Presented by Math for America and the Science Education Initiative
The Intersection of Data and Design: Mark Hansen
Posted July 01, 2011
Data are as ubiquitous as the air we breathe, and the tools for their collection and analysis are changing almost every aspect of our lives. In terms of education, every field of study will have or has had its own "data moment." From the humanities and the arts to design and architecture, academic disciplines are examining and developing new data-oriented practices. The idea of data as a ubiquitous material offers teachers incredible opportunities for creating lessons that cross the traditional data disciplines; statistics, computer science, mathematics.
On May 19, 2011, at The Intersection of Data and Design, UCLA Professor Mark Hansen spoke to educators about the possibilities of these hybrids with the arts. His talk covered a number of data-driven artworks (Listening Post and, at the New York Times Building, Moveable Type) and performances (Shuffle with the Elevator Repair Service) that provided examples of what work at "the intersection" looks like. In addition to teaching at UCLA, he has started a collaboration with the Los Angeles Unified School District (LAUSD) and is currently helping to design a portion of a computer science curriculum focused on having students generate and analyze data from their daily lives using mobile technology.
Hansen began his talk by contemplating how one might define a science of data, as opposed to statistics or math or computer science per se. Many of the data lessons he talked about in the LAUSD curriculum fall into a "between" field, one that has been labeled "data science." Often, however, this term is applied in a somewhat vague way and tends to refer to the tools practitioners use rather than a set of principles. Hansen noted that there must be more to the discipline than that. After all, an astronomer is not characterized solely by her use of telescopes, no matter how important such tools are to her work.
After making a case for data science, Hansen shifted gears and presented some of the hybrid data/design works he's been part of. Listening Post, a project involving the statistical parsing of text from chat rooms into oft-repeated thematic groups, is in some ways an instrumental arrangement of what people say through the chat medium, how they say it, and how it's represented visually on hundreds of small screens and auditorily. Hansen observed that in some ways, the chat exhibited on Listening Post is a partial "response" to the "call" of current events, the news. In two subsequent projects, Hansen and his collaborator Ben Rubin created engaging displays from the text of the news. Unlike Listening Post, the "clean" nature of the news data offered new opportunities for both statistical and computational analysis that added depth to the works.
Hansen spent a fair amount of time discussing one of these news pieces, a permanent artwork in the lobby of the New York Times building. On 560 vacuum fluorescent displays similar to the displays used in Listening Post, Hansen and Rubin programmed the screens to show, in real time, a collection of seemingly unsorted current news stories (headlines, excerpts, or full-text). Some of these stories would disappear from the screens, and a pattern would be revealed: statistically clustered text about particular topics would remain. For Hansen and Rubin this piece was an opportunity to say something new about the data that are there—to surprise viewers or, just as importantly, to show them something they recognized. In his talk, Hansen likened this opportunity to the experience statisticians get by looking at aggregates: Analysis reveals previously obfuscated patterns and yields a "different encounter with data."
Hansen discussed one last art project, this one sponsored by the Cartier Foundation in Paris. The piece focused on the causes behind human migration. His exquisite visual and computational display traces the passage of money to the developing world, the movement of people to urban areas, the human impact of natural disasters and rising sea levels, and many other features of this complex subject. During his talk he shared a key insight garnered from this project: In order to build the algorithms to deal with live streams of data or with constantly updated data, one must understand the statistical properties of those data.
This is the kind of understanding that Hansen and his collaborators at the UCLA School of Education are promoting with their new curriculum for K-12 students in the LAUSD. Counteracting the classical narrative of statistics wherein data are abstracted from their collection and analysis is boiled down to a t-test (a common statistical test of significance), this new curriculum aims to combine statistical and computational thinking to help students view data through a critical lens—with a view to how it is created, represented, formatted, and understood. The curriculum asks students to take an active role in the collection of data about their surroundings through what is known as "participatory sensing," and it requires students to stop and think about every piece of technology they encounter.
Hansen's talk was part of the New York Academy of Sciences's and Math for America's series designed to help teachers integrate more data into their classrooms, expand their knowledge of careers that involve data, and inspire them to use data to reach struggling students.
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Mark Hansen, PhD
University of California, Los Angeles
e-mail | website
Mark H. Hansen is a Professor of Statistics and co-Principal Investigator of the Center for Embedded Networked Sensing at the University of California, Los Angeles. He also holds appointments in the Departments of Design | Media Art and Electrical Engineering. Hansen began his career at Bell Laboratories, and he has retained the focus on applications that he gained at Bell. Some of his current work centers on so-called "participatory sensing," projects that engage the general public in non-professional practices of data collection and analysis. His tertiary education began with a BS in Applied Mathematics and his MS in Mathematics from the University of California, Davis. From there he moved on to the Department of Statistics at University of California, Berkeley, where he received his MA in 1991 and his PhD in 1994. Since 2002, when he left Bell Labs, Hansen has held research positions at UCLA.