In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand, freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper, we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment. Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw, place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length, area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data. When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven graphics.
Our goal is to combine interactive computer systems with the perceptual and cognitive power of human observers to solve practical problems in science and engineering. We are providing visual analysis tools and methods to help scientists and researchers better process and understand large, multi-dimensional data sets in various domains such as neuroscience, genomics, systems biology, astronomy, and medicine. And we are developing data-driven approaches for the acquisition, modeling, visualization, and fabrication of complex objects.
Our group belongs to Harvard's School of Engineering and Applied Sciences and the Center for Brain Science. We are located in the Maxwell Dworkin Building (33 Oxford St.) as well as the Northwest Laboratory (52 Oxford St.) on Harvard's main campus in Cambridge, Massachusetts.