Data visualization in the first person

Abstract

This dissertation will examine what a first person viewpoint means in the context of data visualization and how it can be used for navigating and presenting large datasets. Recent years have seen rapid growth in Big Data methodologies throughout scientific research, business analytics, and online services. The datasets used in these areas are not only growing exponentially larger, but also more complex, incorporating heterogeneous data from many sources that might include digital sensors, websites, mass media, and others. The scale and complexity of these datasets pose significant challenges in the design of effective tools for navigation and analysis. This work will explore methods of representing large datasets as physical, navigable environments. Much of the related research on first person interfaces and 3D visualization has focused on producing tools for expert users and scientific analysis. Due to the complexities of navigation and perception introduced by 3D interfaces, work in this area has had mixed results. In particular, considerable efforts to develop 3D systems for more abstract data, like file systems and social networks, have had difficulty surpassing the efficiency of 2D approaches. However, 3D may offer advantages that have been less explored in this context. In particular, data visualization can be a valuable tool for disseminating scientific results, sharing insights, and explaining methodology. In these applications, clear communication of concepts and narratives are often more essential than efficient navigation. This dissertation will present novel visualization systems designed for large datasets that include audio-video recordings, social media, and others. Discussion will focus on designing visuals that use the first person perspective to give a physical and intuitive form to abstract data, to combine multiple sources of data within a shared space, to construct narratives, and to engage the viewer at a more visceral and emotional level.