refined—for this reason PJIM is always considering taxonomy and ontology that enable improved knowledge-tool building. To that end we’ve presented a paper dealing specifically with node-and-link visualization. On the other hand? Experimentation.
With the growth and attention lavished on data visualization and information design comes the natural fallout of aesthetics for its own sake. This really takes on two forms: experimentation with technical programs and processes yielding unexpected visual outcomes; and models crafted with a bias of achieving compelling, and attractive, visual results (at the short term expense of clarity). This, despite the (professional) basis of our endeavors respecting information visualization and (as we all agree…) the aim toward that greater good of clarity, simplicity, and effective conveyance of information—be it basic instruction, patterned knowledge, or emerging intelligence.
In many of these cases the objective is in making formerly qualitative data realized through a more quantifiable way. Herein lies the opportunity: What if a “front-loaded” aesthetic and experimental approach yields the desired outcomes though the technique is unorthodox? In this spirit of the counter-intuitive, we offer two articles that deal with the “mapping of storytelling”—specifically the kinds of non storytelling that is increasingly part of the film and movie industry, where digitization and data manipulation allow for richly interwoven counter-sequential renderings.
Alternatively, is an article dealing with exceptions and anomalies, what are sometimes called, "Anomolytics." These types of media are where storytelling and big data meet. Consequently, experiments and applied graphics of this type will allow us to find answers from nature and human social structures where stories also overlap, or do not, at first glance, follow logical sequence. A case where art cannot only imitate life, but derive more knowledge from the complexities and milieu of human life.
Jihoon Kang, Publisher, and William Bevington, Editor-in-Chief
Parsons Journal for Information Mapping
by Rasagy Sharma & Venkatesh Rajamanickam
by Misha Rabinovich, MFA & Yogesh Girdhar, PhD
by William M. Bevington
Arc diagrams, interactive data visualization, movie narrative, narrative visualization, non-linear narrative;
For the process of storytelling, various narrative structures have been explored to make a simple story more interesting to the audience. A non-linear narrative is one such structure. While techniques such as in media res, flashbacks, and flash-forwards have been
used since ancient times, these techniques have been extensively used in several movies in the past two decades. Such movies keep the viewers interested even after they leave the theatre, and lead to several in-person and online discussions.
Rasagy Sharma is an Information & Interface Design post-graduate student at NID Bangalore. He is interested Data Visualization, Generative Art and allied areas in the intersection of data, art & technology.
Venkatesh Rajamanickam is currently an Associate Professor at the Industrial Design Centre, IIT Bombay. His research interests include HCI, data visualization, and technology & learning.
Collaboration, computer vision, cultural analytics, economy of abundance, interactive data visualization
This paper describes an interdisciplinary collaboration that began with creative misuse of artificial intelligence and computer vision algorithms originally developed at McGill’s Centre for Intelligent Machines. We began by analyzing image banks and video with software originally designed for surveillance and robotic anomaly detection. We started with basic visual analysis and had only limited success with movie summarization. We moved beyond misuse when the software actually became useful for film analysis with the addition of audio analysis, subtitle analysis, facial recognition, and topic modeling. Using multiple types of visualizations and a back-and-fourth workflow between people and AI we arrived at an approach for cultural analytics that can be used to review and develop film criticism. Finally, we present ways to apply these techniques to Database Cinema and other aspects of film and video creation.
Misha Rabinovich is an artist and educator working collaboratively with artists and scientists on both the east and wests coasts of North America.
Yogesh Girdhar is a data scientist and the postdoctoral scholar of applied ocean physics & engineering at Woods Hole Oceanographic Institution.
Data visualization, node-and-link, big data, social media, node, link, ground, change agent, annotation
Data visualization models that are intended to depict considerable sets of interrelated data (including systems designed to process and render big data, particularly those that must reveal unexpected correlations) and data-supported massive-communication toolsets (such as social-network media systems) increasingly rely on presentations that depict relationships through node-and-link diagrams. The challenge of combining these kinds of quantitative and qualitative datasets can be well met with node-and-link diagramming — provided an articulate and consistent modelling method is applied to the task. This paper is a primer on what node-and-link diagrams are, and what kinds naming categories may be derived and assigned in order to make node-and-link diagrams “articulate and consistent.”
This investigation was originally part of research specifically targeting healthcare issues in which the healthcare providers and healthcare recipients where considered “nodes” in the massive paradigm which is the procedural and financial realm of today’s healthcare world — a massive system. Considerations were given to multiple questions. What should constitute a “node,” what should constitute a “link” in a visualization? What other parts are there? How can these elements be placed into dynamic and uncertain context? How can the intrinsic components of such a network be re-arranged to leveraged improvements? Also, how are all the concerns of healthcare providers signified within such as system? These kinds of questions lead to the development of hundreds of “kinds” and “natures.” Ultimately a system was devised allowing for comprehensive model building. It was based on these elements: node(s), link(s), ground(s), change agent(s), and annotations. This paper provides and overview of how these five elements were considered axiomatic and the nature and variations associated to these core elements.
William M. Bevington currently serves Senior Information Theorist for PIIM. He also serves as Associate Professor of Information Mapping in the School of Art, Media, and Technology at Parsons The New School for Design, The New School, New York. He formerly served as the Executive Director for Parsons Institute of Information Mapping, Chairman of the Communication Design department at Parsons School of Design, and various professorial and instructional roles at his Alma Mater, The Cooper Union for the Advancement of Science and Art. He is an Information Designer and Information Theorist specializing in creating tools for the rapid assessment of complex data. Mr. Bevington has developed toolsets for transit systems applications, stock trading applications, and health management tools as a principal designer at Spire Integrated Design, New York.