The world faces an ever-growing deluge of data that demands new and innovative ways of thinking. Developing clear, sophisticated and accessible visualizations for existing and future data sets is a vital part of understanding and leading an increasingly data-centric world. Sustaining its mandate for global thought leadership surrounding the future of cities, and with the support of Thomson Reuters, the Graduate School of Architecture, Planning and Preservation (GSAPP) at Columbia is at the forefront of a university-wide project on the future of data visualization, the Advanced Data Visualization Project (ADVP). Hosted at GSAPP, the ADVP brings together an interdisciplinary group from within Columbia and beyond to encourage diverse thinking around all facets of data visualization. The first year of the ADVP established a platform for large-scale experiments culminating in the establishment of a formal Institute for Advanced Data Visualization. Led by Laura Kurgan (Director of the Spatial Information Design Lab at GSAPP) and David Benjamin (Director of the Living Architecture Lab at GSAPP), the ADVP initially included six interdisciplinary visualization projects as well as studio courses, events, an exhibition and a publication to engage students and the public.
In collaboration with the Lars Dietrich Lab at Columbia University
Fueled by rapid developments in technology, the main dilemma of biology has shifted from simply acquiring data to that of interpreting it in a meaningful way. Recent improvements in imaging technologies now allow biologists to capture images and videos of life that, while visually stunning on their own, are difficult to interpret as data. The goal of the ADVP is to develop new visual tools to help scientists interpret this new type of data. The project will focus on research and applications of state of the art computer vision and machine learning systems to analyze, process, interpret, and visualize a large set of experimental image data. As a result, the work will also explore a new role for visual designers in the development of machine vision and learning systems, a field which is still only in its infancy.
Project Team: David Benjamin, Project Director Danil Nagy, Research Associate and Data Visualization
Hair Physics Simulation
In collaboration with Eitan Grinspun and the Columbia Computer Graphics Group at Columbia University
Computational simulations of complex natural phenomenon have become useful not only for scientific research but also for visual design and entertainment. Key examples are the complex physics engines developed to simulate the behavior of multi-particle dynamic systems such as smoke, hair, and cloth in digital platforms like Autodesk Maya. These tools operate by generating massive amounts of data at the particle level and then calculating individual physical interactions among the particles. In this case, limiting the particle sampling rate and resolution becomes key to optimizing the simulations. The goal of the ADVP is to work with the data generated by a dynamic hair simulation, using statistical and visualization tools to discover how micro relationships among particles predict macro results. This has the potential to reveal new patterns not predicted by the researchers, and guide the future optimization of the simulation engine. Finally, it can generate new insight on the functioning of the dynamic systems themselves.
Project Team: David Benjamin, Project Director Danil Nagy, Research Associate and Data Visualization Jumping the Great Firewall: In collaboration with Penn Voices, and Mark Hansen, Brown Institute, School of Journalism.
Jumping the Great Firewall
This project is an attempt at the visualization of new phenomenon, which can be called free expression in China. The title, “Jumping the Great Firewall” derives from something that already is happening in China by the users of Weibo a Chinese, Twitter-like microblog. As we have all read on media around the world, The Internet in China is policed by something known as the Great Firewall. It is a name for a human and technological program that keeps unacceptable or “sensitive” content (words and articles about the Tiananmen Square massacre, for example), off the Chinese Internet and from the computers of those who could, potentially, create a movement inside the country. It is a system of control. Twitter and Facebook are therefore, blocked, as are many western news outlets and human rights web sites; web searches are seriously curtailed; sensitive words are blocked; and content is often removed. Those in China who wish to access blocked web sites, must do so via a Virtual Private Network, (VPN) – this is known as jumping the Great Firewall. There is a significant difference between Weibo, and Twitter: you can insert images directly into your post, without links. Images are a lot less searchable than text, which implies that content can spread more widely before it is detected. Those who know this, now take screenshots of controversial posts before they’re removed and re-post them. Visualized here are many deleted posts from two weeks in May 2013.
Project Team: Laura Kurgan: Project Director Dan Taeyong Lee: Research Associate and Data Visualization Yi Du: Research Associate School of Journalism.
Port to Port
Using data assembled by Thomson Reuters from multiple shipping organizations, we are mapping global oil shipping routes as well as other forms of energy navigating ocean territories to the United States. Using D3 as an interactive web platform we are designing an interface that can be scaled globally and locally to tell stories about energy movement from field to destination. Viewed across time changes in patterns of movement become visible as well as expose the geopolitics, price of oil, and conditions at ports change.
Project Team: Laura Kurgan, Project Director Jen Lowe, Research Associate and Data Visualization Dare Brawley, Research Assistant Scott Murray, D3
In collaboration with Alex Gill, and the Columbia Library.
Like many public libraries across the United States, University libraries are undergoing massive changes as their catalogues and parts of their collections have been digitized, and their physical collections have outgrown their physical space. Browsing the collection, now often happens online. What are the possible ways can we browse the collection and learn about ways in which networks of knowledge are created across the Columbia community? Beyond browsing the shelves which hold the books, the serendipity of finding the book on the next shelf, and the Amazon algorithm which tells you what other books buyers, “like” you have purchased – what other serendipities can be introduced into browsing? Using natural language processing, processing and D3, we are trying to discover some of these networks.
Project Team: Laura Kurgan, Project Director Jen Lowe, Research Associate and Data Visualization Derek Watkins, Research Associate and Data Visualization
Biofilm Imaging A
Hair Physics Simulation B
Jumping the Great Firewall C
Port to Port D