Cultural Analytics Research Environment
Interface designs for Cultural Analytics research environment. Concept: Lev Manovich and jeremy Douglass. Graphic design: Sergie Magdalin and Bob Li (undergraduate students, Visual Arts Department, UCSD.) produced: April 2008.
In April 2008 we have been awarded Interdisciplinary Collaboratory Grant from UCSD Chancellor office to begin working on a project "Visualizing Cultural Patterns."
The following researchers are involved in the project:
Lev Manovich (Visual Arts);
Noah Wardrip-Fruin (Communication);
Falko Kuester (Calit2 and Structural Engineering);
Jim Hollan (Cognitive Science);
Jeremy Douglass (Software Studies).
Calit2 article about this project: [@UCSD News][@Calit2]
Digitization of media collections, the development of Web 2.0 and the rapid growth of social media have created unique opportunities to studying social and cultural processes in new ways. For the first time in human history, we have access to large amounts of data about people’s cultural behavior and preferences as well as cultural assets themselves in digital form. A growing number of researchers have already started to take advantage of these opportunities. We propose to extend this work in new directions by taking advantage of the unique combination of expertise by members of our team, which come from the departments of Visual Arts, Communication, Cognitive Science, and Structural Engineering.
Contemporary science increasingly relies on computer-based analysis and visualization of large data sets and data flows. This approach has already yielded significant advances in many fields such as astronomy, geology, genetics, and linguistics. Its success is reflected in the National Science Foundation’s Cyberinfrastructure Vision for 21st Century Discovery document (2006) that emphasizes the development of tools for the collection, storage, analysis, and visualization of large data sets.
The joint availability of (a) large cultural data sets (through the Web and digitization efforts by museums and libraries) and (b) tools already employed in the sciences to analyze big data makes feasible a new methodology for the study of cultural processes and artifacts. If humanities have typically relied on the manual analysis of a small number of cultural objects, we can now create information visualizations of large cultural data sets to discover patterns that have not been visible previously. Some initial work has already been undertaken in this area. However, it is limited by its relative lack of interdisciplinarity. We believe that here at UCSD we can make field-defining progress in this area by bringing together people who study and create digital cultural artifacts, people who study distributed human cognition, and people who are developing computational tools for analysis, display, and interaction with large data sets.
Out team will create new kinds of multi-modal interfaces appropriate for the study and experience of large sets of cultural artifacts in different media. We will also bring together the visualization techniques normally used in science with the techniques developed in digital design and new media art. The practical outcome of our research will be Cultural Analytics Research Environment: an open platform which supports an analysis of different types of visual and media data and a variety of visualization and mapping techniques. To demonstrate the use of our approach, we will produce interactive visualizations of cultural flows, patterns, and relationships based on the analysis of large sets of data comparable in size to data sets used in sciences. We believe that such visualization environments will be used by a range of people – social scientists and cultural theorists who professionally study culture, students in art history, media studies, and communication studies classes, museum visitors, and cultural creators who want to better understand how their work fits within a larger context.
Key differences between existing work in culture visualization and our approach:
1. The projects created so far are typically driven by the available data rather than by theories of cultural and social processes. We believe that significant results will only be achieved in the context of research questions from the humanities, social sciences and culture industries combined with the advances in information visualization and interaction design.
2. Existing cultural visualizations typically use relatively small data sets. We want to develop new visualizations and interaction techniques appropriate for much larger data sets. The limitations of existing visualizations are, in part, an outgrowth of the fact that many are designed for the web, limiting how much information they can show legibly. We will use new wall-size displays with a resolution in hundreds of megapixels to display more information and also to enable new modes of interaction (such as HIperWall and HIperSpace created at Calit2). This will also allow us to address the challenge of analyzing and visualizing data-intensive types of cultural media that so far have not been approached - specifically, feature films, video, computer games, and architecture.
3. The existing work in visualization of media data relies exclusively on existing metadata (such as Flickr community-contributed tags). In contrast, our methodology calls for the computational analysis of cultural objects (e.g., image feature extraction, segmentation, clustering, etc.) to generate new kinds of metadata.
4. While existing projects typically show a single visualization, we will create Cultural Analytics research environment which will allow the user to work with different kinds of data and media all shown together: original cultural objects/conversations, cultural patterns over space and time, statistical results, etc.